Zobrazeno 1 - 10
of 15
pro vyhledávání: '"José Carlos Ortíz-Bayliss"'
Autor:
José Ricardo Abreu-Pederzini, Guillermo Arturo Martínez-Mascorro, José Carlos Ortíz-Bayliss, Hugo Terashima-Marín
Publikováno v:
Applied Sciences, Vol 11, Iss 20, p 9374 (2021)
Artificial neural networks are efficient learning algorithms that are considered to be universal approximators for solving numerous real-world problems in areas such as computer vision, language processing, or reinforcement learning. To approximate a
Externí odkaz:
https://doaj.org/article/4e28805c66e44100ae34f9cde6417b66
Autor:
Gerardo Humberto Valencia-Rivera, Ivan Amaya, Jorge M. Cruz-Duarte, José Carlos Ortíz-Bayliss, Juan Gabriel Avina-Cervantes
Publikováno v:
Energies, Vol 14, Iss 21, p 6909 (2021)
Renewable energy sources are an environmentally attractive idea, but they require a proper control scheme to guarantee optimal operation. In this work, we tune different controllers for an Interleaved Boost Converter (IBC) powered by a photovoltaic a
Externí odkaz:
https://doaj.org/article/76e2ef084f9548f1952b55cbf1d9ac48
Autor:
Gerardo Humberto Valencia-Rivera, Ivan Amaya, Jorge M. Cruz-Duarte, José Carlos Ortiz-Bayliss, Guillermo Tapia-Tinoco, Juan Gabriel Avina-Cervantes
Publikováno v:
Heliyon, Vol 10, Iss 13, Pp e33019- (2024)
Microgrids (MGs) based on renewable energies have emerged as a proficient strategy for tackling power quality issues in conventional distribution networks. Nonetheless, MG systems require a suitable control scheme to supply energy optimally towards t
Externí odkaz:
https://doaj.org/article/680b4465aaf748389b6b5af96bf0d770
Autor:
Gabriel Gonzalez-Sahagun, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Jorge M. Cruz-Duarte
Publikováno v:
IEEE Access, Vol 12, Pp 136131-136147 (2024)
Deep learning models have gained popularity in the last decade for computer vision tasks. Although these models are widely used, they process data in cloud services due to requiring large amounts of memory unavailable on consumer devices. Multiple te
Externí odkaz:
https://doaj.org/article/dfcac66038704835858a82b8a2f6170e
Autor:
Alonso Javier Amado-Garfias, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Hugo Terashima-Marin
Publikováno v:
IEEE Access, Vol 12, Pp 111818-111831 (2024)
Much research aims to enhance weapon detection by applying different techniques to object detection models. However, little research focuses on identifying armed people through real-time surveillance cameras. The proposed solution involves the develo
Externí odkaz:
https://doaj.org/article/661dbc4333fa43699a6bf4157283d5ee
Autor:
Gabriel Gonzalez-Sahagun, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Jorge M. Cruz-Duarte
Publikováno v:
IEEE Access, Vol 12, Pp 51100-51114 (2024)
Over the years, researchers have proposed multiple approaches to reduce the number of parameters Deep Learning models have. Due to the complexity of compressing models, some authors have opted to train Reinforcement Learning agents that learn how to
Externí odkaz:
https://doaj.org/article/1429d0ceed844ca6820ced92ff25d756
Autor:
Maria Torcoroma Benavides-Robles, Gerardo Humberto Valencia-Rivera, Jorge M. Cruz-Duarte, Ivan Amaya, Jose Carlos Ortiz-Bayliss
Publikováno v:
IEEE Access, Vol 12, Pp 16767-16782 (2024)
The Robotic Mobile Fulfillment System (RMFS) is a method for handling products, in which a Line Follower Robot (LFR) transports products to a human workstation for packing. In this systematic review, we delve into the current state of RMFS research u
Externí odkaz:
https://doaj.org/article/845c746edb2d436da5f50b40aebb1dd0
Autor:
Daniel F. Zambrano-Gutierrez, Jorge Mario Cruz-Duarte, Juan Gabriel Avina-Cervantes, Jose Carlos Ortiz-Bayliss, Jesus Joaquin Yanez-Borjas, Ivan Amaya
Publikováno v:
IEEE Access, Vol 11, Pp 7262-7276 (2023)
It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it i
Externí odkaz:
https://doaj.org/article/a6de439d2e23406886c939ff4e7837d6
Publikováno v:
IEEE Access, Vol 10, Pp 43981-44007 (2022)
Hyper-heuristics (HHs) stand as a relatively recent approach to solving optimization problems. There are different kinds of HHs. One of them deals with how low-level heuristics must be combined to deliver an improved solution to a set of problem inst
Externí odkaz:
https://doaj.org/article/19f23461c8c243ee91670611885a8177
Autor:
Enrique Díaz de León-Hicks, Santiago Enrique Conant-Pablos, José Carlos Ortiz-Bayliss, Hugo Terashima-Marín
Publikováno v:
Applied Sciences, Vol 13, Iss 7, p 4601 (2023)
In the algorithm selection problem, where the task is to identify the most suitable solving technique for a particular situation, most methods used as performance mapping mechanisms have been relatively simple models such as logistic regression or ne
Externí odkaz:
https://doaj.org/article/eff56cb8dd7040d287d6fbc9828e0ca1