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\n\n \n \n \n \n \n \n Desarrollo de un exoesqueleto mecatrónico para mano dominante, enfocado a facilitar funciones de agarres, de una persona con pérdida de función motora debido a una lesión medular.\n \n \n \n \n\n\n \n Karina Hidalgo Morillo, Daniel Araujo Moran, Ricardo Erazo Jossa, Dagoberto Mayorca-Torres, F., G., M.\n\n\n \n\n\n\n Comunicación de la Ciencia en la era Digital, pages 30-54. Editorial Fundación LASIRC, 2021.\n
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@inbook{\n type = {inbook},\n year = {2021},\n pages = {30-54},\n websites = {http://fundacionlasirc.org/images/cap_libro/RED_LASIRC_LIBRO_6.pdf%0A},\n publisher = {Editorial Fundación LASIRC},\n id = {eb089d0e-f309-3654-ad57-fc2ced46b75e},\n created = {2021-06-14T01:48:25.923Z},\n file_attached = {false},\n profile_id = {87ca63cb-6234-3429-a14d-4b37645af57b},\n group_id = {1ab4b95c-b54d-3697-b8de-c460d572d3f2},\n last_modified = {2022-05-16T00:17:00.802Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {KarinaHidalgoMorilloDanielAraujoMoranRicardoErazoJossaDagobertoMayorca-Torres2021},\n private_publication = {false},\n bibtype = {inbook},\n author = {Karina Hidalgo Morillo, Daniel Araujo Moran, Ricardo Erazo Jossa, Dagoberto Mayorca-Torres, Fabio Gómez Meneses},\n chapter = {Desarrollo de un exoesqueleto mecatrónico para mano dominante, enfocado a facilitar funciones de agarres, de una persona con pérdida de función motora debido a una lesión medular},\n title = {Comunicación de la Ciencia en la era Digital}\n}
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\n\n \n \n \n \n \n \n Desarrollo de un sistema de monitoreo de somnolencia para conductores a través del uso de visión artificial.\n \n \n \n \n\n\n \n Juan David Arcos Eraso, Diego Andres Lopez Alban, D., M.\n\n\n \n\n\n\n Comunicación de la Ciencia en la era Digital, pages 55-65. Editorial Fundación LASIRC, 2021.\n
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Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inbook{\n type = {inbook},\n year = {2021},\n pages = {55-65},\n websites = {http://fundacionlasirc.org/images/cap_libro/RED_LASIRC_LIBRO_6.pdf%0A},\n publisher = {Editorial Fundación LASIRC},\n id = {b6d73255-eebe-3836-b869-4b5209e0e8ad},\n created = {2021-06-14T01:48:26.134Z},\n file_attached = {false},\n profile_id = {87ca63cb-6234-3429-a14d-4b37645af57b},\n group_id = {1ab4b95c-b54d-3697-b8de-c460d572d3f2},\n last_modified = {2022-05-16T00:17:01.065Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {JuanDavidArcosErasoDiegoAndresLopezAlban2021},\n private_publication = {false},\n bibtype = {inbook},\n author = {Juan David Arcos Eraso, Diego Andres Lopez Alban, Dagoberto Mayorca-Torres},\n chapter = {Desarrollo de un sistema de monitoreo de somnolencia para conductores a través del uso de visión artificial},\n title = {Comunicación de la Ciencia en la era Digital}\n}
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\n\n \n \n \n \n \n Sistema de apoyo para la detección de broca y roya en sistemas de producción tradicional de cultivos de café a partir del análisis a variables climáticas a través de protocolos de comunicaciones para IoT.\n \n \n \n\n\n \n Jaime Miguel Castelblanco Bedoya, D., M.; and Castro, J., A., S.\n\n\n \n\n\n\n Comunicación de la Ciencia en la era Digital, pages 342-355. Editorial Fundación LASIRC, 2021.\n
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\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inbook{\n type = {inbook},\n year = {2021},\n pages = {342-355},\n publisher = {Editorial Fundación LASIRC},\n id = {8dddc3eb-8026-3405-ab25-ea051eeaec5e},\n created = {2021-06-14T01:48:26.175Z},\n file_attached = {false},\n profile_id = {87ca63cb-6234-3429-a14d-4b37645af57b},\n group_id = {1ab4b95c-b54d-3697-b8de-c460d572d3f2},\n last_modified = {2022-05-16T00:17:00.809Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {JaimeMiguelCastelblancoBedoya2021},\n private_publication = {false},\n bibtype = {inbook},\n author = {Jaime Miguel Castelblanco Bedoya, Dagoberto Mayorca-Torres and Castro, José Alejandro Salazar},\n chapter = {Sistema de apoyo para la detección de broca y roya en sistemas de producción tradicional de cultivos de café a partir del análisis a variables climáticas a través de protocolos de comunicaciones para IoT},\n title = {Comunicación de la Ciencia en la era Digital}\n}
