var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"authorFirst\":\"1\",\"filter\":\"type:inproceedings\",\"theme\":\"dividers\",\"host\":\"bibbase.org\"},\n      data: [{\"title\":\"Push Recovery for NAO Humanoid Robot\",\"type\":\"inproceedings\",\"year\":\"2014\",\"keywords\":\"Humanoid,Taarlab\",\"pages\":\"035-040\",\"websites\":\"http://ieeexplore.ieee.org/document/6990873/\",\"month\":\"10\",\"publisher\":\"IEEE\",\"id\":\"9e013e60-438b-33fc-8d10-ba346e36ac63\",\"created\":\"2019-07-05T16:08:44.909Z\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:06.022Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2014nao\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"© 2014 IEEE. This paper presents the ankle, hip, and ankle-hip strategies in frontal plane for NAO humanoid robot. A humanoid is an unstable robotic mechanical system and the main and primary task of each humanoid is to maintain its balance. Moreover, NAO humanoid robot has 25 degrees-of-freedom with brushless DC actuators. The main challenges in the context of balancing of NAO humanoid are the high complexity of the robot and applying joint torque control approaches for NAO humanoid which is a position controlled robot. In this paper, for recovery and prediction the robot from external push, as the main contribution, the virtual leg model is proposed. The control objective is absorbing the external push and then recover the robot to its original configuration. PD controller is used to achieve the control objective. The performance and usage of these strategies and the model is validated by Webots. For each case study, an impulsive force is applied to NAO's torso with various magnitudes. The performance and validation of the proposed push recovery schemes are verified in the simulation. Moreover, the same results are obtained from the practical implementation of these strategies on NAO H25 humanoid robot.\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Masouleh, Mehdi Tale and Kalhor, Ahmad\",\"doi\":\"10.1109/ICRoM.2014.6990873\",\"booktitle\":\"2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)\",\"bibtex\":\"@inproceedings{\\n title = {Push Recovery for NAO Humanoid Robot},\\n type = {inproceedings},\\n year = {2014},\\n keywords = {Humanoid,Taarlab},\\n pages = {035-040},\\n websites = {http://ieeexplore.ieee.org/document/6990873/},\\n month = {10},\\n publisher = {IEEE},\\n id = {9e013e60-438b-33fc-8d10-ba346e36ac63},\\n created = {2019-07-05T16:08:44.909Z},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:06.022Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2014nao},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {© 2014 IEEE. This paper presents the ankle, hip, and ankle-hip strategies in frontal plane for NAO humanoid robot. A humanoid is an unstable robotic mechanical system and the main and primary task of each humanoid is to maintain its balance. Moreover, NAO humanoid robot has 25 degrees-of-freedom with brushless DC actuators. The main challenges in the context of balancing of NAO humanoid are the high complexity of the robot and applying joint torque control approaches for NAO humanoid which is a position controlled robot. In this paper, for recovery and prediction the robot from external push, as the main contribution, the virtual leg model is proposed. The control objective is absorbing the external push and then recover the robot to its original configuration. PD controller is used to achieve the control objective. The performance and usage of these strategies and the model is validated by Webots. For each case study, an impulsive force is applied to NAO's torso with various magnitudes. The performance and validation of the proposed push recovery schemes are verified in the simulation. Moreover, the same results are obtained from the practical implementation of these strategies on NAO H25 humanoid robot.},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Masouleh, Mehdi Tale and Kalhor, Ahmad},\\n doi = {10.1109/ICRoM.2014.6990873},\\n booktitle = {2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Masouleh,  M., T.\",\"Kalhor,  A.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/450ad7fd-5e4b-dddd-1ce2-fa6eeb8fbc5e/ghassemi2014nao.pdf.pdf\",\"Website\":\"http://ieeexplore.ieee.org/document/6990873/\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-masouleh-kalhor-pushrecoveryfornaohumanoidrobot-2014\",\"role\":\"author\",\"keyword\":[\"Humanoid\",\"Taarlab\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Dynamic Balance of a NAO H25 Humanoid Robot based on Model Predictive Control\",\"type\":\"inproceedings\",\"year\":\"2017\",\"keywords\":\"Humanoid,Taarlab\",\"pages\":\"0156-0164\",\"websites\":\"http://ieeexplore.ieee.org/document/8324964/\",\"month\":\"12\",\"publisher\":\"IEEE\",\"id\":\"a57467a7-b0dd-3852-a38a-a5b504630b62\",\"created\":\"2019-07-05T16:08:44.947Z\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:05.700Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"mousavi2017dynamic\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"© 2017 IEEE. This paper proposes a model predictive based approach for dynamic balance of a NAO H25 Humanoid robot. Due to the inherent mechanical properties of a humanoid robot, it has a huge potential to lose its stability during its movements. Using an integrated dynamic model leads to control three potential situations a humanoid robot may encounter, namely under-actuation, over-actuation, and fully-actuation. In this paper, by resorting to the so-called Zero-Momentum Point (ZMP) criterion for robots stability, an investigation is carried out on the stability equations and movement patterns in their relevant state space regarding to choosing ZMP as a sustainability criterion, to the end of achieving an appropriate path for robot's center of mass and two legs. In order to apply the concept of the Model-Predictive Control (MPC) based walking pattern, the ZMP state space equations are distributed along the horizon length, besides defining a specific cost function, which covers control input and ZMP path. Some linear constraints are applied to ZMP implementation and robot steps, which leads to optimize the cost function. This procedure is followed by evaluating how the weight of different parts of the cost function influences its performance. Implementation and simulation in MATLAB, Python, and ADAMS estimates and verifies the MPC performance.\",\"bibtype\":\"inproceedings\",\"author\":\"Mousavi, Fatemeh Sadat and Ghassemi, Payam and Kalhor, Ahmad and Masouleh, Mehdi Tale\",\"doi\":\"10.1109/KBEI.2017.8324964\",\"booktitle\":\"2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)\",\"bibtex\":\"@inproceedings{\\n title = {Dynamic Balance of a NAO H25 Humanoid Robot based on Model Predictive Control},\\n type = {inproceedings},\\n year = {2017},\\n keywords = {Humanoid,Taarlab},\\n pages = {0156-0164},\\n websites = {http://ieeexplore.ieee.org/document/8324964/},\\n month = {12},\\n publisher = {IEEE},\\n id = {a57467a7-b0dd-3852-a38a-a5b504630b62},\\n created = {2019-07-05T16:08:44.947Z},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.700Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {mousavi2017dynamic},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {© 2017 IEEE. This paper proposes a model predictive based approach for dynamic balance of a NAO H25 Humanoid robot. Due to the inherent mechanical properties of a humanoid robot, it has a huge potential to lose its stability during its movements. Using an integrated dynamic model leads to control three potential situations a humanoid robot may encounter, namely under-actuation, over-actuation, and fully-actuation. In this paper, by resorting to the so-called Zero-Momentum Point (ZMP) criterion for robots stability, an investigation is carried out on the stability equations and movement patterns in their relevant state space regarding to choosing ZMP as a sustainability criterion, to the end of achieving an appropriate path for robot's center of mass and two legs. In order to apply the concept of the Model-Predictive Control (MPC) based walking pattern, the ZMP state space equations are distributed along the horizon length, besides defining a specific cost function, which covers control input and ZMP path. Some linear constraints are applied to ZMP implementation and robot steps, which leads to optimize the cost function. This procedure is followed by evaluating how the weight of different parts of the cost function influences its performance. Implementation and simulation in MATLAB, Python, and ADAMS estimates and verifies the MPC performance.},\\n bibtype = {inproceedings},\\n author = {Mousavi, Fatemeh Sadat and Ghassemi, Payam and Kalhor, Ahmad and Masouleh, Mehdi Tale},\\n doi = {10.1109/KBEI.2017.8324964},\\n booktitle = {2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)}\\n}\",\"author_short\":[\"Mousavi,  F., S.\",\"Ghassemi,  P.\",\"Kalhor,  A.\",\"Masouleh,  M., T.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/3847f735-33f6-aae9-7471-b6c9b1afbeb6/mousavi2017dynamic.pdf.pdf\",\"Website\":\"http://ieeexplore.ieee.org/document/8324964/\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"mousavi-ghassemi-kalhor-masouleh-dynamicbalanceofanaoh25humanoidrobotbasedonmodelpredictivecontrol-2017\",\"role\":\"author\",\"keyword\":[\"Humanoid\",\"Taarlab\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings\",\"type\":\"inproceedings\",\"year\":\"2017\",\"pages\":\"V02BT03A026\",\"websites\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375\",\"month\":\"8\",\"publisher\":\"American Society of Mechanical Engineers\",\"day\":\"6\",\"city\":\"Cleveland, Ohio, USA\",\"id\":\"b74d514d-3038-3197-aa98-1cad32734741\",\"created\":\"2019-07-08T20:51:37.509Z\",\"accessed\":\"2019-07-08\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:06.003Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2017optimal\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Zhu, Kaige and Chowdhury, Souma\",\"doi\":\"10.1115/DETC2017-68350\",\"booktitle\":\"Volume 2B: 43rd Design Automation Conference\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings},\\n type = {inproceedings},\\n year = {2017},\\n pages = {V02BT03A026},\\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375},\\n month = {8},\\n publisher = {American Society of Mechanical Engineers},\\n day = {6},\\n city = {Cleveland, Ohio, USA},\\n id = {b74d514d-3038-3197-aa98-1cad32734741},\\n created = {2019-07-08T20:51:37.509Z},\\n accessed = {2019-07-08},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:06.003Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2017optimal},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Zhu, Kaige and Chowdhury, Souma},\\n doi = {10.1115/DETC2017-68350},\\n booktitle = {Volume 2B: 43rd Design Automation Conference},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Zhu,  K.\",\"Chowdhury,  S.\"],\"urls\":{\"Website\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering\",\"type\":\"inproceedings\",\"year\":\"2018\",\"pages\":\"V02AT03A014\",\"websites\":\"http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2018-86161,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51753/Quebec City, Quebec, Canada/273573\",\"month\":\"8\",\"publisher\":\"American Society of Mechanical Engineers\",\"day\":\"26\",\"id\":\"af5d7d17-8c27-3faf-93cf-b8653370cf4e\",\"created\":\"2019-07-08T20:53:03.602Z\",\"accessed\":\"2019-07-08\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-25T07:49:59.568Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2018decmata\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Chowdhury, Souma\",\"doi\":\"10.1115/DETC2018-86161\",\"booktitle\":\"Volume 2A: 44th Design Automation Conference\",\"keywords\":\"ADAMS,MRTA,Swarm\",\"bibtex\":\"@inproceedings{\\n title = {Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering},\\n type = {inproceedings},\\n year = {2018},\\n pages = {V02AT03A014},\\n websites = {http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2018-86161,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51753/Quebec City, Quebec, Canada/273573},\\n month = {8},\\n publisher = {American Society of Mechanical Engineers},\\n day = {26},\\n id = {af5d7d17-8c27-3faf-93cf-b8653370cf4e},\\n created = {2019-07-08T20:53:03.602Z},\\n accessed = {2019-07-08},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-25T07:49:59.568Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2018decmata},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Chowdhury, Souma},\\n doi = {10.1115/DETC2018-86161},\\n booktitle = {Volume 2A: 44th Design Automation Conference},\\n keywords = {ADAMS,MRTA,Swarm}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Chowdhury,  S.