Ponce, Hiram
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Preferred name
Ponce, Hiram
Official Name
Ponce Espinosa, Hiram Eredín
Alternative Name
hponce
Main Affiliation
ORCID
Scopus Author ID
54911890000
Researcher ID
K-7593-2019

Research Output
Now showing 1 - 5 of 5
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Electric wheelchair module: Converting a mechanical to an electric wheelchair

2017 , Galván, Emiliano , González Mora, José Guillermo , Hernández Ortega, Guillermo , Mañón, Santiago , Ponce, Hiram

There is a growing need for transportation, either inside a house or office, as well as in the streets and other public spaces. This constant need is a disadvantage for anyone with some kind of disability, specially those that suffer motor disabilities. Electric wheelchairs are part of the technological solutions to this demand. However, their cost are very high in constrast to mechanical wheelchairs. In that sense, this paper aims to present a new module device that can be used for converting a mechanical wheelchair to an electrical one. The proposed device was designed for simplicity in installation, high benefit-cost ratio and easiness in control movement. Preliminary results showed the implementation of the proposal in a functional prototype. © 2017 IEEE.

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A Reinforcement Learning Method for Continuous Domains Using Artificial Hydrocarbon Networks

2018 , Ponce, Hiram , González Mora, José Guillermo , Martinez-Villaseñor, Lourdes

Reinforcement learning in continuous states and actions has been limitedly studied in ocassions given difficulties in the determination of the transition function, lack of performance in continuous-to-discrete relaxation problems, among others. For instance, real-world problems, e.g. Fobotics, require these methods for learning complex tasks. Thus, in this paper, we propose a method for reinforcement learning with continuous states and actions using a model-based approach learned with artificial hydrocarbon networks (AHN). The proposed method considers modeling the dynamics of the continuous task with the supervised AHN method. Initial random rollouts and posterior data collection from policy evaluation improve the training of the AHN-based dynamics model. Preliminary results over the well-known mountain car task showed that artificial hydrocarbon networks can contribute to model-based approaches in continuous RL problems in both estimation efficiency (0.0012 in root mean squared-error) and sub-optimal policy convergence (reached in 357 steps), in just 5 trials over a parameter space θin R86. Data from experimental results are available at: http://sites.google.com/up.edu.mx/reinforcement-learning/ ©2018 IEEE.

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Development of Fast and Reliable Nature-Inspired Computing for Supervised Learning in High-Dimensional Data

2019 , Ponce, Hiram , Souza, Paulo , Morales, Elizabeth , González Mora, José Guillermo

Machine learning and data mining tasks in big data involve different nature of inputs that typically exhibit high dimensionality, e.g. more than 1,000 features, far from current acceptable scales computing in one machine. In many different domains, data have highly nonlinear representations that nature-inspired models can easily capture, outperforming simple models. But, the usage of these approaches in high-dimensional data are computationally costly. Recently, artificial hydrocarbon networks (AHN)—a supervised learning method inspired on organic chemical structures and mechanisms—have shown improvements in predictive power and interpretability in contrast with other well-known machine learning models, such as neural networks and random forests. However, AHN are very time-consuming that are not able to deal with big data until now. In this chapter, we present a fast and reliable nature-inspired training method for AHN, so they can handle high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined both individual encoding and objective function related to the AHN-model, and it is also implemented in parallel-computing. After benchmark performing of population-based optimization methods, grey wolf optimization (GWO) was selected. Our results demonstrate that the proposed hybrid GWO-based training method for AHN runs more than 1400x faster in high-dimensional data, without loss of predictability, yielding a fast and reliable nature-inspired machine learning model. We also present a use case in assisted living monitoring, i.e. human fall classification comprising 1,269 features from sensor signals and video recordings, with this proposed training algorithm to show its implementation and performance. We anticipate our new training algorithm to be useful in many applications like medical engineering, robotics, finance, aerospace, and others, in which big data is essential. © Springer Nature Switzerland AG 2020.

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An Implementation of a Monocular 360-Degree Vision System for Mobile Robot Navigation

2018 , Acevedo Medina, Eduardo , Beltrán, Arturo , Castellanos Canales, Mauricio , Chaverra, Luis , González Mora, José Guillermo , Sarmiento Oseguera, Manuel Antonio , Ponce, Hiram

One of the problems facing autonomous navigation is obstacle sensing and dynamic surroundings. Multi-sensor systems and omni directional vision systems have been implemented to increase the observability of robots. However, these approaches consider several drawbacks: the cost of processing multiple signals, synchronization of data collection, cost of materials and energy consumption, among others. Thus in this paper, we propose a new 360-degree vision system for mobile robot navigation using a static monocular camera. We demonstrate that using our system, it is possible to monitor the complete surroundings of the robot with a single sensor, i.e. the camera. Moreover, it can detect the position and orientation of an object from an egocentric point of view. We also present a low cost prototype of our proposal to validate it. © 2018 IEEE.

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Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data

2020 , Ponce, Hiram , De Campos Souza, Paulo V. , Junio Guimarães, Augusto , González Mora, José Guillermo

Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in comparison with other well-known machine learning models. However, AHN are very time-consuming that are not able to deal with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined individual encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a stochastic learning approach for consuming large data. We conducted three experiments with synthetic and real data sets to validate the training execution time and performance of the proposed algorithm. Experimental results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed of training more than 10,000x times in the worst case scenario. Additionally, we present two case studies in real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities classification in healthcare monitoring systems (classification problem). These case studies showed that SPE-AHN improves the state-of-the-art machine learning models in both engineering problems. We anticipate our new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering, aerospace, and others, in which large amounts of data (e.g. big data) is essential. © 2019 Elsevier Ltd.

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