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\n\n \n \n \n \n \n \n Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive Models.\n \n \n \n \n\n\n \n Sánchez-Pozo, N., N.; Trilles-Oliver, S.; Solé-Ribalta, A.; Lorente-Leyva, L., L.; Mayorca-Torres, D.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n pages 293-304. Sanjurjo González, H.; Pastor López, I.; García Bringas, P.; Quintián, H.; and Corchado, E., editor(s). Springer International Publishing, 2021.\n
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@inbook{\n type = {inbook},\n year = {2021},\n pages = {293-304},\n websites = {https://link.springer.com/10.1007/978-3-030-86271-8_25},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {94c3b43a-efaf-332a-9762-fc20be83a03e},\n created = {2021-12-13T10:41:50.590Z},\n file_attached = {false},\n profile_id = {87ca63cb-6234-3429-a14d-4b37645af57b},\n group_id = {1ab4b95c-b54d-3697-b8de-c460d572d3f2},\n last_modified = {2021-12-13T11:36:25.177Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Sanchez-Pozo2021},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper presents a comparative analysis of predictive models applied to air quality estimation. Currently, among other global issues, there is a high concern about air pollution, for this reason, there are several air quality indicators, with carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) being the main ones. When the concentration level of an indicator exceeds an established air quality safety threshold, it is considered harmful to human health, therefore, in cities like London, there are monitoring systems for air pollutants. This study aims to compare the efficiency of stochastic and heuristic predictive models for forecasting ozone (O3) concentration to estimate London's air quality by analyzing an open dataset retrieved from the London Datastore portal. Models based on data analysis have been widely used in air quality forecasting. This paper develops four predictive models (autoregressive integrated moving average - ARIMA, support vector regression - SVR, neural networks (specifically, long-short term memory - LSTM) and Facebook Prophet). Experimentally, ARIMA models and LSTM are proved to reach the highest accuracy in predicting the concentration of air pollutants among the considered models. As a result, the comparative analysis of the loss function (root-mean-square error) reveled that ARIMA and LSTM are the most suitable, accomplishing a low error rate of 0.18 and 0.20, respectively.},\n bibtype = {inbook},\n author = {Sánchez-Pozo, Nadia N and Trilles-Oliver, Sergi and Solé-Ribalta, Albert and Lorente-Leyva, Leandro L and Mayorca-Torres, Dagoberto and Peluffo-Ordóñez, Diego H},\n editor = {Sanjurjo González, Hugo and Pastor López, Iker and García Bringas, Pablo and Quintián, Héctor and Corchado, Emilio},\n doi = {10.1007/978-3-030-86271-8_25},\n chapter = {Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive Models}\n}
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\n This paper presents a comparative analysis of predictive models applied to air quality estimation. Currently, among other global issues, there is a high concern about air pollution, for this reason, there are several air quality indicators, with carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) being the main ones. When the concentration level of an indicator exceeds an established air quality safety threshold, it is considered harmful to human health, therefore, in cities like London, there are monitoring systems for air pollutants. This study aims to compare the efficiency of stochastic and heuristic predictive models for forecasting ozone (O3) concentration to estimate London's air quality by analyzing an open dataset retrieved from the London Datastore portal. Models based on data analysis have been widely used in air quality forecasting. This paper develops four predictive models (autoregressive integrated moving average - ARIMA, support vector regression - SVR, neural networks (specifically, long-short term memory - LSTM) and Facebook Prophet). Experimentally, ARIMA models and LSTM are proved to reach the highest accuracy in predicting the concentration of air pollutants among the considered models. As a result, the comparative analysis of the loss function (root-mean-square error) reveled that ARIMA and LSTM are the most suitable, accomplishing a low error rate of 0.18 and 0.20, respectively.\n
