Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Rubiano-Fonseca Astrid"'
Autor:
Rubiano Fonseca, Astrid
Le sujet principal de cette thèse est le développement d’un contrôle commande intelligentpour une prothèse de main robotique avec des parties souples qui comporte: (i) uneinterface homme–machine permettant de contrôler notre prothèse, (ii)
Externí odkaz:
http://www.theses.fr/2016PA100189/document
Publikováno v:
Revista Facultad de Ingeniería - UPTC; 2020, Vol. 29 Issue 54, p1-17, 35p
Publikováno v:
Universidad Michoacana de San Nicolás de Hidalgo
UMICH
Redalyc-UMICH
Redalyc
UMICH
Redalyc-UMICH
Redalyc
The development of new codes for earthquake-resistant structures has made possible to guarantee a better performance of buildings, when they are subjected to seismic actions. Therefore, it is convenient that current codes for design of building becom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45da7126418d69f7fd79c677ddb5b72f
http://www.redalyc.org/articulo.oa?id=40429649013
http://www.redalyc.org/articulo.oa?id=40429649013
Publikováno v:
Tecnura, Volume: 17, Issue: 37, Pages: 74-83, Published: SEP 2013
Resumen El presente artículo muestra cómo se planteó el diseño e implementación de un sistema de almacenamiento de señales biológicas con parámetros de seguridad, manejando dos escenarios importantes: por un lado el tratamiento de señales y
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______618::7995734b6e87bbd45b958c198c107814
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0123-921X2013000300008&lng=en&tlng=en
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0123-921X2013000300008&lng=en&tlng=en
Autor:
Aponte Rodríguez, Jorge Alexander, Amaya Hurtado, Darío, Rubiano Fonseca, Astrid, Prada Jiménez, Vladimir
Publikováno v:
Ciencia e Ingenieria Neogranadina; Vol. 20 No. 1 (2010); 77-96
Ciencia e Ingeniería Neogranadina; Vol. 20 Núm. 1 (2010); 77-96
Ciencia e Ingeniería Neogranadina; v. 20 n. 1 (2010); 77-96
Ciencia e Ingeniería Neogranadina, Volume: 20, Issue: 1, Pages: 77-96, Published: JUN 2010
Ciencia e Ingeniería Neogranadina; Vol. 20 Núm. 1 (2010); 77-96
Ciencia e Ingeniería Neogranadina; v. 20 n. 1 (2010); 77-96
Ciencia e Ingeniería Neogranadina, Volume: 20, Issue: 1, Pages: 77-96, Published: JUN 2010
Este artículo presenta el modelado, diseño y construcción de un cohete tipo aficionado de bajo costo, empleando un sistema de control activo por medio de una tobera móvil para lograr una mayor estabilidad. Se plantean los métodos y procedimiento
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::545048811d5441dcca5b4784470b1bbb
https://revistas.unimilitar.edu.co/index.php/rcin/article/view/1479
https://revistas.unimilitar.edu.co/index.php/rcin/article/view/1479
Publikováno v:
Tecnura; jul-sep2013, Vol. 17 Issue 37, p74-83, 10p
Autor:
DÍAZ PASCUAS, ANDRÉS ROBERTO1 pascuass@gmail.com, TORRES RODRÍGUEZ, DAVID LEONARDO1 andres.diaz.pascuas@gmail.com, RUBIANO FONSECA, ASTRID2,3,4,5 rubiano@unimilitar.edu.co
Publikováno v:
Revista Épsilon. ene-jun2015, Issue 24, p31-48. 18p.
