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 - 10 of 149
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Predicting climate conditions using Internet-of- Things and artificial hydrocarbon networks

2017 , Ponce, Hiram , Gutiérrez, Sebastián , Montoya Pacheco, Alejandro

The prediction and understanding of environmental conditions is of great importance to prevent and analyze changes in environment, supporting meteorological based sectors, such as agriculture. In that sense, this paper presents an Internet of Things (IoT) system for predicting climate conditions, i.e. temperature, using artificial intelligence by means of a supervised learning method, the artificial hydrocarbon networks model. It allows predicting the temperature of remote locations using information from a web service comparing it with a field temperature sensor. Experimental results of the supervised learning model are presented in two modes: offline training to detect the suitable parameters of the model and testing to validate the model with new data retrieval from the web service. Preliminary results conclude that artificial hydrocarbon networks model predicts remote temperature with mean error of 0.05°c in testing mode. © 2018 IMEKO-International Measurement Federation Secretariat. All rights reserved.

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A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks

2020 , Brieva, Jorge , Ponce, Hiram , Moya-Albor, Ernesto

The monitoring of respiratory rate is a relevant factor in medical applications and day-to-day activities. Contact sensors have been used mostly as a direct solution and they have shown their effectiveness, but with some disadvantages for example in vulnerable skins such as burns patients. For this reason, contactless monitoring systems are gaining increasing attention for respiratory detection. In this paper, we present a new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN). The system tracks chest movements of the subject using two strategies: using a manually selected ROI and without the selection of a ROI in the image frame. The system is based on the classifications of the frames as an inhalation or exhalation using CNN. Our proposal has been tested on 10 healthy subjects in different positions. To compare performance of methods to detect respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for the automatic strategy is 3.28± 3.33% with and agreement with respect of the reference of 98%. © 2020 by the authors.

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A Robust Control Scheme for Renewable-Based Distributed Generators Using Artificial Hydrocarbon Networks

2019 , Rosales, Antonio , Ponce, Pedro , Ponce, Hiram , Molina, Arturo

Distributed generators (DGs) based on renewable energy systems such as wind turbines, solar panels, and storage systems, are key in transforming the current electric grid into a green and sustainable network. These DGs are called inverter-interfaced systems because they are integrated into the grid through power converters. However, inverter-interfaced systems lack inertia, deteriorating the stability of the grid as frequency and voltage oscillations emerge. Additionally, when DGs are connected to the grid, its robustness against unbalanced conditions must to be ensured. This paper presents a robust control scheme for power regulation in DGs, which includes inertia and operates under unbalanced conditions. The proposed scheme integrates a robust control algorithm to ensured power regulation, despite unbalanced voltages. The control algorithm is an artificial hydrocarbon network controller, which is a chemically-inspired technique, based on carbon networks, that provides stability, robustness, and accuracy. The robustness and stability of the proposed control scheme are tested using Lyapunov techniques. Simulation, considering one- and three-phase voltage sags, is executed to validate the performance of the control scheme.

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Mechatronics Teaching through Virtual Platforms for Home Confinement due to COVID-19

2020 , Ruiz Delgado, Luis David , Muro Álvarez, Santiago del , Gutiérrez, Sebastián , Ponce, Hiram

The pandemic that has been unleashed all over the world has forced us to change our way of life, this includes to avoid face-to-face classes. Therefore, an important question arises: how to carry out the online classes of a subject in which the main focus is on developing projects such as the Mechatronics course? This document presents the platforms used in the online course that include: Factory I/O, Overleaf, Tinkercad and FluidSIM, for replacing face-to-face practices. The course development, its characteristics and even the practices that have been done during the online course on the platforms are explained. The main idea is to show that it is possible to substitute the face-to-face practices, using appropriate online tools for each topic within the course. The use of online platforms and simulation software allows obtaining new skills of teaching regarding laboratory subjects. In general, online platforms allow having an alternative and new tools to transform the most of the course to the online mode. © 2020 IEEE.

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Artificial Hydrocarbon Networks for Online Sales Prediction

2015 , Ponce, Hiram , Miralles-Pechuán, Luis , Martinez-Villaseñor, Lourdes

Online retail sales have been growing worldwide in the last decade. In order to cope with this high dynamicity and market share competition, online retail sales prediction and online advertising have become very important to answer questions of pricing decisions, advertising responsiveness, and product demand. To make adequate investment in products and channels it is necessary to have a model that relates certain features of the product with the number of sales that will occur in the future. In this paper we describe a comparative analysis of machine learning techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). This method is a new type of machine learning that have proved to adapt very well to a wide spectrum of problems of regression and classification. Thus, we use artificial hydrocarbon networks for predicting the number of online sales, and then we compare their performance with other ten well-known methods of machine learning regression, obtaining promising results.