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\n\n \n \n \n \n \n \n Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques.\n \n \n \n \n\n\n \n López-Albán, D.; López-Barrera, A.; Mayorca-Torres, D.; and Peluffo-Ordóñez, D.\n\n\n \n\n\n\n pages 55-67. Florez, H.; and Pollo-Cattaneo, M., F., editor(s). Springer International Publishing, 2021.\n
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@inbook{\n type = {inbook},\n year = {2021},\n pages = {55-67},\n websites = {https://link.springer.com/10.1007/978-3-030-89654-6_5},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {ae8aa06d-e67b-3e3f-8e71-0da8b50c129d},\n created = {2021-12-13T10:41:50.594Z},\n file_attached = {false},\n profile_id = {87ca63cb-6234-3429-a14d-4b37645af57b},\n group_id = {1ab4b95c-b54d-3697-b8de-c460d572d3f2},\n last_modified = {2021-12-13T11:36:24.660Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Lopez-Alban2021},\n source_type = {CONF},\n private_publication = {false},\n abstract = {The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96% rating yield using the proposed methodology with the Fast Tree algorithm.},\n bibtype = {inbook},\n author = {López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D},\n editor = {Florez, Hector and Pollo-Cattaneo, Ma Florencia},\n doi = {10.1007/978-3-030-89654-6_5},\n chapter = {Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques}\n}
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\n The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96% rating yield using the proposed methodology with the Fast Tree algorithm.\n
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\n\n \n \n \n \n \n \n Comparison of Kernel Functions in the Classification of Irradiance Zones from Multispectral Satellite Images.\n \n \n \n \n\n\n \n Pachajoa, D.; Mora-Paz, H.; Mayorca-Torres, D.; Pachajoa, D.; Mora-Paz, H.; and Mayorca-Torres, D.\n\n\n \n\n\n\n
Revista Facultad de Ingeniería, 30(58): 2021. 2021.\n
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@article{\n title = {Comparison of Kernel Functions in the Classification of Irradiance Zones from Multispectral Satellite Images},\n type = {article},\n year = {2021},\n pages = {2021},\n volume = {30},\n websites = {http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0121-11292021000400106&lng=en&nrm=iso&tlng=en,http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0121-11292021000400106&lng=en&nrm=iso&tlng=en},\n publisher = {Universidad Pedagógica y Tecnológica de Colombia},\n id = {48dbdef6-4594-3201-86d0-56f9acf5752c},\n created = {2022-03-18T22:52:48.269Z},\n accessed = {2022-03-18},\n file_attached = {false},\n profile_id = {87ca63cb-6234-3429-a14d-4b37645af57b},\n group_id = {1ab4b95c-b54d-3697-b8de-c460d572d3f2},\n last_modified = {2022-05-16T00:17:01.157Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Due to the growing energy demand and the eminent global warming, there is special},\n bibtype = {article},\n author = {Pachajoa, Dalila-Mercedes and Mora-Paz, Héctor-Andrés and Mayorca-Torres, Dagoberto and Pachajoa, Dalila-Mercedes and Mora-Paz, Héctor-Andrés and Mayorca-Torres, Dagoberto},\n doi = {10.19053/01211129.V30.N58.2021.13845},\n journal = {Revista Facultad de Ingeniería},\n number = {58}\n}
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\n Due to the growing energy demand and the eminent global warming, there is special\n
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\n\n \n \n \n \n \n \n Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive Models.\n \n \n \n \n\n\n \n Sánchez-Pozo, N., N.; Trilles-Oliver, S.; Solé-Ribalta, A.; Lorente-Leyva, L., L.; Mayorca-Torres, D.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12886 LNAI: 293-304. 9 2021.\n