Publikováno v:
Repositorio UMNG
Universidad Militar Nueva Granada
instacron:Universidad Militar Nueva Granada
Universidad Militar Nueva Granada
instacron:Universidad Militar Nueva Granada
El estudio de la robótica blanda explora el uso de materiales alternativos que permitan la creación de mecanismos flexibles, basados en la naturaleza. Para hacer realidad estos diseños se implementa el uso de materiales inteligentes, cuyas propied
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::37ebb7692ab82a47199ae78aafdeb680
https://hdl.handle.net/10654/39063
https://hdl.handle.net/10654/39063
Autor:
Gómez Vargas, Oscar Edilson
Publikováno v:
Repositorio UMNG
Universidad Militar Nueva Granada
instacron:Universidad Militar Nueva Granada
Universidad Militar Nueva Granada
instacron:Universidad Militar Nueva Granada
Se realizó esta investigación con el objetivo de diseñar una metodología de gerencia de proyectos de investigación, desarrollo tecnológico e innovación para el sector aeronáutico militar colombiano. Para esto, se utilizó una metodología de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::623a13ee1860a5b98e6780190aa4775f
Publikováno v:
FELTAN, Corina Maria, et al. ROBOT AUTÓNOMO LIMPIADOR DE PISO. Salão do Conhecimento, 2017, vol. 3, no 3.
DUBEY, Sanjay, et al. An FPGA based service Robot for floor cleaning with autonomous navigation. En Research Advances in Integrated Navigation Systems (RAINS), International Conference on. IEEE, 2016. p. 1-6.
VELANDIA, Natalie Segura; BELENO, Ruben D. Hernandez; MORENO, Robinson Jimenez. Applications of Deep Neural Networks. International Journal of Systems Signal Control and Engineering Applications, 2017, vol. 10, no 1.
WHITLEY, Darrell. A genetic algorithm tutorial. Statistics and computing, 1994, vol. 4, no 2, p. 65-85.
KARABOGA, Dervis; AKAY, Bahriye. A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 2009, vol. 214, no 1, p. 108-132.
ABE, Shigeo. Support vector machines for pattern classification. London: Springer, 2005.
HOPFIELD, John J. Artificial neural networks. IEEE Circuits and Devices Magazine, 1988, vol. 4, no 5, p. 3-10.
LECUN, Yann; BENGIO, Yoshua; HINTON, Geoffrey. Deep learning. Nature, 2015, vol. 521, no 7553, p. 436.
HINTON, Geoffrey E.; OSINDERO, Simon; TEH, Yee-Whye. A fast learning algorithm for deep belief nets. Neural computation, 2006, vol. 18, no 7, p. 1527-1554.
PASCANU, Razvan, et al. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026, 2013.
ZEILER, Matthew D.; FERGUS, Rob. Visualizing and understanding convolutional networks. En European conference on computer vision. Springer, Cham, 2014. p. 818-833.
LECUN, Yann, et al. Backpropagation applied to handwritten zip code recognition. Neural computation, 1989, vol. 1, no 4, p. 541-551.
KRIZHEVSKY, Alex; SUTSKEVER, Ilya; HINTON, Geoffrey E. Imagenet classification with deep convolutional neural networks. En Advances in neural information processing systems. 2012. p. 1097-1105.
KIM, Kyung-Min, et al. Pororobot: A deep learning robot that plays video Q&A games. En Proceedings of AAAI Fall Symposium on AI for HRI. 2015.
TAPUS, Adriana; MATARIC, Maja J.; SCASSELLATI, Brian. Socially assistive robotics [grand challenges of robotics]. IEEE Robotics & Automation Magazine, 2007, vol. 14, no 1, p. 35-42.
FEIL-SEIFER, David; MATARIC, Maja J. Defining socially assistive robotics. En Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on. IEEE, 2005. p. 465-468.
SONG, Won-Kyung; KIM, Jongbae. Novel assistive robot for self-feeding. En Robotic Systems-Applications, Control and Programming. InTech, 2012.
NAGI, Jawad, et al. Max-pooling convolutional neural networks for vision-based hand gesture recognition. En Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on. IEEE, 2011. p. 342-347.
BROEKENS, Joost, et al. Assistive social robots in elderly care: a review. Gerontechnology, 2009, vol. 8, no 2, p. 94-103.
ORIENT-LÓPEZ, F., et al. Tratamiento neurorrehabilitador de la esclerosis lateral amiotrófica. Rev Neurol, 2006, vol. 43, no 9, p. 549-55.
