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 14
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Towards Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches

2019 , Ponce, Hiram

Inspiration in nature has been widely explored, from macro to micro-scale. Natural phenomena mainly considers adaptability, optimization, robustness, organization, among other properties, to deal with complexity. When looking into chemical phenomena, stability and organization are two properties that emerge. Recently, artificial hydrocarbon networks (AHN), a supervised learning method inspired in the inner structures and mechanisms of chemical compounds, have been proposed as a data-driven approach in artificial intelligence. AHN have been successfully applied in data-driven approaches, such as: regression and classification models, control systems, signal processing, and robotics. To do so, molecules-the basic units of information in AHN-play an important role in the stability, organization and interpretability of this method. Until now, building the architecture of AHN has been treated as a whole entity; but distributed computing mechanisms, as well as the exploitation of hierarchical organization of molecules, can enhance the performance of AHN. Thus, this paper aims to discuss challenges and trends of artificial hydrocarbon networks as a data-driven method, with emphasis on packaging, distributed computing and hierarchical properties. Throughout this work, it presents a description of the main insights of AHN and the proposed distributed and hierarchical mechanisms in molecules. Potential applications and future trends on AHN are also discussed. © 2019 IEEE.

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Multibody dynamics for human-like locomotion

2020 , Acevedo, Mario , Ponce, Hiram

Multibody dynamics has been a fundamental tool for modeling, simulation and design of human-like locomotion systems. Either in the prosthetic and orthotics sector to develop devices for improvement or restoration of mobility, or as in the simulation, design, and optimization of humanoid robots. A lot of research and development has been done in these challenging areas where new mechanisms and improvements in dynamics are always welcome. The dynamic balancing of mechanisms (force and moment balancing at the fixed base) is an area that, along with multibody dynamics, can help to improve the design of human-like locomotion systems. In this chapter, the application of a force-balanced mechanism is proposed as a leg to be part of a biped robot. Stability is analyzed through the application of learning approaches based on an artificial intelligence, namely artificial hydrocarbon networks. Modeling and results from multibody dynamics simulation are presented. © 2020 Elsevier Inc. All rights reserved.

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An intelligent climate monitoring system for hygrothermal virtual measurement in closed buildings using Internet-of-things and artificial hydrocarbon networks

2024 , Ponce, Hiram , Botero-Valencia, Juan , Botero Valencia, Juan , Marquez-Viloria, David , Castano-Londono, Luis

Studies analyzing indoor thermal environments comprising temperature and humidity may be insufficient when obtaining data from sensors, which may be susceptible to inaccurate or failed information from internal and external factors. Therefore, this study proposes an intelligent climate monitoring using a supervised learning method for virtual hygrothermal measurement in enclosed buildings used to predict temperature and relative humidity when a sensor failure is detected. The methodology comprises the data collection from a wireless sensor network, the building of the learning model for predicting the dynamics of environmental variables, and the implementation of a sensor failure detection model. We use an artificial hydrocarbon network as the learning model for their simplicity and effectiveness under uncertain and noisy data. The experiments use data acquired in two settings: (1) a laboratory office and (2) a museum storage room. The first scenario has multiple workstations, and the staff turns on or off the air conditioning depending on the feeling of comfort, generating an uncontrolled environment for the variables of interest. The second scenario has controlled temperature and humidity to ensure the conservation conditions of the museum pieces. Both scenarios used 12 sensors that acquired data for one month, providing an average of 58,300 values for each variable. Results of the proposed methodology provide 95% of accuracy in terms of sensor failure detection and identification, and less than 0.22% of tolerance variability in temperature and humidity after sensor accommodation in both scenarios. ©Elsevier

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A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

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

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

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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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Explainable artificial hydrocarbon networks classifier applied to preeclampsia

2024 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Martínez Velasco, Antonieta Teodora

Explainability is crucial in domains where system decisions have significant implications for human trust in black-box models. Lack of understanding regarding how these decisions are made hinders the adoption of so-called clinical decision support systems. While neural networks and deep learning methods exhibit impressive performance, they remain less explainable than white-box approaches. Artificial Hydrocarbon Networks (AHN) is an effective black-box model that can be used to support critical clinical decisions if accompanied by explainability mechanisms to instill confidence among clinicians. In this paper, we present a use case involving global and local explanations for AHN models, provided with an automatic procedure so-called eXplainable Artificial Hydrocarbon Networks (XAHN). We apply XAHN to preeclampsia prognosis, enabling interpretability within an accurate black-box model. Our approach involves training a suitable AHN model using the cross-validation with ten repetitions, followed by a comparative analysis against four well-known machine learning techniques. Notably, the AHN model outperformed the others, achieving an F1-score of 74.91%. Additionally, we assess the efficacy of our XAHN explainer through a survey applied to clinicians, evaluating the goodness and satisfaction of the provided explanations. To the best of our knowledge, this work represents one of the earliest attempts to address the explainability challenge in preeclampsia prediction.© 2024 The Author(s). Published by Elsevier Inc.

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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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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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The development of an artificial organic networks toolkit for LabVIEW

2015 , Ponce, Hiram , Ponce, Pedro , Molina, Arturo

Two of the most challenging problems that scientists and researchers face when they want to experiment with new cutting-edge algorithms are the time-consuming for encoding and the difficulties for linking them with other technologies and devices. In that sense, this article introduces the artificial organic networks toolkit for LabVIEW™ (AON-TL) from the implementation point of view. The toolkit is based on the framework provided by the artificial organic networks technique, giving it the potential to add new algorithms in the future based on this technique. Moreover, the toolkit inherits both the rapid prototyping and the easy-to-use characteristics of the LabVIEW™ software (e.g., graphical programming, transparent usage of other softwares and devices, built-in programming event-driven for user interfaces), to make it simple for the end-user. In fact, the article describes the global architecture of the toolkit, with particular emphasis in the software implementation of the so-called artificial hydrocarbon networks algorithm. Lastly, the article includes two case studies for engineering purposes (i.e., sensor characterization) and chemistry applications (i.e., blood–brain barrier partitioning data model) to show the usage of the toolkit and the potential scalability of the artificial organic networks technique. © 2015 Wiley Periodicals, Inc.

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Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition

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

In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.

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