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Articles by Robinson Jimenez Moreno
Total Records ( 3 ) for Robinson Jimenez Moreno
  Javier O. Pinzon Arenas , Ruben D. Hernandez Beleno and Robinson Jimenez Moreno
  This study presents the training and validation of a deep convolutional neural network architecture used for a human-robot interaction. Two different datasets of images were employed with the aim of recognizing 2 kinds of hand gestures which are "closed" and "open" and control a robotic arm with these gestures. To choose the best training in the network, different behavioral parameters such as training accuracy and loss were evaluated to obtain the best training epoch and validation parameters such as validation accuracy and internal behavior of the network through the activations of the convolution layers. Once the trained network is chosen, camera tests and interaction with a robotic arm are performed, evaluating the interaction between the user and the actions of the robot through the network.
  Javier O. Pinzon Arenas , Robinson Jimenez Moreno and Ruben D. Hernandez Beleno
  This study presents the implementation of a convolutional neural network focused on the recognition of hand gestures for this case 3 specific types of gestures using the EMG signals as input which were acquired through the Myo armband device and processed by means of a characteristic map extraction technique which is the power spectral density. The development of this work is divided into 2 phases where the first consists of the acquisition and processing of the electromyographic signals of different users with different arm thickness from which 2 databases were built and the second phase describes the architecture of the convolutional neural network to be used and the training that was performed with each database independently, obtaining two trained networks. Finally, two types of tests are performed, a validation test in which the accuracy of the two trained networks is verified where a accuracy rate of 91.7 and 92.5% was achieved and a real-time behavioral test where the two networks responded adequately, meaning that the use of convolutional neural networks for the recognition of hand gestures by means of electromyographic signals can reach high ranges of accuracy, even greater than 90%.
  Natalie Segura Velandia Ruben D. Hernandez Beleno and Robinson Jimenez Moreno
  In this study it is present a review of artificial intelligence algorithms applied to robotic agents, emphasizing learning from Convolutional Neural Networks (CNN). For the construction of this review, a bibliographical research was carried out in the databases of scientific articles, research journals and technological advances where the most important works are described which implement different methods in a variety applications such as autonomous vehicles, robots for space exploration and robotic manipulators in the planning of trajectories making use of CNN. Artificial Intelligence (AI) is defined as the technique in which there are developed algorithms capable of reasoning, learning and making decisions based on experience. This technique has achieved its interaction with robotic agents getting to create what is now known as intelligent robots, capable of solving and performing multiple tasks of daily life but like all developing science it is still possible to optimize and improve their algorithms to achieve human precision, referred to the manner and efficiency with which a human develops different types of activities. As a result, some of the applications that are available to people will be presented in order to highlight the extent to which this synergy of technologies has reached its development.
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