Richardson Products Incorporated, Meal Buddy Robotic Assistive Feeder, 2017. [Online]. Disponible en: http://www.richardsonproducts.com/mealbuddy.html
Ministerios de Salud y Protección Social, Sala situacional de las Personas con Discapacidad (PCD), 2017, Recuperado de: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/PES/presentacion-sala-situacional-discapacidad-2017.pdf
PARODI, José Francisco, et al. Factores de Riesgo Asociados al Estrés Del Cuidador Del Paciente Adulto Mayor. Rev Aso Colomb Gerontol Geriatr, 2011, vol. 25, no 2, p. 1503-1514.
PAZ RODRÍGUEZ, F.; ANDRADE PALOS, P.; LLANOS DEL PILAR, A. M. Consecuencias emocionales del cuidado del paciente con esclerosis lateral amiotrófica. Rev Neurol, 2005, vol. 40, no 8, p. 459-64.
ABDEL-ZAHER, Ahmed M.; ELDEIB, Ayman M. Breast cancer classification using deep belief networks. Expert Systems with Applications, 2016, vol. 46, p. 139-144.
ZHENG, Wei-Long, et al. EEG-based emotion classification using deep belief networks. En Multimedia and Expo (ICME), 2014 IEEE International Conference on. IEEE, 2014. p. 1-6.
OLATOMIWA, Lanre, et al. A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy, 2015, vol. 115, p. 632-644.
HONG, Haoyuan, et al. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena, 2015, vol. 133, p. 266-281.
RUBIANO, A., et al. Elbow flexion and extension identification using surface electromyography signals. En Signal Processing Conference (EUSIPCO), 2015 23rd European. IEEE, 2015. p. 644-648.
SERCU, Tom, et al. Very deep multilingual convolutional neural networks for LVCSR. En Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE, 2016. p. 4955-4959.
ARENAS, Javier Orlando Pinzón; MURILLO, Paula Catalina Useche; MORENO, Robinson Jiménez. Convolutional neural network architecture for hand gesture recognition. En Electronics, Electrical Engineering and Computing (INTERCON), 2017 IEEE XXIV International Conference on. IEEE, 2017. p. 1-4.
BARROS, Pablo, et al. A multichannel convolutional neural network for hand posture recognition. En International Conference on Artificial Neural Networks. Springer, Cham, 2014. p. 403-410.
PARKHI, Omkar M., et al. Deep Face Recognition. En BMVC. 2015. p. 6.
TAJBAKHSH, Nima, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning?. IEEE transactions on medical imaging, 2016, vol. 35, no 5, p. 1299-1312.
GIRSHICK, Ross, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. En Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. p. 580-587.
ARENAS, Javier O. Pinzón; MORENO, Robinson Jiménez; MURILLO, Paula C. Useche. Hand Gesture Recognition by Means of Region-Based Convolutional Neural Networks. Contemporary Engineering Sciences, 2017, vol. 10, no 27, p. 1329-1342.
REN, Shaoqing, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 2017, vol. 39, no 6, p. 1137-1149.
HE, Kaiming, et al. Mask r-cnn. En Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017. p. 2980-2988.
GUBBI, Jayavardhana, et al. Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 2013, vol. 29, no 7, p. 1645-1660.
FERRÚS, Rafael Mateo; SOMONTE, Manuel Domínguez. Design in robotics based in the voice of the customer of household robots. Robotics and Autonomous Systems, 2016, vol. 79, p. 99-107.
SoftBank Robotics, Who is NAO?, 2017. [Online]. Disponible en: https://www.ald.softbankrobotics.com/en/robots/nao
MARTENS, Christian; PRENZEL, Oliver; GRÄSER, Axel. The rehabilitation robots FRIEND-I & II: Daily life independency through semi-autonomous task-execution. En Rehabilitation robotics. InTech, 2007.
KITTMANN, Ralf, et al. Let me introduce myself: I am Care-O-bot 4, a gentleman robot. Mensch und computer 2015–proceedings, 2015.
