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 45
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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 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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Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

2023 , Brieva, Jorge , Ponce, Hiram , Moya-Albor, Ernesto

The breathing rate monitoring is an important measure in medical applications and daily physical activities. The contact sensors have shown their effectiveness for breathing monitoring and have been mostly used as a standard reference, but with some disadvantages for example in burns patients with vulnerable skins. Contactless monitoring systems are then gaining attention for respiratory frequency detection. We propose a new non-contact technique to estimate the breathing rate based on the motion video magnification method by means of the Hermite transform and an Artificial Hydrocarbon Network (AHN). The chest movements are tracked by the system without the use of an ROI in the image video. The machine learning system classifies the frames as inhalation or exhalation using a Bayesian-optimized AHN. The method was compared using an optimized Convolutional Neural Network (CNN). This proposal has been tested on a Data-Set containing ten healthy subjects in four positions. The percentage error and the Bland–Altman analysis is used to compare the performance of the strategies estimating the breathing rate. Besides, the Bland–Altman analysis is used to search for the agreement of the estimation to the reference.The percentage error for the AHN method is (Formula presented.) with and agreement with respect of the reference of ≈99%. © 2023 by the authors.

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A non-contact heart rate estimation method using video magnification and neural networks

2020 , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Heart rate (HR) monitoring is a significant task in many medical, sports and aged care in assisted living applications, among other disciplines. In the literature, several works have reported effectiveness in addressing the measurement of HR using contact sensors such as adhesive or dry electro-conductive electrodes. However, there are several issues associated with contact sensors like portability problems, skin irritation, discomfort and body movement constraints. In this regard, this paper presents a non-contact HR estimation method using vision-based methods and neural networks. This work uses a bio-inspired Eulerian motion magnification approach to highlight the blood irrigation process of the cardiac pulse, which is later inputted to a feed-forward neural network trained to estimate the HR. For experimental analysis, we compare two magnification procedures, based on Gaussian and Hermite decomposition, over video recordings collected from the wrists of five subjects. Results show that the Hermite-based magnification method is robust under noise analysis (4.24 bpm of root mean squared-error in the worst case scenario). Furthermore, our results demonstrate that the Hermite-based method is competitive in the state-of-the-art (1.86 bpm in average of root mean squared-error) and can be implemented using a single camera for contactless HR estimation. ©2020 IEEE Instrumentation and Measurement Magazine, Institute of Electrical and Electronics Engineers Inc.

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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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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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Challenges and trends in multimodal fall detection for healthcare : Preface

2020 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Brieva, Jorge

This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples. This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others. ©2020 Springer Nature Switzerland AG.

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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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Design of a Soft Gripper Using Genetic Algorithms

2021 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Mayorga-Acosta, Carlos

In this paper, we present an artificial intelligence-assisted design of a soft robotic gripper. First, we formulate the design of the soft gripper as an optimization problem. Then, we design and configure a genetic algorithm (GA) method to solve the problem under design constraints. Lastly, we implement the whole system in co-simulation between the GA and a computer-aided design software that evaluates the candidate solutions using finite element analysis. A network-attached storage server connecting multiple nodes runs the GA method in parallel, to accelerate the process. After experimentation, we present simulation results to validate our approach. © 2021 Instituto Politécnico Nacional. All rights reserved.

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UP-fall detection dataset : a multimodal approach

2019 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Brieva, Jorge , Moya-Albor, Ernesto , Nuñez-Martínez, José , Peñafort Asturiano, Carlos J.

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community. ©2019 NLM (Medline).

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