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/071f92f5-3416-22d1-5a17-c80d63381b66/full_text.pdf.pdf\",\"Website\":\"http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2018-86161,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51753/Quebec City, Quebec, Canada/273573\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-chowdhury-decentralizedtaskallocationinmultirobotsystemsviabipartitegraphmatchingaugmentedwithfuzzyclustering-2018\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"MRTA\",\"Swarm\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":4,\"html\":\"\"},{\"title\":\"CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency\",\"type\":\"inproceedings\",\"year\":\"2018\",\"websites\":\"https://arc.aiaa.org/doi/10.2514/6.2018-3107\",\"month\":\"6\",\"publisher\":\"American Institute of Aeronautics and Astronautics\",\"day\":\"25\",\"city\":\"Atlanta, GA, USA\",\"id\":\"cc163f9a-922a-36bd-b7d4-712619fd852f\",\"created\":\"2019-07-08T20:54:02.941Z\",\"accessed\":\"2019-07-08\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:05.767Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"lulekar2018riblet\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.\",\"bibtype\":\"inproceedings\",\"author\":\"Lulekar, Sumeet and Ghassemi, Payam and Chowdhury, Souma\",\"doi\":\"10.2514/6.2018-3107\",\"booktitle\":\"2018 Multidisciplinary Analysis and Optimization Conference\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency},\\n type = {inproceedings},\\n year = {2018},\\n websites = {https://arc.aiaa.org/doi/10.2514/6.2018-3107},\\n month = {6},\\n publisher = {American Institute of Aeronautics and Astronautics},\\n day = {25},\\n city = {Atlanta, GA, USA},\\n id = {cc163f9a-922a-36bd-b7d4-712619fd852f},\\n created = {2019-07-08T20:54:02.941Z},\\n accessed = {2019-07-08},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.767Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {lulekar2018riblet},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.},\\n bibtype = {inproceedings},\\n author = {Lulekar, Sumeet and Ghassemi, Payam and Chowdhury, Souma},\\n doi = {10.2514/6.2018-3107},\\n booktitle = {2018 Multidisciplinary Analysis and Optimization Conference},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Lulekar,  S.\",\"Ghassemi,  P.\",\"Chowdhury,  S.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf\",\"Website\":\"https://arc.aiaa.org/doi/10.2514/6.2018-3107\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search\",\"type\":\"inproceedings\",\"year\":\"2019\",\"pages\":\"V001T02A058\",\"websites\":\"https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658\",\"month\":\"8\",\"publisher\":\"American Society of Mechanical Engineers\",\"day\":\"18\",\"city\":\"Anaheim, CA, USA\",\"id\":\"595b1ccf-7204-3901-9fbc-ac4ca113a8d9\",\"created\":\"2019-07-08T20:56:16.682Z\",\"accessed\":\"2019-07-08\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-25T07:49:59.592Z\",\"read\":\"true\",\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2019bswarm-detc\",\"patent_owner\":\"Ghassemi, Payam\",\"notes\":\"Identified as one of the strongest CIE 2019 papers; invited to submit to Journal of Computing and Information Science in Engineering.\",\"private_publication\":false,\"abstract\":\"Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Chowdhury, Souma\",\"doi\":\"10.1115/DETC2019-97887\",\"booktitle\":\"Volume 1: 39th Computers and Information in Engineering Conference\",\"keywords\":\"ADAMS,BSwarm,Swarm\",\"bibtex\":\"@inproceedings{\\n title = {Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search},\\n type = {inproceedings},\\n year = {2019},\\n pages = {V001T02A058},\\n websites = {https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658},\\n month = {8},\\n publisher = {American Society of Mechanical Engineers},\\n day = {18},\\n city = {Anaheim, CA, USA},\\n id = {595b1ccf-7204-3901-9fbc-ac4ca113a8d9},\\n created = {2019-07-08T20:56:16.682Z},\\n accessed = {2019-07-08},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-25T07:49:59.592Z},\\n read = {true},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2019bswarm-detc},\\n patent_owner = {Ghassemi, Payam},\\n notes = {Identified as one of the strongest CIE 2019 papers; invited to submit to Journal of Computing and Information Science in Engineering.},\\n private_publication = {false},\\n abstract = {Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Chowdhury, Souma},\\n doi = {10.1115/DETC2019-97887},\\n booktitle = {Volume 1: 39th Computers and Information in Engineering Conference},\\n keywords = {ADAMS,BSwarm,Swarm}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Chowdhury,  S.\"],\"urls\":{\"Website\":\"https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"BSwarm\",\"Swarm\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring\",\"type\":\"inproceedings\",\"year\":\"2019\",\"websites\":\"https://arc.aiaa.org/doi/10.2514/6.2019-2983\",\"month\":\"6\",\"publisher\":\"American Institute of Aeronautics and Astronautics\",\"day\":\"17\",\"city\":\"Dallas, TX, USA\",\"id\":\"f8e1829b-a456-33db-98eb-e0b2402024c0\",\"created\":\"2019-07-08T20:56:27.362Z\",\"accessed\":\"2019-07-08\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:06.192Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2019amrpbs\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to \\\"when\\\" to sample and \\\"how many\\\" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards \\\"where\\\" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (\\\"q-EI\\\"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Lulekar, Sumeet S. and Chowdhury, Souma\",\"doi\":\"10.2514/6.2019-2983\",\"booktitle\":\"AIAA Aviation 2019 Forum\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring},\\n type = {inproceedings},\\n year = {2019},\\n websites = {https://arc.aiaa.org/doi/10.2514/6.2019-2983},\\n month = {6},\\n publisher = {American Institute of Aeronautics and Astronautics},\\n day = {17},\\n city = {Dallas, TX, USA},\\n id = {f8e1829b-a456-33db-98eb-e0b2402024c0},\\n created = {2019-07-08T20:56:27.362Z},\\n accessed = {2019-07-08},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:06.192Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2019amrpbs},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to \\\"when\\\" to sample and \\\"how many\\\" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards \\\"where\\\" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (\\\"q-EI\\\"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Lulekar, Sumeet S. and Chowdhury, Souma},\\n doi = {10.2514/6.2019-2983},\\n booktitle = {AIAA Aviation 2019 Forum},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Lulekar,  S., S.\",\"Chowdhury,  S.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf\",\"Website\":\"https://arc.aiaa.org/doi/10.2514/6.2019-2983\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Push Recovery Methods Based on Admittance Control Strategies for a NAO-H25 Humanoid\",\"type\":\"inproceedings\",\"year\":\"2018\",\"keywords\":\"Humanoid,Taarlab\",\"pages\":\"451-457\",\"websites\":\"https://ieeexplore.ieee.org/document/8657613/\",\"month\":\"10\",\"publisher\":\"IEEE\",\"id\":\"758b43e7-26ae-3469-bc4c-a07e538d916f\",\"created\":\"2019-07-08T20:56:34.647Z\",\"accessed\":\"2019-07-08\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:05.528Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"mousavi2018push\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Mousavi, Fatemeh Sadat and Tale Masouleh, Mehdi and Kalhor, Ahmad and Ghassemi, Payam\",\"doi\":\"10.1109/ICRoM.2018.8657613\",\"booktitle\":\"2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM)\",\"bibtex\":\"@inproceedings{\\n title = {Push Recovery Methods Based on Admittance Control Strategies for a NAO-H25 Humanoid},\\n type = {inproceedings},\\n year = {2018},\\n keywords = {Humanoid,Taarlab},\\n pages = {451-457},\\n websites = {https://ieeexplore.ieee.org/document/8657613/},\\n month = {10},\\n publisher = {IEEE},\\n id = {758b43e7-26ae-3469-bc4c-a07e538d916f},\\n created = {2019-07-08T20:56:34.647Z},\\n accessed = {2019-07-08},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.528Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {mousavi2018push},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Mousavi, Fatemeh Sadat and Tale Masouleh, Mehdi and Kalhor, Ahmad and Ghassemi, Payam},\\n doi = {10.1109/ICRoM.2018.8657613},\\n booktitle = {2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM)}\\n}\",\"author_short\":[\"Mousavi,  F., S.\",\"Tale Masouleh,  M.\",\"Kalhor,  A.\",\"Ghassemi,  P.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/337a7d85-a784-0601-1451-8df174492312/mousavi2018push.pdf.pdf\",\"Website\":\"https://ieeexplore.ieee.org/document/8657613/\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"mousavi-talemasouleh-kalhor-ghassemi-pushrecoverymethodsbasedonadmittancecontrolstrategiesforanaoh25humanoid-2018\",\"role\":\"author\",\"keyword\":[\"Humanoid\",\"Taarlab\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Optimal Metamodeling to Interpret Activity-Based Health Sensor Data\",\"type\":\"inproceedings\",\"year\":\"2017\",\"websites\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086\",\"month\":\"8\",\"publisher\":\"American Society of Mechanical Engineers\",\"day\":\"6\",\"city\":\"Cleveland, Ohio, USA\",\"id\":\"dcb348b4-c676-3a9f-bd62-e9cbe8eed25d\",\"created\":\"2019-07-08T21:19:22.379Z\",\"accessed\":\"2019-07-08\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:05.932Z\",\"read\":\"true\",\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2017health\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"© 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of < 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Mehmani, Ali, Chowdhury, Souma\",\"doi\":\"10.1115/DETC2017-68385\",\"booktitle\":\"Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {Optimal Metamodeling to Interpret Activity-Based Health Sensor Data},\\n type = {inproceedings},\\n year = {2017},\\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086},\\n month = {8},\\n publisher = {American Society of Mechanical Engineers},\\n day = {6},\\n city = {Cleveland, Ohio, USA},\\n id = {dcb348b4-c676-3a9f-bd62-e9cbe8eed25d},\\n created = {2019-07-08T21:19:22.379Z},\\n accessed = {2019-07-08},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.932Z},\\n read = {true},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2017health},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {© 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of < 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Mehmani, Ali, Chowdhury, Souma},\\n doi = {10.1115/DETC2017-68385},\\n booktitle = {Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Mehmani, Ali, Chowdhury,  S.