Eclipse Automation, Obi, 2017. [Online]. Disponible en: https://meetobi.com/
SECOM, My Spoon: Meal-assistance Robot, 2017. [Online]. Disponible en: https://www.secom.co.jp/english/myspoon/index.html
Camanio Care AB, Bestic: increase your mealtime independence, 2017. [Online]. Disponible en: http://www.camanio.com/us/products/bestic/
PARK, Daehyung, et al. “A multimodal execution monitor with anomaly classification for robot-assisted feeding”, En 2016 IEEE International Conference on Robots and Systems (IROS). 2017.
SNYDER, Wesley E.; QI, Hairong. Machine vision. Cambridge University Press, 2010.
PÉREZ, Luis, et al. Robot guidance using machine vision techniques in industrial environments: A comparative review. Sensors, 2016, vol. 16, no 3, p. 335.
DAVIES, E. Roy. Machine vision: theory, algorithms, practicalities. Elsevier, 2004.
CUBERO, Sergio, et al. Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest—a review. Food and Bioprocess Technology, 2016, vol. 9, no 10, p. 1623-1639.
RAUTARAY, Siddharth S.; AGRAWAL, Anupam. Vision based hand gesture recognition for human computer interaction: a survey. Artificial Intelligence Review, 2015, vol. 43, no 1, p. 1-54.
HE, Kaiming, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. En Proceedings of the IEEE international conference on computer vision. 2015. p. 1026-1034.
CIRESAN, Dan C., et al. Flexible, high performance convolutional neural networks for image classification. En IJCAI Proceedings-International Joint Conference on Artificial Intelligence. 2011. p. 1237.
JIN, Jonghoon; DUNDAR, Aysegul; CULURCIELLO, Eugenio. Flattened convolutional neural networks for feedforward acceleration. arXiv preprint arXiv:1412.5474, 2014.
SRIVASTAVA, Nitish, et al. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 2014, vol. 15, no 1, p. 1929-1958.
IOFFE, Sergey; SZEGEDY, Christian. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. En International Conference on Machine Learning. 2015. p. 448-456.
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BADRINARAYANAN, Vijay; KENDALL, Alex; CIPOLLA, Roberto. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, vol. 39, no 12, p. 2481-2495.
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ZITNICK, C. Lawrence; DOLLÁR, Piotr. Edge boxes: Locating object proposals from edges. En European conference on computer vision. Springer, Cham, 2014. p. 391-405.
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PALOMARES, Fernando Giménez; SERRÁ, Juan Antonio Monsoriu; MARTÍNEZ, Elena Alemany. Aplicación de la convolución de matrices al filtrado de imágenes. Modelling in Science Education and Learning, 2016, vol. 9, no 1, p. 97-108.
Repositorio UMNG
Universidad Militar Nueva Granada
instacron:Universidad Militar Nueva Granada
DUBEY, Sanjay, et al. An FPGA based service Robot for floor cleaning with autonomous navigation. En Research Advances in Integrated Navigation Systems (RAINS), International Conference on. IEEE, 2016. p. 1-6.
VELANDIA, Natalie Segura; BELENO, Ruben D. Hernandez; MORENO, Robinson Jimenez. Applications of Deep Neural Networks. International Journal of Systems Signal Control and Engineering Applications, 2017, vol. 10, no 1.
WHITLEY, Darrell. A genetic algorithm tutorial. Statistics and computing, 1994, vol. 4, no 2, p. 65-85.
KARABOGA, Dervis; AKAY, Bahriye. A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 2009, vol. 214, no 1, p. 108-132.
ABE, Shigeo. Support vector machines for pattern classification. London: Springer, 2005.
HOPFIELD, John J. Artificial neural networks. IEEE Circuits and Devices Magazine, 1988, vol. 4, no 5, p. 3-10.
LECUN, Yann; BENGIO, Yoshua; HINTON, Geoffrey. Deep learning. Nature, 2015, vol. 521, no 7553, p. 436.
