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 11
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Publication

Design and Equilibrium Control of a Force-Balanced One-Leg Mechanism

2018 , Ponce, Hiram , Acevedo, Mario

The problem of equilibrium is critical for planning, control, and analysis of legged robot. Control algorithms for legged robots use the equilibrium criteria to avoid falls. The computational efficiency of the equilibrium tests is critical. To comply with this it is necessary to calculate the horizontal momentum rotation for every moment. For arbitrary contact geometries, more complex and computationally-expensive techniques are required. On the other hand designing equilibrium controllers for legged robots is a challenging problem. Nonlinear or more complex control systems have to be designed, complicating the computational cost and demanding robust actuators. In this paper, we propose a force-balanced mechanism as a building element for the synthesis of legged robots that can be easily balance controlled. The mechanism has two degrees of freedom, in opposition to the more traditional one degree of freedom linkages generally used as legs in robotics. This facilitates the efficient use of the “projection of the center of mass” criterion with the aid of a counter rotating inertia, reducing the number of calculations required by the control algorithm. Different experiments to balance the mechanism and to track unstable set-point positions have been done. Proportional error controllers with different strategies as well as learning approaches, based on an artificial intelligence method namely artificial hydrocarbon networks, have been used. Dynamic simulations results are reported. Videos of experiments will be available at: https://sites.google.com/up.edu.mx/smart-robotic-legs/. © 2018, Springer Nature Switzerland AG.

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Machine Learning Model of Digital Transformation Index for Mexican Households

2022 , García, Alfredo , Salazar, Vladimir , Ponce, Hiram

Digital transformation refers to the change in all aspects of human society by the adoption of digital technologies. Different methodologies and measurements have been proposed to determine the level of digital transformation in regions or countries. In this work, we propose the creation of a digital transformation index for Mexican households using machine learning models for digital transformation measurement analysis and estimation. We include three dimensions in terms of the information and communication technologies infrastructure, availability of services, and usage. We also use a public dataset from the Mexican government to build and train three machine learning models. Experimental results validate that our methodology can deliver a digital transformation measurement using machine learning models consistently with 84% of accuracy and 84% of F1-score. We also prototype a simple web application using the best machine learning model found. We anticipate that measuring the digital transformation in companies, governments, and households allows better decisions in business intelligence and public policy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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A Comparative Analysis of Evolutionary Learning in Artificial Hydrocarbon Networks

2020 , Ponce, Hiram , Souza, Paulo

Artificial hydrocarbon networks (AHN) is a supervised learning model that is loosely inspired on the interactions of molecules in organic compounds. This method is able to model data in a hierarchical and robust way. However, the original training algorithm is very time-consuming. Recently, novel training algorithms have been applied, including evolutionary learning. Particularly, this training algorithm employed particle swarm optimization (PSO), as part of the procedure. In this paper, we present a benchmark of other meta-heuristic optimization algorithms implemented on the training method for AHN. In this study, PSO, harmony search algorithm, cuckoo search, grey wolf optimization and whale optimization algorithm, were tested. The experimental results were done using public data sets on regression and binary classification problems. From the results, we concluded that the best algorithm was cuckoo search optimization for regression problems, while there is no evidence that one of the algorithms performed better for binary classification problems. © 2020, Springer Nature Switzerland AG.

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Modeling and Control Balance Design for a New Bio-inspired Four-Legged Robot

2019 , Ponce, Hiram , Acevedo, Mario , Morales, Elizabeth , Martinez-Villaseñor, Lourdes , Mayorga-Acosta, Carlos , Díaz Ramos, Gabriel

Bio-inspired robots have chosen to propose novel developments aiming to inhabit and interact complex and dynamic environments. Bio-inspired four-legged robots, typically inspired on animal locomotion, provide advantages on mobility, obstacle avoidance, energy efficiency and others. Balancing is a major challenge when legged robots require to move over uncertain and sharp terrains. It becomes of particular importance to solve other locomotion tasks such as walking, running or jumping. In this paper, we present a preliminary study on the modeling and control balance design of a bio-inspired four-legged robot for standing on its aligned legs in a straight line. The proposed robot is loosely inspired on the bio-mechanics of the chameleon. Thus, a mathematical modeling, simulation, intelligent control strategy, prototype implementation and preliminary results of control balance in our robot are presented and discussed. © Springer Nature Switzerland AG 2019.