\"],\"urls\":{\"Website\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search\",\"type\":\"inproceedings\",\"year\":\"2019\",\"keywords\":\"ADAMS,BSwarm,Conference,Swarm\",\"pages\":\"188-194\",\"websites\":\"http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/\",\"month\":\"8\",\"publisher\":\"Institute of Electrical and Electronics Engineers. (acceptance: 33%)\",\"day\":\"22\",\"city\":\"New Brunswick, NJ\",\"id\":\"4d126477-38d4-3fc0-8451-18bd5bbcf53e\",\"created\":\"2019-07-11T05:04:57.734Z\",\"accessed\":\"2019-07-11\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-25T07:49:59.807Z\",\"read\":\"true\",\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2019bswarm-mrs\",\"patent_owner\":\"Ghassemi, Payam\",\"notes\":\"(acceptance: 33%)\",\"private_publication\":false,\"abstract\":\"Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots' planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and Chowdhury, Souma\",\"doi\":\"10.1109/MRS.2019.8901084\",\"booktitle\":\"International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019\",\"bibtex\":\"@inproceedings{\\n title = {Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search},\\n type = {inproceedings},\\n year = {2019},\\n keywords = {ADAMS,BSwarm,Conference,Swarm},\\n pages = {188-194},\\n websites = {http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/},\\n month = {8},\\n publisher = {Institute of Electrical and Electronics Engineers. (acceptance: 33%)},\\n day = {22},\\n city = {New Brunswick, NJ},\\n id = {4d126477-38d4-3fc0-8451-18bd5bbcf53e},\\n created = {2019-07-11T05:04:57.734Z},\\n accessed = {2019-07-11},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-25T07:49:59.807Z},\\n read = {true},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2019bswarm-mrs},\\n patent_owner = {Ghassemi, Payam},\\n notes = {(acceptance: 33%)},\\n private_publication = {false},\\n abstract = {Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots' planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Chowdhury, Souma},\\n doi = {10.1109/MRS.2019.8901084},\\n booktitle = {International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Chowdhury,  S.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf\",\"Website\":\"http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"BSwarm\",\"Conference\",\"Swarm\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response: Extended Abstract\",\"type\":\"inproceedings\",\"year\":\"2019\",\"pages\":\"83-85\",\"websites\":\"http://arxiv.org/abs/1907.04394,https://ieeexplore.ieee.org/document/8901062/\",\"month\":\"8\",\"publisher\":\"IEEE\",\"day\":\"22\",\"city\":\"New Brunswick, NJ\",\"id\":\"3c46070e-f917-332e-adf0-6246955d7f09\",\"created\":\"2019-07-11T05:04:57.748Z\",\"accessed\":\"2019-07-11\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-25T07:49:59.774Z\",\"read\":\"true\",\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2019decmrta\",\"patent_owner\":\"Ghassemi, Payam\",\"private_publication\":false,\"abstract\":\"Multiple robotic systems, working together, can provide important solutions to different real-world applications (e.g., disaster response), among which task allocation problems feature prominently. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involves novel construction and matching of bipartite graphs. Its performance is tested on a multi-UAV flood response application.\",\"bibtype\":\"inproceedings\",\"author\":\"Ghassemi, Payam and DePauw, David and Chowdhury, Souma\",\"doi\":\"10.1109/MRS.2019.8901062\",\"booktitle\":\"2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)\",\"keywords\":\"ADAMS,Conference,MRTA,Swarm\",\"bibtex\":\"@inproceedings{\\n title = {Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response: Extended Abstract},\\n type = {inproceedings},\\n year = {2019},\\n pages = {83-85},\\n websites = {http://arxiv.org/abs/1907.04394,https://ieeexplore.ieee.org/document/8901062/},\\n month = {8},\\n publisher = {IEEE},\\n day = {22},\\n city = {New Brunswick, NJ},\\n id = {3c46070e-f917-332e-adf0-6246955d7f09},\\n created = {2019-07-11T05:04:57.748Z},\\n accessed = {2019-07-11},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-25T07:49:59.774Z},\\n read = {true},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2019decmrta},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n abstract = {Multiple robotic systems, working together, can provide important solutions to different real-world applications (e.g., disaster response), among which task allocation problems feature prominently. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involves novel construction and matching of bipartite graphs. Its performance is tested on a multi-UAV flood response application.},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and DePauw, David and Chowdhury, Souma},\\n doi = {10.1109/MRS.2019.8901062},\\n booktitle = {2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)},\\n keywords = {ADAMS,Conference,MRTA,Swarm}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"DePauw,  D.\",\"Chowdhury,  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last_modified = {2020-06-15T14:37:13.985Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2014dynamic},\\n patent_owner = {Ghassemi, Payam},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Masouleh, M.T.},\\n booktitle = {ISME 2014 22nd Annual International Conference}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Masouleh,  M.\"],\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-masouleh-dynamicequationofconstrainedmotionofhumanoidrobotsusingtheanalyticalmethod-2014\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System\",\"type\":\"inproceedings\",\"year\":\"2018\",\"websites\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832\",\"month\":\"8\",\"publisher\":\"American Society of Mechanical Engineers\",\"day\":\"26\",\"id\":\"de1afd2c-7502-3e5f-afe0-2feeeb76b88b\",\"created\":\"2020-06-14T21:18:56.238Z\",\"accessed\":\"2020-06-14\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-10T06:28:05.724Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"liu2018battery\",\"private_publication\":false,\"abstract\":\"This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.\",\"bibtype\":\"inproceedings\",\"author\":\"Liu, Yuanzhi and Ghassemi, Payam and Chowdhury, Souma and Zhang, Jie\",\"doi\":\"10.1115/DETC2018-85620\",\"booktitle\":\"Volume 2B: 44th Design Automation Conference\",\"keywords\":\"ADAMS,SBO\",\"bibtex\":\"@inproceedings{\\n title = {Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System},\\n type = {inproceedings},\\n year = {2018},\\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832},\\n month = {8},\\n publisher = {American Society of Mechanical Engineers},\\n day = {26},\\n id = {de1afd2c-7502-3e5f-afe0-2feeeb76b88b},\\n created = {2020-06-14T21:18:56.238Z},\\n accessed = {2020-06-14},\\n file_attached = {false},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:05.724Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {liu2018battery},\\n private_publication = {false},\\n abstract = {This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.},\\n bibtype = {inproceedings},\\n author = {Liu, Yuanzhi and Ghassemi, Payam and Chowdhury, Souma and Zhang, Jie},\\n doi = {10.1115/DETC2018-85620},\\n booktitle = {Volume 2B: 44th Design Automation Conference},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Liu,  Y.\",\"Ghassemi,  P.\",\"Chowdhury,  S.\",\"Zhang,  J.\"],\"urls\":{\"Website\":\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring\",\"type\":\"inproceedings\",\"year\":\"2020\",\"websites\":\"https://arc.aiaa.org/doi/10.2514/6.2020-3183\",\"month\":\"6\",\"publisher\":\"American Institute of Aeronautics and Astronautics\",\"day\":\"15\",\"city\":\"Virtual 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Aeronautics and Astronautics},\\n day = {15},\\n city = {Virtual Event},\\n id = {c0028f93-0234-3d27-9b51-13ad2dc29da7},\\n created = {2020-06-15T01:47:41.205Z},\\n accessed = {2020-06-14},\\n file_attached = {true},\\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\\n last_modified = {2020-07-10T06:28:06.000Z},\\n read = {false},\\n starred = {false},\\n authored = {true},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ghassemi2020physbo},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Ghassemi, Payam and Behjat, Amir and Zeng, Chen and Lulekar, Sumeet S. and Rai, Rahul and Chowdhury, Souma},\\n doi = {10.2514/6.2020-3183},\\n booktitle = {AIAA AVIATION 2020 FORUM},\\n keywords = {ADAMS,SBO}\\n}\",\"author_short\":[\"Ghassemi,  P.\",\"Behjat,  A.\",\"Zeng,  C.\",\"Lulekar,  S., S.\",\"Rai,  R.\",\"Chowdhury,  S.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf\",\"Website\":\"https://arc.aiaa.org/doi/10.2514/6.2020-3183\"},\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020\",\"role\":\"author\",\"keyword\":[\"ADAMS\",\"SBO\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Using Physiological Measurements to Predict the Tactical Decisions in Human Swarm Teams\",\"type\":\"inproceedings\",\"year\":\"2020\",\"month\":\"10\",\"publisher\":\"IEEE\",\"city\":\"Toronto, Canada\",\"id\":\"b32e2cff-bd6a-3705-8410-b2d49385bebb\",\"created\":\"2020-08-21T09:23:24.515Z\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-08-21T09:25:47.567Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"hemanth2020smc\",\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Manjunatha, Hemanth and Distefano, Joseph P. and Ghassemi, Payam and Chowdhury, Souma and Dantu, Kathik and Doermann, David and Esfahani, Ehsan\",\"booktitle\":\"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)\",\"keywords\":\"ADAMS,HumanSwarm,Swarm\",\"bibtex\":\"@inproceedings{\\n title = {Using Physiological Measurements to Predict the Tactical Decisions in Human Swarm Teams},\\n type = {inproceedings},\\n year = {2020},\\n month = {10},\\n publisher = {IEEE},\\n city = {Toronto, Canada},\\n id = 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E.\"],\"biburl\":\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"bibbaseid\":\"manjunatha-distefano-ghassemi-chowdhury-dantu-doermann-esfahani-usingphysiologicalmeasurementstopredictthetacticaldecisionsinhumanswarmteams-2020\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"ADAMS\",\"HumanSwarm\",\"Swarm\"],\"metadata\":{\"authorlinks\":{\"ghassemi,  p\":\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas\",\"type\":\"inproceedings\",\"year\":\"2021\",\"publisher\":\"IEEE (Accepted)\",\"city\":\"Xi’an, China\",\"id\":\"0bedc84d-3690-3ba6-bc3f-fb3dd9a1faae\",\"created\":\"2020-11-14T06:47:09.948Z\",\"file_attached\":false,\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2021-02-28T19:55:54.281Z\",\"read\":false,\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Collins, Leighton and Ghassemi, Payam and Chowdhury, Souma and Dantu, Karthik and Esfahani, Ehsan and Doermann, David\",\"booktitle\":\"IEEE International Conference on Robotics and Automation (ICRA)\",\"bibtex\":\"@inproceedings{\\n title = {Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas},\\n type = {inproceedings},\\n year = {2021},\\n publisher = {IEEE (Accepted)},\\n city = {Xi’an, China},\\n id = {0bedc84d-3690-3ba6-bc3f-fb3dd9a1faae},\\n created = {2020-11-14T06:47:09.948Z},\\n file_attached = {false},\\n profile_id = 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href=\"data:text/bibtex;base64,@inproceedings{
 title = {Push Recovery for NAO Humanoid Robot},
 type = {inproceedings},
 year = {2014},
 keywords = {Humanoid,Taarlab},
 pages = {035-040},
 websites = {http://ieeexplore.ieee.org/document/6990873/},
 month = {10},
 publisher = {IEEE},
 id = {9e013e60-438b-33fc-8d10-ba346e36ac63},
 created = {2019-07-05T16:08:44.909Z},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:06.022Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2014nao},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {© 2014 IEEE. This paper presents the ankle, hip, and ankle-hip strategies in frontal plane for NAO humanoid robot. A humanoid is an unstable robotic mechanical system and the main and primary task of each humanoid is to maintain its balance. Moreover, NAO humanoid robot has 25 degrees-of-freedom with brushless DC actuators. The main challenges in the context of balancing of NAO humanoid are the high complexity of the robot and applying joint torque control approaches for NAO humanoid which is a position controlled robot. In this paper, for recovery and prediction the robot from external push, as the main contribution, the virtual leg model is proposed. The control objective is absorbing the external push and then recover the robot to its original configuration. PD controller is used to achieve the control objective. The performance and usage of these strategies and the model is validated by Webots. For each case study, an impulsive force is applied to NAO's torso with various magnitudes. The performance and validation of the proposed push recovery schemes are verified in the simulation. Moreover, the same results are obtained from the practical implementation of these strategies on NAO H25 humanoid robot.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Masouleh, Mehdi Tale and Kalhor, Ahmad},