HINTON, Geoffrey E.; OSINDERO, Simon; TEH, Yee-Whye. A fast learning algorithm for deep belief nets. Neural computation, 2006, vol. 18, no 7, p. 1527-1554.
PASCANU, Razvan, et al. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026, 2013.
ZEILER, Matthew D.; FERGUS, Rob. Visualizing and understanding convolutional networks. En European conference on computer vision. Springer, Cham, 2014. p. 818-833.
LECUN, Yann, et al. Backpropagation applied to handwritten zip code recognition. Neural computation, 1989, vol. 1, no 4, p. 541-551.
KRIZHEVSKY, Alex; SUTSKEVER, Ilya; HINTON, Geoffrey E. Imagenet classification with deep convolutional neural networks. En Advances in neural information processing systems. 2012. p. 1097-1105.
KIM, Kyung-Min, et al. Pororobot: A deep learning robot that plays video Q&A games. En Proceedings of AAAI Fall Symposium on AI for HRI. 2015.
TAPUS, Adriana; MATARIC, Maja J.; SCASSELLATI, Brian. Socially assistive robotics [grand challenges of robotics]. IEEE Robotics & Automation Magazine, 2007, vol. 14, no 1, p. 35-42.
FEIL-SEIFER, David; MATARIC, Maja J. Defining socially assistive robotics. En Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on. IEEE, 2005. p. 465-468.
SONG, Won-Kyung; KIM, Jongbae. Novel assistive robot for self-feeding. En Robotic Systems-Applications, Control and Programming. InTech, 2012.
NAGI, Jawad, et al. Max-pooling convolutional neural networks for vision-based hand gesture recognition. En Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on. IEEE, 2011. p. 342-347.
BROEKENS, Joost, et al. Assistive social robots in elderly care: a review. Gerontechnology, 2009, vol. 8, no 2, p. 94-103.
ORIENT-LÓPEZ, F., et al. Tratamiento neurorrehabilitador de la esclerosis lateral amiotrófica. Rev Neurol, 2006, vol. 43, no 9, p. 549-55.
Richardson Products Incorporated, Meal Buddy Robotic Assistive Feeder, 2017. [Online]. Disponible en: http://www.richardsonproducts.com/mealbuddy.html
Ministerios de Salud y Protección Social, Sala situacional de las Personas con Discapacidad (PCD), 2017, Recuperado de: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/PES/presentacion-sala-situacional-discapacidad-2017.pdf
PARODI, José Francisco, et al. Factores de Riesgo Asociados al Estrés Del Cuidador Del Paciente Adulto Mayor. Rev Aso Colomb Gerontol Geriatr, 2011, vol. 25, no 2, p. 1503-1514.
PAZ RODRÍGUEZ, F.; ANDRADE PALOS, P.; LLANOS DEL PILAR, A. M. Consecuencias emocionales del cuidado del paciente con esclerosis lateral amiotrófica. Rev Neurol, 2005, vol. 40, no 8, p. 459-64.
ABDEL-ZAHER, Ahmed M.; ELDEIB, Ayman M. Breast cancer classification using deep belief networks. Expert Systems with Applications, 2016, vol. 46, p. 139-144.
ZHENG, Wei-Long, et al. EEG-based emotion classification using deep belief networks. En Multimedia and Expo (ICME), 2014 IEEE International Conference on. IEEE, 2014. p. 1-6.
OLATOMIWA, Lanre, et al. A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy, 2015, vol. 115, p. 632-644.
HONG, Haoyuan, et al. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena, 2015, vol. 133, p. 266-281.
RUBIANO, A., et al. Elbow flexion and extension identification using surface electromyography signals. En Signal Processing Conference (EUSIPCO), 2015 23rd European. IEEE, 2015. p. 644-648.
SERCU, Tom, et al. Very deep multilingual convolutional neural networks for LVCSR. En Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE, 2016. p. 4955-4959.
ARENAS, Javier Orlando Pinzón; MURILLO, Paula Catalina Useche; MORENO, Robinson Jiménez. Convolutional neural network architecture for hand gesture recognition. En Electronics, Electrical Engineering and Computing (INTERCON), 2017 IEEE XXIV International Conference on. IEEE, 2017. p. 1-4.