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Artificial organic networks : artificial intelligence based on carbon networks

2014 , Ponce, Hiram , HIRAM EREDIN PONCE ESPINOSA;376768 , Campus Ciudad de México

This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: · approximation; · inference; · clustering; · control; · classification; and · audio-signal filtering. The text finishes with a consideration of directions in which AHNs could be implemented and developed in future. A complete LabVIEW™ toolkit, downloadable from the book’s page at springer.com enables readers to design and implement organic neural networks of their own. The novel approach to creating networks suitable for machine learning systems demonstrated in Artificial Organic Networks will be of interest to academic researchers and graduate students working in areas associated with computational intelligence, intelligent control, systems approximation and complex networks. ©2014 Springer.

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A Survey on Freezing of Gait Detection and Prediction in Parkinson’s Disease

2020 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Miralles-Pechuán, Luis

Most of Parkinson’s disease (PD) patients present a set of motor and non-motor symptoms and behaviors that vary during the day and from day-to-day. In particular, freezing of gait (FOG) impairs their quality of life and increases the risk of falling. Smart technology like mobile communication and wearable sensors can be used for detection and prediction of FOG, increasing the understanding of the complex PD. There are surveys reviewing works on Parkinson and/or technologies used to manage this disease. In this review, we summarize and analyze works addressing FOG detection and prediction based on wearable sensors, vision and other devices. We aim to identify trends, challenges and opportunities in the development of FOG detection and prediction systems. © 2020, Springer Nature Switzerland AG.

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Versatility of Artificial Hydrocarbon Networks for Supervised Learning

2018 , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning task, and it is difficult to comply with all possible desirable features of each particular domain. However, it is important that a new technique comply with the most requirements and desirable features of as many domains and learning tasks as possible. In this paper, we presented artificial hydrocarbon networks (AHN) as versatile and efficient supervised learning method. We determined the ability of AHN to solve different problem domains, with different data-sources and to learn different tasks. The analysis considered six applications in which AHN was successfully applied. © Springer Nature Switzerland AG 2018.

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Intelligent Management System for Micro-Grids using Internet-of-Things

2021 , Gutiérrez, Sebastián , Medina, Guillermo , Ponce, Hiram , Espinosa Loera, Ricardo Abel

The current research work proposes the design of an intelligent platform for managing the generation, distribution, transmission, commercialization and consumption of electricity, allowing the decision-making process aimed at reducing costs and maximizing the use of energy resources. The technological proposal is a system of information made of integrated sensors. This creates an electrical real-time network that share energy in the cloud through LPWAN networks. Later, the data from the sensors are received in an intelligent platform (Max4 IoT) that employs super-computing systems and artificial intelligence for the analysis of individual and aggregated data. The system is able to learn about the electrical network, knowing the consumers’ behavior and energy trends, allowing to generate responses based on specific situations by sending SMS alerts and emails about abnormal situations or programmed tasks. In addition, the system allows the generation of reports on the network status in real-time and commands control signals on the switching on-and-off of equipment in response to consumption peaks and/or power supply outages. The platform allows interaction with distributed generation systems and micro-grids, through its mobile or web interface, increasing user interaction with the electrical system and the inclusion of renewable energy sources, thus influencing a better quality of service provided to the end user. © 2021 IEEE.

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Preface : Advances in Soft Computing : 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13–18, 2023, Proceedings, Part II

2024-01-01 , Calvo, Hiram , Martinez-Villaseñor, Lourdes , Ponce, Hiram

The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society for Artificial Intelligence (SMIA) since 2000. MICAI is a major international artificial intelligence (AI) forum and the main event in the academic life of the country’s growing AI community. This year, MICAI 2023 was graciously hosted by the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) and the Universidad Autónoma del Estado de Yucatán (UAEY). The conference presented a cornucopia of scientific endeavors. ©Springer.

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Feature Selection Methods Evaluation for CTR Estimation

2016 , Miralles-Pechuán, Luis , Ponce, Hiram , Martinez-Villaseñor, Lourdes

The most widespread payment model in online advertising is Cost-per-click (CPC). In this model the advertisers pay each time that a user generates a click. In order to enhance the income of CPC Advertising Networks, it is necessary to give priority to the most profitable adverts. The most important factor in the profitability of an advert is Click-through-rate (CTR), which is the probability that a user generates a click in a given advert. In this paper we find which feature selection method between PCA, RFE, Gain ratio and NSGA-II is better suited, or if otherwise, the machine learning classification methods work best without any feature selection method. ©2016 IEE

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