 doi = {10.1109/ICRoM.2014.6990873},
 booktitle = {2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)}
}

@inproceedings{
 title = {Dynamic Balance of a NAO H25 Humanoid Robot based on Model Predictive Control},
 type = {inproceedings},
 year = {2017},
 keywords = {Humanoid,Taarlab},
 pages = {0156-0164},
 websites = {http://ieeexplore.ieee.org/document/8324964/},
 month = {12},
 publisher = {IEEE},
 id = {a57467a7-b0dd-3852-a38a-a5b504630b62},
 created = {2019-07-05T16:08:44.947Z},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.700Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {mousavi2017dynamic},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {© 2017 IEEE. This paper proposes a model predictive based approach for dynamic balance of a NAO H25 Humanoid robot. Due to the inherent mechanical properties of a humanoid robot, it has a huge potential to lose its stability during its movements. Using an integrated dynamic model leads to control three potential situations a humanoid robot may encounter, namely under-actuation, over-actuation, and fully-actuation. In this paper, by resorting to the so-called Zero-Momentum Point (ZMP) criterion for robots stability, an investigation is carried out on the stability equations and movement patterns in their relevant state space regarding to choosing ZMP as a sustainability criterion, to the end of achieving an appropriate path for robot's center of mass and two legs. In order to apply the concept of the Model-Predictive Control (MPC) based walking pattern, the ZMP state space equations are distributed along the horizon length, besides defining a specific cost function, which covers control input and ZMP path. Some linear constraints are applied to ZMP implementation and robot steps, which leads to optimize the cost function. This procedure is followed by evaluating how the weight of different parts of the cost function influences its performance. Implementation and simulation in MATLAB, Python, and ADAMS estimates and verifies the MPC performance.},
 bibtype = {inproceedings},
 author = {Mousavi, Fatemeh Sadat and Ghassemi, Payam and Kalhor, Ahmad and Masouleh, Mehdi Tale},
 doi = {10.1109/KBEI.2017.8324964},
 booktitle = {2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)}
}

@inproceedings{
 title = {Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings},
 type = {inproceedings},
 year = {2017},
 pages = {V02BT03A026},
 websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
 day = {6},
 city = {Cleveland, Ohio, USA},
 id = {b74d514d-3038-3197-aa98-1cad32734741},
 created = {2019-07-08T20:51:37.509Z},
 accessed = {2019-07-08},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:06.003Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2017optimal},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Zhu, Kaige and Chowdhury, Souma},
 doi = {10.1115/DETC2017-68350},
 booktitle = {Volume 2B: 43rd Design Automation Conference},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering},
 type = {inproceedings},
 year = {2018},
 pages = {V02AT03A014},
 websites = {http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2018-86161,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51753/Quebec City, Quebec, Canada/273573},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
 day = {26},
 id = {af5d7d17-8c27-3faf-93cf-b8653370cf4e},
 created = {2019-07-08T20:53:03.602Z},
 accessed = {2019-07-08},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-25T07:49:59.568Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2018decmata},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Chowdhury, Souma},
 doi = {10.1115/DETC2018-86161},
 booktitle = {Volume 2A: 44th Design Automation Conference},
 keywords = {ADAMS,MRTA,Swarm}
}

@inproceedings{
 title = {CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency},
 type = {inproceedings},
 year = {2018},
 websites = {https://arc.aiaa.org/doi/10.2514/6.2018-3107},
 month = {6},
 publisher = {American Institute of Aeronautics and Astronautics},
 day = {25},
 city = {Atlanta, GA, USA},
 id = {cc163f9a-922a-36bd-b7d4-712619fd852f},
 created = {2019-07-08T20:54:02.941Z},
 accessed = {2019-07-08},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.767Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {lulekar2018riblet},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.},
 bibtype = {inproceedings},
 author = {Lulekar, Sumeet and Ghassemi, Payam and Chowdhury, Souma},
 doi = {10.2514/6.2018-3107},
 booktitle = {2018 Multidisciplinary Analysis and Optimization Conference},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search},
 type = {inproceedings},
 year = {2019},
 pages = {V001T02A058},
 websites = {https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
 day = {18},
 city = {Anaheim, CA, USA},
 id = {595b1ccf-7204-3901-9fbc-ac4ca113a8d9},
 created = {2019-07-08T20:56:16.682Z},
 accessed = {2019-07-08},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-25T07:49:59.592Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2019bswarm-detc},
 patent_owner = {Ghassemi, Payam},
 notes = {Identified as one of the strongest CIE 2019 papers; invited to submit to Journal of Computing and Information Science in Engineering.},
 private_publication = {false},
 abstract = {Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Chowdhury, Souma},
 doi = {10.1115/DETC2019-97887},
 booktitle = {Volume 1: 39th Computers and Information in Engineering Conference},
 keywords = {ADAMS,BSwarm,Swarm}
}

@inproceedings{
 title = {Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring},
 type = {inproceedings},
 year = {2019},
 websites = {https://arc.aiaa.org/doi/10.2514/6.2019-2983},
 month = {6},
 publisher = {American Institute of Aeronautics and Astronautics},
 day = {17},
 city = {Dallas, TX, USA},
 id = {f8e1829b-a456-33db-98eb-e0b2402024c0},
 created = {2019-07-08T20:56:27.362Z},
 accessed = {2019-07-08},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:06.192Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2019amrpbs},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to "when" to sample and "how many" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards "where" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria ("q-EI"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Lulekar, Sumeet S. and Chowdhury, Souma},
 doi = {10.2514/6.2019-2983},
 booktitle = {AIAA Aviation 2019 Forum},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Push Recovery Methods Based on Admittance Control Strategies for a NAO-H25 Humanoid},
 type = {inproceedings},
 year = {2018},
 keywords = {Humanoid,Taarlab},
 pages = {451-457},
 websites = {https://ieeexplore.ieee.org/document/8657613/},
 month = {10},
 publisher = {IEEE},
 id = {758b43e7-26ae-3469-bc4c-a07e538d916f},
 created = {2019-07-08T20:56:34.647Z},
 accessed = {2019-07-08},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.528Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {mousavi2018push},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Mousavi, Fatemeh Sadat and Tale Masouleh, Mehdi and Kalhor, Ahmad and Ghassemi, Payam},
 doi = {10.1109/ICRoM.2018.8657613},
 booktitle = {2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM)}
}

@inproceedings{
 title = {Optimal Metamodeling to Interpret Activity-Based Health Sensor Data},
 type = {inproceedings},
 year = {2017},
 websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
 day = {6},
 city = {Cleveland, Ohio, USA},
 id = {dcb348b4-c676-3a9f-bd62-e9cbe8eed25d},
 created = {2019-07-08T21:19:22.379Z},
 accessed = {2019-07-08},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.932Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2017health},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {© 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of < 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Mehmani, Ali, Chowdhury, Souma},
 doi = {10.1115/DETC2017-68385},
 booktitle = {Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search},
 type = {inproceedings},
 year = {2019},
 keywords = {ADAMS,BSwarm,Conference,Swarm},
 pages = {188-194},
 websites = {http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/},
 month = {8},
 publisher = {Institute of Electrical and Electronics Engineers. (acceptance: 33%)},
 day = {22},
 city = {New Brunswick, NJ},
 id = {4d126477-38d4-3fc0-8451-18bd5bbcf53e},
 created = {2019-07-11T05:04:57.734Z},
 accessed = {2019-07-11},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-25T07:49:59.807Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2019bswarm-mrs},
 patent_owner = {Ghassemi, Payam},
 notes = {(acceptance: 33%)},
 private_publication = {false},
 abstract = {Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots' planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Chowdhury, Souma},
 doi = {10.1109/MRS.2019.8901084},
 booktitle = {International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019}
}

@inproceedings{
 title = {Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response: Extended Abstract},
 type = {inproceedings},
 year = {2019},
 pages = {83-85},
 websites = {http://arxiv.org/abs/1907.04394,https://ieeexplore.ieee.org/document/8901062/},
 month = {8},
 publisher = {IEEE},
 day = {22},
 city = {New Brunswick, NJ},
 id = {3c46070e-f917-332e-adf0-6246955d7f09},
 created = {2019-07-11T05:04:57.748Z},
 accessed = {2019-07-11},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-25T07:49:59.774Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2019decmrta},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {Multiple robotic systems, working together, can provide important solutions to different real-world applications (e.g., disaster response), among which task allocation problems feature prominently. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involves novel construction and matching of bipartite graphs. Its performance is tested on a multi-UAV flood response application.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and DePauw, David and Chowdhury, Souma},
 doi = {10.1109/MRS.2019.8901062},
 booktitle = {2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)},
 keywords = {ADAMS,Conference,MRTA,Swarm}
}

@inproceedings{
 title = {A Robust Tracking Control System for a 2-DOF Manipulator Based on an On-line Linearization Technique},
 type = {inproceedings},
 year = {2016},
 id = {8731ce8e-fdb7-3c2f-82d8-1c9ff22d765e},
 created = {2019-10-31T01:04:49.851Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-06-15T14:37:13.987Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {saadati2016robust},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Saadati, Hamid Reza and Kalhor, Ahmad and Ghassemi, Payam},
 booktitle = {3rd International Conference on Electrical Engineering, Electrical and Computer}
}

@inproceedings{
 title = {Comparative Performance Analysis of Various Training for Control of Inverted Pendulum Using Narma-L2 Control},
 type = {inproceedings},
 year = {2015},
 id = {1c437e44-ab80-31e0-920e-9c0d3ecc52d5},
 created = {2019-10-31T01:06:12.805Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-06-15T14:37:13.989Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {kosari2015comparative},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Kosari, A and Ghassemi, Payam and Gilani, Ali},
 booktitle = {2nd National Conference on Mathematics and its Applications in Engineering Sciences}
}

@inproceedings{
 title = {Dynamic Equation of Constrained Motion of Humanoid Robots Using the Analytical Method},
 type = {inproceedings},
 year = {2014},
 publisher = {Iranian Society of Mechanical Engineering},
 id = {2296cb41-9ef2-382f-af39-48ca84af9d73},
 created = {2019-10-31T01:10:50.408Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-06-15T14:37:13.985Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2014dynamic},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Masouleh, M.T.},
 booktitle = {ISME 2014 22nd Annual International Conference}
}