BARROS, Pablo, et al. A multichannel convolutional neural network for hand posture recognition. En International Conference on Artificial Neural Networks. Springer, Cham, 2014. p. 403-410.
PARKHI, Omkar M., et al. Deep Face Recognition. En BMVC. 2015. p. 6.
TAJBAKHSH, Nima, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning?. IEEE transactions on medical imaging, 2016, vol. 35, no 5, p. 1299-1312.
GIRSHICK, Ross, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. En Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. p. 580-587.
ARENAS, Javier O. Pinzón; MORENO, Robinson Jiménez; MURILLO, Paula C. Useche. Hand Gesture Recognition by Means of Region-Based Convolutional Neural Networks. Contemporary Engineering Sciences, 2017, vol. 10, no 27, p. 1329-1342.
REN, Shaoqing, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 2017, vol. 39, no 6, p. 1137-1149.
HE, Kaiming, et al. Mask r-cnn. En Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017. p. 2980-2988.
GUBBI, Jayavardhana, et al. Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 2013, vol. 29, no 7, p. 1645-1660.
FERRÚS, Rafael Mateo; SOMONTE, Manuel Domínguez. Design in robotics based in the voice of the customer of household robots. Robotics and Autonomous Systems, 2016, vol. 79, p. 99-107.
SoftBank Robotics, Who is NAO?, 2017. [Online]. Disponible en: https://www.ald.softbankrobotics.com/en/robots/nao
MARTENS, Christian; PRENZEL, Oliver; GRÄSER, Axel. The rehabilitation robots FRIEND-I & II: Daily life independency through semi-autonomous task-execution. En Rehabilitation robotics. InTech, 2007.
KITTMANN, Ralf, et al. Let me introduce myself: I am Care-O-bot 4, a gentleman robot. Mensch und computer 2015–proceedings, 2015.
Eclipse Automation, Obi, 2017. [Online]. Disponible en: https://meetobi.com/
SECOM, My Spoon: Meal-assistance Robot, 2017. [Online]. Disponible en: https://www.secom.co.jp/english/myspoon/index.html
Camanio Care AB, Bestic: increase your mealtime independence, 2017. [Online]. Disponible en: http://www.camanio.com/us/products/bestic/
PARK, Daehyung, et al. “A multimodal execution monitor with anomaly classification for robot-assisted feeding”, En 2016 IEEE International Conference on Robots and Systems (IROS). 2017.
SNYDER, Wesley E.; QI, Hairong. Machine vision. Cambridge University Press, 2010.
PÉREZ, Luis, et al. Robot guidance using machine vision techniques in industrial environments: A comparative review. Sensors, 2016, vol. 16, no 3, p. 335.
DAVIES, E. Roy. Machine vision: theory, algorithms, practicalities. Elsevier, 2004.
CUBERO, Sergio, et al. Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest—a review. Food and Bioprocess Technology, 2016, vol. 9, no 10, p. 1623-1639.
RAUTARAY, Siddharth S.; AGRAWAL, Anupam. Vision based hand gesture recognition for human computer interaction: a survey. Artificial Intelligence Review, 2015, vol. 43, no 1, p. 1-54.
HE, Kaiming, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. En Proceedings of the IEEE international conference on computer vision. 2015. p. 1026-1034.
CIRESAN, Dan C., et al. Flexible, high performance convolutional neural networks for image classification. En IJCAI Proceedings-International Joint Conference on Artificial Intelligence. 2011. p. 1237.
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Repositorio UMNG
Universidad Militar Nueva Granada
instacron:Universidad Militar Nueva Granada
El presente trabajo esboza la implementación de un algoritmo para el control de un robot asistencial, el cual se enfoca en la alimentación asistida. El algoritmo aplicado al robot tiene 3 pilares fundamentales para su funcionamiento: detección de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::e187a721796af1481e88e0c17a831be9
https://hdl.handle.net/10654/32762
https://hdl.handle.net/10654/32762