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@article{\n title = {Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive Models},\n type = {article},\n year = {2021},\n keywords = {Air quality,Contamination,Forecasting,Predictive models},\n pages = {293-304},\n volume = {12886 LNAI},\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-86271-8_25},\n month = {9},\n publisher = {Springer, Cham},\n day = {22},\n id = {693957cb-36d1-37a0-996a-c0a4352dbc67},\n created = {2022-03-18T22:53:13.470Z},\n accessed = {2022-03-18},\n file_attached = {false},\n profile_id = {87ca63cb-6234-3429-a14d-4b37645af57b},\n group_id = {1ab4b95c-b54d-3697-b8de-c460d572d3f2},\n last_modified = {2022-05-16T00:17:01.097Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents a comparative analysis of predictive models applied to air quality estimation. Currently, among other global issues, there is a high concern about air pollution, for this reason, there are several air quality indicators, with carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) being the main ones. When the concentration level of an indicator exceeds an established air quality safety threshold, it is considered harmful to human health, therefore, in cities like London, there are monitoring systems for air pollutants. This study aims to compare the efficiency of stochastic and heuristic predictive models for forecasting ozone (O3) concentration to estimate London's air quality by analyzing an open dataset retrieved from the London Datastore portal. Models based on data analysis have been widely used in air quality forecasting. This paper develops four predictive models (autoregressive integrated moving average - ARIMA, support vector regression - SVR, neural networks (specifically, long-short term memory - LSTM) and Facebook Prophet). Experimentally, ARIMA models and LSTM are proved to reach the highest accuracy in predicting the concentration of air pollutants among the considered models. As a result, the comparative analysis of the loss function (root-mean-square error) reveled that ARIMA and LSTM are the most suitable, accomplishing a low error rate of 0.18 and 0.20, respectively.},\n bibtype = {article},\n author = {Sánchez-Pozo, Nadia N. and Trilles-Oliver, Sergi and Solé-Ribalta, Albert and Lorente-Leyva, Leandro L. and Mayorca-Torres, Dagoberto and Peluffo-Ordóñez, Diego H.},\n doi = {10.1007/978-3-030-86271-8_25},\n journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}
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\n This paper presents a comparative analysis of predictive models applied to air quality estimation. Currently, among other global issues, there is a high concern about air pollution, for this reason, there are several air quality indicators, with carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) being the main ones. When the concentration level of an indicator exceeds an established air quality safety threshold, it is considered harmful to human health, therefore, in cities like London, there are monitoring systems for air pollutants. This study aims to compare the efficiency of stochastic and heuristic predictive models for forecasting ozone (O3) concentration to estimate London's air quality by analyzing an open dataset retrieved from the London Datastore portal. Models based on data analysis have been widely used in air quality forecasting. This paper develops four predictive models (autoregressive integrated moving average - ARIMA, support vector regression - SVR, neural networks (specifically, long-short term memory - LSTM) and Facebook Prophet). Experimentally, ARIMA models and LSTM are proved to reach the highest accuracy in predicting the concentration of air pollutants among the considered models. As a result, the comparative analysis of the loss function (root-mean-square error) reveled that ARIMA and LSTM are the most suitable, accomplishing a low error rate of 0.18 and 0.20, respectively.\n
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