@inproceedings{
 title = {Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System},
 type = {inproceedings},
 year = {2018},
 websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
 day = {26},
 id = {de1afd2c-7502-3e5f-afe0-2feeeb76b88b},
 created = {2020-06-14T21:18:56.238Z},
 accessed = {2020-06-14},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:05.724Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {liu2018battery},
 private_publication = {false},
 abstract = {This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.},
 bibtype = {inproceedings},
 author = {Liu, Yuanzhi and Ghassemi, Payam and Chowdhury, Souma and Zhang, Jie},
 doi = {10.1115/DETC2018-85620},
 booktitle = {Volume 2B: 44th Design Automation Conference},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring},
 type = {inproceedings},
 year = {2020},
 websites = {https://arc.aiaa.org/doi/10.2514/6.2020-3183},
 month = {6},
 publisher = {American Institute of Aeronautics and Astronautics},
 day = {15},
 city = {Virtual Event},
 id = {c0028f93-0234-3d27-9b51-13ad2dc29da7},
 created = {2020-06-15T01:47:41.205Z},
 accessed = {2020-06-14},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-10T06:28:06.000Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2020physbo},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Behjat, Amir and Zeng, Chen and Lulekar, Sumeet S. and Rai, Rahul and Chowdhury, Souma},
 doi = {10.2514/6.2020-3183},
 booktitle = {AIAA AVIATION 2020 FORUM},
 keywords = {ADAMS,SBO}
}

@inproceedings{
 title = {Using Physiological Measurements to Predict the Tactical Decisions in Human Swarm Teams},
 type = {inproceedings},
 year = {2020},
 month = {10},
 publisher = {IEEE},
 city = {Toronto, Canada},
 id = {b32e2cff-bd6a-3705-8410-b2d49385bebb},
 created = {2020-08-21T09:23:24.515Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-08-21T09:25:47.567Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {hemanth2020smc},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Manjunatha, Hemanth and Distefano, Joseph P. and Ghassemi, Payam and Chowdhury, Souma and Dantu, Kathik and Doermann, David and Esfahani, Ehsan},
 booktitle = {2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
 keywords = {ADAMS,HumanSwarm,Swarm}
}

@inproceedings{
 title = {Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas},
 type = {inproceedings},
 year = {2021},
 publisher = {IEEE (Accepted)},
 city = {Xi’an, China},
 id = {0bedc84d-3690-3ba6-bc3f-fb3dd9a1faae},
 created = {2020-11-14T06:47:09.948Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2021-02-28T19:55:54.281Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Collins, Leighton and Ghassemi, Payam and Chowdhury, Souma and Dantu, Karthik and Esfahani, Ehsan and Doermann, David},
 booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! 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To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"collins-ghassemi-chowdhury-dantu-esfahani-doermann-scalablecoveragepathplanningofmultirobotteamsformonitoringnonconvexareas-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Collins,  L.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Chowdhury,  S.; Dantu,  K.; Esfahani,  E.;  and Doermann,  D.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas.\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>IEEE International Conference on Robotics and Automation (ICRA)</i>,  2021. IEEE (Accepted)\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/collins-ghassemi-chowdhury-dantu-esfahani-doermann-scalablecoveragepathplanningofmultirobotteamsformonitoringnonconvexareas-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('collins-ghassemi-chowdhury-dantu-esfahani-doermann-scalablecoveragepathplanningofmultirobotteamsformonitoringnonconvexareas-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas},\n type = {inproceedings},\n year = {2021},\n publisher = {IEEE (Accepted)},\n city = {Xi’an, China},\n id = {0bedc84d-3690-3ba6-bc3f-fb3dd9a1faae},\n created = {2020-11-14T06:47:09.948Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2021-02-28T19:55:54.281Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Collins, Leighton and Ghassemi, Payam and Chowdhury, Souma and Dantu, Karthik and Esfahani, Ehsan and Doermann, David},\n booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Behjat,  A.; Zeng,  C.; Lulekar,  S., S.; Rai,  R.;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf', event);\">\n    Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>AIAA AVIATION 2020 FORUM</i>, 6 2020. American Institute of Aeronautics and Astronautics\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf\"\n     onclick=\"log_download('ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/4259c768-3967-f275-63ab-12b37a2a1b3a/ghassemi2020physbo.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/6.2020-3183\"\n     onclick=\"log_download('ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020', 'https://arc.aiaa.org/doi/10.2514/6.2020-3183', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2514/6.2020-3183\"\n     onclick=\"log_download('ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020', 'http://doi.org/10.2514/6.2020-3183', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-behjat-zeng-lulekar-rai-chowdhury-physicsawaresurrogatebasedoptimizationwithtransfermappinggaussianprocessesforbioinspiredflowtailoring-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Physics-Aware Surrogate based Optimization with Transfer Mapping Gaussian Processes: for Bio-inspired Flow Tailoring},\n type = {inproceedings},\n year = {2020},\n websites = {https://arc.aiaa.org/doi/10.2514/6.2020-3183},\n month = {6},\n publisher = {American Institute of Aeronautics and Astronautics},\n day = {15},\n city = {Virtual Event},\n id = {c0028f93-0234-3d27-9b51-13ad2dc29da7},\n created = {2020-06-15T01:47:41.205Z},\n accessed = {2020-06-14},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:06.000Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2020physbo},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Behjat, Amir and Zeng, Chen and Lulekar, Sumeet S. and Rai, Rahul and Chowdhury, Souma},\n doi = {10.2514/6.2020-3183},\n booktitle = {AIAA AVIATION 2020 FORUM},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"manjunatha-distefano-ghassemi-chowdhury-dantu-doermann-esfahani-usingphysiologicalmeasurementstopredictthetacticaldecisionsinhumanswarmteams-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Manjunatha,  H.; Distefano,  J., P.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Chowdhury,  S.; Dantu,  K.; Doermann,  D.;  and Esfahani,  E.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Using Physiological Measurements to Predict the Tactical Decisions in Human Swarm Teams.\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)</i>, 10 2020. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/manjunatha-distefano-ghassemi-chowdhury-dantu-doermann-esfahani-usingphysiologicalmeasurementstopredictthetacticaldecisionsinhumanswarmteams-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('manjunatha-distefano-ghassemi-chowdhury-dantu-doermann-esfahani-usingphysiologicalmeasurementstopredictthetacticaldecisionsinhumanswarmteams-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Using Physiological Measurements to Predict the Tactical Decisions in Human Swarm Teams},\n type = {inproceedings},\n year = {2020},\n month = {10},\n publisher = {IEEE},\n city = {Toronto, Canada},\n id = {b32e2cff-bd6a-3705-8410-b2d49385bebb},\n created = {2020-08-21T09:23:24.515Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-08-21T09:25:47.567Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {hemanth2020smc},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Manjunatha, Hemanth and Distefano, Joseph P. and Ghassemi, Payam and Chowdhury, Souma and Dantu, Kathik and Doermann, David and Esfahani, Ehsan},\n booktitle = {2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},\n keywords = {ADAMS,HumanSwarm,Swarm}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019', 'https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658', event);\">\n    Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 1: 39th Computers and Information in Engineering Conference</i>, pages V001T02A058, 8 2019. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658\"\n     onclick=\"log_download('ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019', 'https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2019-97887\"\n     onclick=\"log_download('ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019', 'http://doi.org/10.1115/DETC2019-97887', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search},\n type = {inproceedings},\n year = {2019},\n pages = {V001T02A058},\n websites = {https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {18},\n city = {Anaheim, CA, USA},\n id = {595b1ccf-7204-3901-9fbc-ac4ca113a8d9},\n created = {2019-07-08T20:56:16.682Z},\n accessed = {2019-07-08},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-25T07:49:59.592Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2019bswarm-detc},\n patent_owner = {Ghassemi, Payam},\n notes = {Identified as one of the strongest CIE 2019 papers; invited to submit to Journal of Computing and Information Science in Engineering.},\n private_publication = {false},\n abstract = {Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Chowdhury, Souma},\n doi = {10.1115/DETC2019-97887},\n booktitle = {Volume 1: 39th Computers and Information in Engineering Conference},\n keywords = {ADAMS,BSwarm,Swarm}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Lulekar,  S., S.;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf', event);\">\n    Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>AIAA Aviation 2019 Forum</i>, 6 2019. American Institute of Aeronautics and Astronautics\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf\"\n     onclick=\"log_download('ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/311265fb-adcc-95c1-3cc2-d196ae2920ba/ghassemi2019amrpbs.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/6.2019-2983\"\n     onclick=\"log_download('ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019', 'https://arc.aiaa.org/doi/10.2514/6.2019-2983', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2514/6.2019-2983\"\n     onclick=\"log_download('ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019', 'http://doi.org/10.2514/6.2019-2983', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-lulekar-chowdhury-adaptivemodelrefinementwithbatchbayesiansamplingforoptimizationofbioinspiredflowtailoring-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring},\n type = {inproceedings},\n year = {2019},\n websites = {https://arc.aiaa.org/doi/10.2514/6.2019-2983},\n month = {6},\n publisher = {American Institute of Aeronautics and Astronautics},\n day = {17},\n city = {Dallas, TX, USA},\n id = {f8e1829b-a456-33db-98eb-e0b2402024c0},\n created = {2019-07-08T20:56:27.362Z},\n accessed = {2019-07-08},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:06.192Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2019amrpbs},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to &quot;when&quot; to sample and &quot;how many&quot; samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards &quot;where&quot; to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (&quot;q-EI&quot;), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Lulekar, Sumeet S. and Chowdhury, Souma},\n doi = {10.2514/6.2019-2983},\n booktitle = {AIAA Aviation 2019 Forum},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to \"when\" to sample and \"how many\" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards \"where\" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria (\"q-EI\"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf', event);\">\n    Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019</i>, pages 188-194, 8 2019. Institute of Electrical and Electronics Engineers. (acceptance: 33%)\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf\"\n     onclick=\"log_download('ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/\"\n     onclick=\"log_download('ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019', 'http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/MRS.2019.8901084\"\n     onclick=\"log_download('ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019', 'http://doi.org/10.1109/MRS.2019.8901084', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search},\n type = {inproceedings},\n year = {2019},\n keywords = {ADAMS,BSwarm,Conference,Swarm},\n pages = {188-194},\n websites = {http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/},\n month = {8},\n publisher = {Institute of Electrical and Electronics Engineers. (acceptance: 33%)},\n day = {22},\n city = {New Brunswick, NJ},\n id = {4d126477-38d4-3fc0-8451-18bd5bbcf53e},\n created = {2019-07-11T05:04:57.734Z},\n accessed = {2019-07-11},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-25T07:49:59.807Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2019bswarm-mrs},\n patent_owner = {Ghassemi, Payam},\n notes = {(acceptance: 33%)},\n private_publication = {false},\n abstract = {Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots&#x27; planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Chowdhury, Souma},\n doi = {10.1109/MRS.2019.8901084},\n booktitle = {International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots' planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-depauw-chowdhury-decentralizeddynamictaskallocationinswarmroboticsystemsfordisasterresponseextendedabstract-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; DePauw,  D.;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/45d324b2-176b-99df-1091-a3415ffd0a56/ghassemi2019decmrta.