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A novel robust liquid level controller for coupled-tanks systems using artificial hydrocarbon networks

2015 , Ponce, Hiram , Ponce, Pedro , Bastida, Héctor , Molina, Arturo

This paper proposes a robust liquid-level controller for coupled-tanks systems when dealing with variable discharge rates at the secondary tank, based on a hybrid fuzzy inference system that uses artificial hydrocarbon networks at the defuzzification step, so-called fuzzy-molecular control. The design methodology of the proposed controller is presented and discussed. In addition, a case study was run over the CE105 TecQuipment coupled-tanks system in order to implement and validate the fuzzy-molecular controller proposed in that work. A comparative evaluation with the proposed controller, a conventional PID controller specifically designed for this system and a QFT robust controller, was done. Also, a performance evaluation in terms of robustness, reference-tracking in a fixed operating point and reference-tracking in a variable operating point on-the-fly was run and analyzed. Results conclude that the proposed fuzzy-molecular controller deals with uncertainty and noise, can handle dynamics in operating point, a model of the plant is not required, and it is easy and simple to implement in comparison with other controllers in literature. To this end, the proposed fuzzy-molecular liquid-level controller inherits characteristics from fuzzy controllers and artificial hydrocarbon networks in order to implement an advanced robust and intelligent control system.

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Bio-inspired Training Algorithms for Artificial Hydrocarbon Networks: A Comparative Study

2014 , Ponce, Hiram

Artificial hydrocarbon networks (AHN) is a supervised learning algorithm inspired on chemical organic compounds. Its first implementation occupied the well-known least squares estimates (LSE) as part of the training algorithm. Unsurprisingly, AHN cannot converge to suitable solutions when dealing with high dimensional data, falling into the curse of dimensionality. In that sense, this paper proposes two hybrid training algorithms for AHN using bio-inspired algorithms, i.e. Simulated annealing and particle swarm optimization, and compares them against the LSE-based method. Experimental results show that these bio-inspired algorithms improve the performance of artificial hydrocarbon networks, concluding that these hybrid algorithms can be used as alternative learning algorithms for high dimensional data.

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The Power of Natural Inspiration in Control Systems

2015 , Ponce, Hiram , Ayala-Solares, José Roberto

Throughout history, nature has always been an inspiration for mankind. It is not an exaggeration to say that almost every human invention, from engineering to social sciences, has been an attempt to replicate nature. In fact, nature continues to play an important roll in different human activities. From a scientific perspective, nature-inspired methods have proven to be an efficient tool for tackling real-life problems that are difficult to solve because of their high complexity or the limitation of resources to analyze them. The core idea is the fact that several natural phenomena, from simple to complex, always try to optimize certain parameters. Thus, this chapter gives an overview of nature-inspired methods from computational point of view, and summarizes key contributions of this book that focuses on methods that can simulate natural phenomena using computers, and the benefits of applying this methodology to the analysis and design of engineering control systems. © Springer International Publishing Switzerland 2016.

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Design of an automated hive for bee proliferation and crop betterment

2017 , Lopez Tagle, Eduardo , Siqueiros, Eduardo , Ponce, Hiram

Apiculture has been a method for harvesting honey and helping crop proliferation, nonetheless late climate changes have affected bee population. Having an automated hive as a tool for beekeepers would help to increase and protect current bee population guaranteeing crop proliferation needed for human consumption. In this paper, we propose an electronic beehive that recollect honey automatically, regulate the temperature of the inner hive and distribute food for bees. Preliminary results showed the control and automation of the proposed system as well as the prototype implementation, concluding that the automated beehive might be able to improve bee proliferation and crop betterment in an automated way. © 2017 IEEE.

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Editorial: Artificial intelligence in brain-computer interfaces and neuroimaging for neuromodulation and neurofeedback

2022 , Ponce, Hiram , Yinong, Chen , Martinez-Villaseñor, Lourdes

Neuromodulation and neurofeedback are two alternative non-pharmacological ways of treating neurological related diseases and disorders (Grazzi et al., 2021; Hamed et al., 2022). Neuromodulation refers to as the modulation of brain function via the application of weak direct current (Lewis et al., 2016). Neurofeedback is a psychophysiological procedure that provides models of neural activity to subjects aiming to control them online (Marzbani et al., 2016). Both alternatives have been successfully applied in a variety of neurological conditions including Parkinson's disease, chronic pain, epilepsy, depression, essential tremor, among many others (Tsatali et al., 2019; Baptista et al., 2020; Hamed et al., 2022). Typical challenges in these types of treatment are related to the way of collecting data, the improvement in the efficiency of the methods, the interpretability of feedback signals, to name a few (Johnson et al., 2013; Lewis et al., 2016; Marzbani et al., 2016; Papo, 2019). © 2023 Frontiers Media S.A. All rights reserved

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