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-depauw-chowdhury-decentralizeddynamictaskallocationinswarmroboticsystemsfordisasterresponseextendedabstract-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/45d324b2-176b-99df-1091-a3415ffd0a56/ghassemi2019decmrta.pdf.pdf', event);\">\n    Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response: Extended Abstract.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)</i>, pages 83-85, 8 2019. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/45d324b2-176b-99df-1091-a3415ffd0a56/ghassemi2019decmrta.pdf.pdf\"\n     onclick=\"log_download('ghassemi-depauw-chowdhury-decentralizeddynamictaskallocationinswarmroboticsystemsfordisasterresponseextendedabstract-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/45d324b2-176b-99df-1091-a3415ffd0a56/ghassemi2019decmrta.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response: Extended Abstract [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://arxiv.org/abs/1907.04394,https://ieeexplore.ieee.org/document/8901062/\"\n     onclick=\"log_download('ghassemi-depauw-chowdhury-decentralizeddynamictaskallocationinswarmroboticsystemsfordisasterresponseextendedabstract-2019', 'http://arxiv.org/abs/1907.04394,https://ieeexplore.ieee.org/document/8901062/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response: Extended Abstract [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/MRS.2019.8901062\"\n     onclick=\"log_download('ghassemi-depauw-chowdhury-decentralizeddynamictaskallocationinswarmroboticsystemsfordisasterresponseextendedabstract-2019', 'http://doi.org/10.1109/MRS.2019.8901062', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-depauw-chowdhury-decentralizeddynamictaskallocationinswarmroboticsystemsfordisasterresponseextendedabstract-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-depauw-chowdhury-decentralizeddynamictaskallocationinswarmroboticsystemsfordisasterresponseextendedabstract-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response: Extended Abstract},\n type = {inproceedings},\n year = {2019},\n pages = {83-85},\n websites = {http://arxiv.org/abs/1907.04394,https://ieeexplore.ieee.org/document/8901062/},\n month = {8},\n publisher = {IEEE},\n day = {22},\n city = {New Brunswick, NJ},\n id = {3c46070e-f917-332e-adf0-6246955d7f09},\n created = {2019-07-11T05:04:57.748Z},\n accessed = {2019-07-11},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-25T07:49:59.774Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2019decmrta},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {Multiple robotic systems, working together, can provide important solutions to different real-world applications (e.g., disaster response), among which task allocation problems feature prominently. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involves novel construction and matching of bipartite graphs. Its performance is tested on a multi-UAV flood response application.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and DePauw, David and Chowdhury, Souma},\n doi = {10.1109/MRS.2019.8901062},\n booktitle = {2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)},\n keywords = {ADAMS,Conference,MRTA,Swarm}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Multiple robotic systems, working together, can provide important solutions to different real-world applications (e.g., disaster response), among which task allocation problems feature prominently. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involves novel construction and matching of bipartite graphs. Its performance is tested on a multi-UAV flood response application.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-chowdhury-decentralizedtaskallocationinmultirobotsystemsviabipartitegraphmatchingaugmentedwithfuzzyclustering-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/071f92f5-3416-22d1-5a17-c80d63381b66/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-chowdhury-decentralizedtaskallocationinmultirobotsystemsviabipartitegraphmatchingaugmentedwithfuzzyclustering-2018', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/071f92f5-3416-22d1-5a17-c80d63381b66/full_text.pdf.pdf', event);\">\n    Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 2A: 44th Design Automation Conference</i>, pages V02AT03A014, 8 2018. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/071f92f5-3416-22d1-5a17-c80d63381b66/full_text.pdf.pdf\"\n     onclick=\"log_download('ghassemi-chowdhury-decentralizedtaskallocationinmultirobotsystemsviabipartitegraphmatchingaugmentedwithfuzzyclustering-2018', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/071f92f5-3416-22d1-5a17-c80d63381b66/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2018-86161,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51753/Quebec City, Quebec, Canada/273573\"\n     onclick=\"log_download('ghassemi-chowdhury-decentralizedtaskallocationinmultirobotsystemsviabipartitegraphmatchingaugmentedwithfuzzyclustering-2018', 'http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2018-86161,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51753/Quebec City, Quebec, Canada/273573', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2018-86161\"\n     onclick=\"log_download('ghassemi-chowdhury-decentralizedtaskallocationinmultirobotsystemsviabipartitegraphmatchingaugmentedwithfuzzyclustering-2018', 'http://doi.org/10.1115/DETC2018-86161', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-chowdhury-decentralizedtaskallocationinmultirobotsystemsviabipartitegraphmatchingaugmentedwithfuzzyclustering-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-chowdhury-decentralizedtaskallocationinmultirobotsystemsviabipartitegraphmatchingaugmentedwithfuzzyclustering-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented With Fuzzy Clustering},\n type = {inproceedings},\n year = {2018},\n pages = {V02AT03A014},\n websites = {http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2018-86161,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51753/Quebec City, Quebec, Canada/273573},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {26},\n id = {af5d7d17-8c27-3faf-93cf-b8653370cf4e},\n created = {2019-07-08T20:53:03.602Z},\n accessed = {2019-07-08},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-25T07:49:59.568Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2018decmata},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Chowdhury, Souma},\n doi = {10.1115/DETC2018-86161},\n booktitle = {Volume 2A: 44th Design Automation Conference},\n keywords = {ADAMS,MRTA,Swarm}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Lulekar,  S.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf', event);\">\n    CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>2018 Multidisciplinary Analysis and Optimization Conference</i>, 6 2018. American Institute of Aeronautics and Astronautics\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf\"\n     onclick=\"log_download('lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/0051451d-31f4-b10f-74c8-9c4eacb83786/lulekar2018riblet.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://arc.aiaa.org/doi/10.2514/6.2018-3107\"\n     onclick=\"log_download('lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018', 'https://arc.aiaa.org/doi/10.2514/6.2018-3107', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2514/6.2018-3107\"\n     onclick=\"log_download('lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018', 'http://doi.org/10.2514/6.2018-3107', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lulekar-ghassemi-chowdhury-cfdbasedanalysisandsurrogatebasedoptimizationofbioinspiredsurfaceribletsforaerodynamicefficiency-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {CFD-based Analysis and Surrogate-based Optimization of Bio-inspired Surface Riblets for Aerodynamic Efficiency},\n type = {inproceedings},\n year = {2018},\n websites = {https://arc.aiaa.org/doi/10.2514/6.2018-3107},\n month = {6},\n publisher = {American Institute of Aeronautics and Astronautics},\n day = {25},\n city = {Atlanta, GA, USA},\n id = {cc163f9a-922a-36bd-b7d4-712619fd852f},\n created = {2019-07-08T20:54:02.941Z},\n accessed = {2019-07-08},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.767Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {lulekar2018riblet},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.},\n bibtype = {inproceedings},\n author = {Lulekar, Sumeet and Ghassemi, Payam and Chowdhury, Souma},\n doi = {10.2514/6.2018-3107},\n booktitle = {2018 Multidisciplinary Analysis and Optimization Conference},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  For dynamic systems that operate in the transitional range of Reynolds number (laminar to turbulent How), passive and active surface features can play an important role in maximizing the aerodynamic efficiency or control authority. This paper focuses on passive riblet-like features that are inspired by those naturally observed in marine animals. Unlike more conventional riblet geomtries, e.g., sawtooth and scalloped surface ridges, a smoother and parametrized riblet design is considered (defined by a bell-shaped function), studied and optimized to obtain maximum aerodynamic drag reduction. A CFD-based analysis is performed using RANS solvers to quantify the aerodynamic forces on a 3D airfoil section (NACA0012 airfoil is used), with the riblet-like features that run along the chord-wise direction on the top surface of the airfoil. To pursue optimization of the riblet geometry and spacing, surrogate modeling is performed first to alleviate the prohibitive computational cost of the CFD simulations, and a variable fidelity optimization method is used to subsequently maximize drag reduction for different angle of attack cases. Up to 6.6% reduction in drag is observed with optimal riblet design, compared to the bare 3D airfoil section. Further insights are derived into the How physics driving the aerodynamic efficiency benefits of smooth riblets, with riblets of 0.5 height-to-spacing ratio (and that impedes upward momentum transfer) identified as particularly promising in our case studies.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mousavi-talemasouleh-kalhor-ghassemi-pushrecoverymethodsbasedonadmittancecontrolstrategiesforanaoh25humanoid-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Mousavi,  F., S.; Tale Masouleh,  M.; Kalhor,  A.;  and <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/337a7d85-a784-0601-1451-8df174492312/mousavi2018push.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mousavi-talemasouleh-kalhor-ghassemi-pushrecoverymethodsbasedonadmittancecontrolstrategiesforanaoh25humanoid-2018', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/337a7d85-a784-0601-1451-8df174492312/mousavi2018push.pdf.pdf', event);\">\n    Push Recovery Methods Based on Admittance Control Strategies for a NAO-H25 Humanoid.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM)</i>, pages 451-457, 10 2018. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/337a7d85-a784-0601-1451-8df174492312/mousavi2018push.pdf.pdf\"\n     onclick=\"log_download('mousavi-talemasouleh-kalhor-ghassemi-pushrecoverymethodsbasedonadmittancecontrolstrategiesforanaoh25humanoid-2018', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/337a7d85-a784-0601-1451-8df174492312/mousavi2018push.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Push Recovery Methods Based on Admittance Control Strategies for a NAO-H25 Humanoid [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8657613/\"\n     onclick=\"log_download('mousavi-talemasouleh-kalhor-ghassemi-pushrecoverymethodsbasedonadmittancecontrolstrategiesforanaoh25humanoid-2018', 'https://ieeexplore.ieee.org/document/8657613/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Push Recovery Methods Based on Admittance Control Strategies for a NAO-H25 Humanoid [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ICRoM.2018.8657613\"\n     onclick=\"log_download('mousavi-talemasouleh-kalhor-ghassemi-pushrecoverymethodsbasedonadmittancecontrolstrategiesforanaoh25humanoid-2018', 'http://doi.org/10.1109/ICRoM.2018.8657613', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mousavi-talemasouleh-kalhor-ghassemi-pushrecoverymethodsbasedonadmittancecontrolstrategiesforanaoh25humanoid-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mousavi-talemasouleh-kalhor-ghassemi-pushrecoverymethodsbasedonadmittancecontrolstrategiesforanaoh25humanoid-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Push Recovery Methods Based on Admittance Control Strategies for a NAO-H25 Humanoid},\n type = {inproceedings},\n year = {2018},\n keywords = {Humanoid,Taarlab},\n pages = {451-457},\n websites = {https://ieeexplore.ieee.org/document/8657613/},\n month = {10},\n publisher = {IEEE},\n id = {758b43e7-26ae-3469-bc4c-a07e538d916f},\n created = {2019-07-08T20:56:34.647Z},\n accessed = {2019-07-08},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.528Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {mousavi2018push},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Mousavi, Fatemeh Sadat and Tale Masouleh, Mehdi and Kalhor, Ahmad and Ghassemi, Payam},\n doi = {10.1109/ICRoM.2018.8657613},\n booktitle = {2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Liu,  Y.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Chowdhury,  S.;  and Zhang,  J.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832', event);\">\n    Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 2B: 44th Design Automation Conference</i>, 8 2018. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832\"\n     onclick=\"log_download('liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2018-85620\"\n     onclick=\"log_download('liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018', 'http://doi.org/10.1115/DETC2018-85620', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('liu-ghassemi-chowdhury-zhang-surrogatebasedmultiobjectiveoptimizationofjtypebatterythermalmanagementsystem-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Surrogate Based Multi-Objective Optimization of J-Type Battery Thermal Management System},\n type = {inproceedings},\n year = {2018},\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2018/51760/Quebec City, Quebec, Canada/274832},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {26},\n id = {de1afd2c-7502-3e5f-afe0-2feeeb76b88b},\n created = {2020-06-14T21:18:56.238Z},\n accessed = {2020-06-14},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.724Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {liu2018battery},\n private_publication = {false},\n abstract = {This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.},\n bibtype = {inproceedings},\n author = {Liu, Yuanzhi and Ghassemi, Payam and Chowdhury, Souma and Zhang, Jie},\n doi = {10.1115/DETC2018-85620},\n booktitle = {Volume 2B: 44th Design Automation Conference},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper proposes a novel and flexible J-type air-based battery thermal management system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling valves, the J-type BTMS can be adaptively controlled in real time to help balance the temperature uniformity and energy efficiency under various charging/discharging situations (especially extreme fast changing). Results of computational fluid dynamics simulations show that the J-type system performs better than the U-type and Z-type systems. To further improve the thermal performance of the proposed J-type BTMS, a surrogate-based multi-objective optimization is performed, with the consideration of the two major objectives, i.e., uniformity and energy efficiency. The concurrent surrogate selection (COSMOS) framework is adopted in this paper to determine the most suitable surrogate models. Optimization results show that: (i) the uniformity of the temperature distribution is improved by 38.6% compared to the benchmark, (ii) the maximum temperature is reduced by 19.1%, and (iii) the pressure drop is decreased by 14.5%.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"mousavi-ghassemi-kalhor-masouleh-dynamicbalanceofanaoh25humanoidrobotbasedonmodelpredictivecontrol-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Mousavi,  F., S.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Kalhor,  A.;  and Masouleh,  M., T.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/3847f735-33f6-aae9-7471-b6c9b1afbeb6/mousavi2017dynamic.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mousavi-ghassemi-kalhor-masouleh-dynamicbalanceofanaoh25humanoidrobotbasedonmodelpredictivecontrol-2017', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/3847f735-33f6-aae9-7471-b6c9b1afbeb6/mousavi2017dynamic.pdf.pdf', event);\">\n    Dynamic Balance of a NAO H25 Humanoid Robot based on Model Predictive Control.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)</i>, pages 0156-0164, 12 2017. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/3847f735-33f6-aae9-7471-b6c9b1afbeb6/mousavi2017dynamic.pdf.pdf\"\n     onclick=\"log_download('mousavi-ghassemi-kalhor-masouleh-dynamicbalanceofanaoh25humanoidrobotbasedonmodelpredictivecontrol-2017', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/3847f735-33f6-aae9-7471-b6c9b1afbeb6/mousavi2017dynamic.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Dynamic Balance of a NAO H25 Humanoid Robot based on Model Predictive Control [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://ieeexplore.ieee.org/document/8324964/\"\n     onclick=\"log_download('mousavi-ghassemi-kalhor-masouleh-dynamicbalanceofanaoh25humanoidrobotbasedonmodelpredictivecontrol-2017', 'http://ieeexplore.ieee.org/document/8324964/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Dynamic Balance of a NAO H25 Humanoid Robot based on Model Predictive Control [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/KBEI.2017.8324964\"\n     onclick=\"log_download('mousavi-ghassemi-kalhor-masouleh-dynamicbalanceofanaoh25humanoidrobotbasedonmodelpredictivecontrol-2017', 'http://doi.org/10.1109/KBEI.2017.8324964', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mousavi-ghassemi-kalhor-masouleh-dynamicbalanceofanaoh25humanoidrobotbasedonmodelpredictivecontrol-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mousavi-ghassemi-kalhor-masouleh-dynamicbalanceofanaoh25humanoidrobotbasedonmodelpredictivecontrol-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Dynamic Balance of a NAO H25 Humanoid Robot based on Model Predictive Control},\n type = {inproceedings},\n year = {2017},\n keywords = {Humanoid,Taarlab},\n pages = {0156-0164},\n websites = {http://ieeexplore.ieee.org/document/8324964/},\n month = {12},\n publisher = {IEEE},\n id = {a57467a7-b0dd-3852-a38a-a5b504630b62},\n created = {2019-07-05T16:08:44.947Z},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.700Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {mousavi2017dynamic},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {© 2017 IEEE. This paper proposes a model predictive based approach for dynamic balance of a NAO H25 Humanoid robot. Due to the inherent mechanical properties of a humanoid robot, it has a huge potential to lose its stability during its movements. Using an integrated dynamic model leads to control three potential situations a humanoid robot may encounter, namely under-actuation, over-actuation, and fully-actuation. In this paper, by resorting to the so-called Zero-Momentum Point (ZMP) criterion for robots stability, an investigation is carried out on the stability equations and movement patterns in their relevant state space regarding to choosing ZMP as a sustainability criterion, to the end of achieving an appropriate path for robot&#x27;s center of mass and two legs. In order to apply the concept of the Model-Predictive Control (MPC) based walking pattern, the ZMP state space equations are distributed along the horizon length, besides defining a specific cost function, which covers control input and ZMP path. Some linear constraints are applied to ZMP implementation and robot steps, which leads to optimize the cost function. This procedure is followed by evaluating how the weight of different parts of the cost function influences its performance. Implementation and simulation in MATLAB, Python, and ADAMS estimates and verifies the MPC performance.},\n bibtype = {inproceedings},\n author = {Mousavi, Fatemeh Sadat and Ghassemi, Payam and Kalhor, Ahmad and Masouleh, Mehdi Tale},\n doi = {10.1109/KBEI.2017.8324964},\n booktitle = {2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  © 2017 IEEE. This paper proposes a model predictive based approach for dynamic balance of a NAO H25 Humanoid robot. Due to the inherent mechanical properties of a humanoid robot, it has a huge potential to lose its stability during its movements. Using an integrated dynamic model leads to control three potential situations a humanoid robot may encounter, namely under-actuation, over-actuation, and fully-actuation. In this paper, by resorting to the so-called Zero-Momentum Point (ZMP) criterion for robots stability, an investigation is carried out on the stability equations and movement patterns in their relevant state space regarding to choosing ZMP as a sustainability criterion, to the end of achieving an appropriate path for robot's center of mass and two legs. In order to apply the concept of the Model-Predictive Control (MPC) based walking pattern, the ZMP state space equations are distributed along the horizon length, besides defining a specific cost function, which covers control input and ZMP path. Some linear constraints are applied to ZMP implementation and robot steps, which leads to optimize the cost function. This procedure is followed by evaluating how the weight of different parts of the cost function influences its performance. Implementation and simulation in MATLAB, Python, and ADAMS estimates and verifies the MPC performance.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Zhu,  K.;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375', event);\">\n    Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 2B: 43rd Design Automation Conference</i>, pages V02BT03A026, 8 2017. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375\"\n     onclick=\"log_download('ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2017-68350\"\n     onclick=\"log_download('ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017', 'http://doi.org/10.1115/DETC2017-68350', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-zhu-chowdhury-optimalsurrogateandneuralnetworkmodelingfordayaheadforecastingofthehourlyenergyconsumptionofuniversitybuildings-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings},\n type = {inproceedings},\n year = {2017},\n pages = {V02BT03A026},\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58134/Cleveland, Ohio, USA/252375},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {6},\n city = {Cleveland, Ohio, USA},\n id = {b74d514d-3038-3197-aa98-1cad32734741},\n created = {2019-07-08T20:51:37.509Z},\n accessed = {2019-07-08},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:06.003Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2017optimal},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Zhu, Kaige and Chowdhury, Souma},\n doi = {10.1115/DETC2017-68350},\n booktitle = {Volume 2B: 43rd Design Automation Conference},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Mehmani, Ali, Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086', event);\">\n    Optimal Metamodeling to Interpret Activity-Based Health Sensor Data.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices</i>, 8 2017. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086\"\n     onclick=\"log_download('ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017', 'https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Optimal Metamodeling to Interpret Activity-Based Health Sensor Data [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2017-68385\"\n     onclick=\"log_download('ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017', 'http://doi.org/10.1115/DETC2017-68385', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-mehmani-optimalmetamodelingtointerpretactivitybasedhealthsensordata-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Optimal Metamodeling to Interpret Activity-Based Health Sensor Data},\n type = {inproceedings},\n year = {2017},\n websites = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2017/58158/Cleveland, Ohio, USA/259086},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {6},\n city = {Cleveland, Ohio, USA},\n id = {dcb348b4-c676-3a9f-bd62-e9cbe8eed25d},\n created = {2019-07-08T21:19:22.379Z},\n accessed = {2019-07-08},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:05.932Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2017health},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {© 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of &lt; 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Mehmani, Ali, Chowdhury, Souma},\n doi = {10.1115/DETC2017-68385},\n booktitle = {Volume 3: 19th International Conference on Advanced Vehicle Technologies; 14th International Conference on Design Education; 10th Frontiers in Biomedical Devices},\n keywords = {ADAMS,SBO}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  © 2017 ASME. Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features - heart rate, QRS time, and QR ratio in each heartbeat period - models with median error of < 25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"saadati-kalhor-ghassemi-arobusttrackingcontrolsystemfora2dofmanipulatorbasedonanonlinelinearizationtechnique-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Saadati,  H., R.; Kalhor,  A.;  and <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A Robust Tracking Control System for a 2-DOF Manipulator Based on an On-line Linearization Technique.\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>3rd International Conference on Electrical Engineering, Electrical and Computer</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/saadati-kalhor-ghassemi-arobusttrackingcontrolsystemfora2dofmanipulatorbasedonanonlinelinearizationtechnique-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('saadati-kalhor-ghassemi-arobusttrackingcontrolsystemfora2dofmanipulatorbasedonanonlinelinearizationtechnique-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {A Robust Tracking Control System for a 2-DOF Manipulator Based on an On-line Linearization Technique},\n type = {inproceedings},\n year = {2016},\n id = {8731ce8e-fdb7-3c2f-82d8-1c9ff22d765e},\n created = {2019-10-31T01:04:49.851Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-06-15T14:37:13.987Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {saadati2016robust},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Saadati, Hamid Reza and Kalhor, Ahmad and Ghassemi, Payam},\n booktitle = {3rd International Conference on Electrical Engineering, Electrical and Computer}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"kosari-ghassemi-gilani-comparativeperformanceanalysisofvarioustrainingforcontrolofinvertedpendulumusingnarmal2control-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Kosari,  A.; <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Gilani,  A.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Comparative Performance Analysis of Various Training for Control of Inverted Pendulum Using Narma-L2 Control.\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>2nd National Conference on Mathematics and its Applications in Engineering Sciences</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kosari-ghassemi-gilani-comparativeperformanceanalysisofvarioustrainingforcontrolofinvertedpendulumusingnarmal2control-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kosari-ghassemi-gilani-comparativeperformanceanalysisofvarioustrainingforcontrolofinvertedpendulumusingnarmal2control-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Comparative Performance Analysis of Various Training for Control of Inverted Pendulum Using Narma-L2 Control},\n type = {inproceedings},\n year = {2015},\n id = {1c437e44-ab80-31e0-920e-9c0d3ecc52d5},\n created = {2019-10-31T01:06:12.805Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-06-15T14:37:13.989Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {kosari2015comparative},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Kosari, A and Ghassemi, Payam and Gilani, Ali},\n booktitle = {2nd National Conference on Mathematics and its Applications in Engineering Sciences}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-masouleh-kalhor-pushrecoveryfornaohumanoidrobot-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Masouleh,  M., T.;  and Kalhor,  A.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/450ad7fd-5e4b-dddd-1ce2-fa6eeb8fbc5e/ghassemi2014nao.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-masouleh-kalhor-pushrecoveryfornaohumanoidrobot-2014', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/450ad7fd-5e4b-dddd-1ce2-fa6eeb8fbc5e/ghassemi2014nao.pdf.pdf', event);\">\n    Push Recovery for NAO Humanoid Robot.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)</i>, pages 035-040, 10 2014. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/450ad7fd-5e4b-dddd-1ce2-fa6eeb8fbc5e/ghassemi2014nao.pdf.pdf\"\n     onclick=\"log_download('ghassemi-masouleh-kalhor-pushrecoveryfornaohumanoidrobot-2014', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/450ad7fd-5e4b-dddd-1ce2-fa6eeb8fbc5e/ghassemi2014nao.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Push Recovery for NAO Humanoid Robot [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://ieeexplore.ieee.org/document/6990873/\"\n     onclick=\"log_download('ghassemi-masouleh-kalhor-pushrecoveryfornaohumanoidrobot-2014', 'http://ieeexplore.ieee.org/document/6990873/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Push Recovery for NAO Humanoid Robot [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ICRoM.2014.6990873\"\n     onclick=\"log_download('ghassemi-masouleh-kalhor-pushrecoveryfornaohumanoidrobot-2014', 'http://doi.org/10.1109/ICRoM.2014.6990873', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-masouleh-kalhor-pushrecoveryfornaohumanoidrobot-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-masouleh-kalhor-pushrecoveryfornaohumanoidrobot-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Push Recovery for NAO Humanoid Robot},\n type = {inproceedings},\n year = {2014},\n keywords = {Humanoid,Taarlab},\n pages = {035-040},\n websites = {http://ieeexplore.ieee.org/document/6990873/},\n month = {10},\n publisher = {IEEE},\n id = {9e013e60-438b-33fc-8d10-ba346e36ac63},\n created = {2019-07-05T16:08:44.909Z},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-10T06:28:06.022Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2014nao},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {© 2014 IEEE. This paper presents the ankle, hip, and ankle-hip strategies in frontal plane for NAO humanoid robot. A humanoid is an unstable robotic mechanical system and the main and primary task of each humanoid is to maintain its balance. Moreover, NAO humanoid robot has 25 degrees-of-freedom with brushless DC actuators. The main challenges in the context of balancing of NAO humanoid are the high complexity of the robot and applying joint torque control approaches for NAO humanoid which is a position controlled robot. In this paper, for recovery and prediction the robot from external push, as the main contribution, the virtual leg model is proposed. The control objective is absorbing the external push and then recover the robot to its original configuration. PD controller is used to achieve the control objective. The performance and usage of these strategies and the model is validated by Webots. For each case study, an impulsive force is applied to NAO&#x27;s torso with various magnitudes. The performance and validation of the proposed push recovery schemes are verified in the simulation. Moreover, the same results are obtained from the practical implementation of these strategies on NAO H25 humanoid robot.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Masouleh, Mehdi Tale and Kalhor, Ahmad},\n doi = {10.1109/ICRoM.2014.6990873},\n booktitle = {2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  © 2014 IEEE. This paper presents the ankle, hip, and ankle-hip strategies in frontal plane for NAO humanoid robot. A humanoid is an unstable robotic mechanical system and the main and primary task of each humanoid is to maintain its balance. Moreover, NAO humanoid robot has 25 degrees-of-freedom with brushless DC actuators. The main challenges in the context of balancing of NAO humanoid are the high complexity of the robot and applying joint torque control approaches for NAO humanoid which is a position controlled robot. In this paper, for recovery and prediction the robot from external push, as the main contribution, the virtual leg model is proposed. The control objective is absorbing the external push and then recover the robot to its original configuration. PD controller is used to achieve the control objective. The performance and usage of these strategies and the model is validated by Webots. For each case study, an impulsive force is applied to NAO's torso with various magnitudes. The performance and validation of the proposed push recovery schemes are verified in the simulation. Moreover, the same results are obtained from the practical implementation of these strategies on NAO H25 humanoid robot.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-masouleh-dynamicequationofconstrainedmotionofhumanoidrobotsusingtheanalyticalmethod-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Masouleh,  M.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Dynamic Equation of Constrained Motion of Humanoid Robots Using the Analytical Method.\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>ISME 2014 22nd Annual International Conference</i>,  2014. Iranian Society of Mechanical Engineering\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-masouleh-dynamicequationofconstrainedmotionofhumanoidrobotsusingtheanalyticalmethod-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-masouleh-dynamicequationofconstrainedmotionofhumanoidrobotsusingtheanalyticalmethod-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Dynamic Equation of Constrained Motion of Humanoid Robots Using the Analytical Method},\n type = {inproceedings},\n year = {2014},\n publisher = {Iranian Society of Mechanical Engineering},\n id = {2296cb41-9ef2-382f-af39-48ca84af9d73},\n created = {2019-10-31T01:10:50.408Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-06-15T14:37:13.985Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2014dynamic},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Masouleh, M.T.},\n booktitle = {ISME 2014 22nd Annual International Conference}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);