Ponce, Hiram
Thumbnail Image
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 154
No Thumbnail Available
Publication

A Vision-based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach

2024 , Roberto Cuesta-Solano , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

The paper presents a Fuzzy Q-Learning (FQL) and optical flow based autonomous navigation approach. The FQL method takes decisions in an unknown environment and without mapping, using motion information and through a reinforcement signal into an evolutionary algorithm. The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are “dense” or “thin” which has a relationship with the proximity of objects. The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component. The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python.

View
No Thumbnail Available
Publication

Special issue on Mexican International Conference on Artificial Intelligence, MICAI 2014 and 2015

2017 , Ponce, Hiram , González-Mendoza, Miguel , Martinez-Villaseñor, Lourdes

This special issue of the journal Soft Computing offers extended versions of some of the best-awarded, high-reviewed and invited papers presented on the 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, held in Tuxtla Gutiérrez, Chiapas, Mexico, on November 16–22, 2014, under the organization of the Mexican Society for Artificial Intelligence (SMIA) in cooperation with the Instituto Tecnológico de Tuxtla Gutiérrez and Universidad Autónoma de Chiapas, and on the 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, held in Cuernavaca, Morelos, Mexico, on October 25–31, 2015, under the organization of the SMIA in cooperation with the Instituto de Investigaciones Eléctricas. ©2017 Soft Computing, Springer Verlag.

View
No Thumbnail Available
Publication

Distributed evolutionary learning control for mobile robot navigation based on virtual and physical agents

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

This paper presents a distributed evolutionary learning control based on social wound treatment for mobile robot navigation using an integrated multi-robot system comprised of simulated and physical robots. To do so, this work proposes an extension of the population-based metaheuristic wound treatment optimization (WTO) method into a distributed scheme. In addition, this distributed WTO method is implemented on the multi-robot system allowing them to experience the environment in their own and communicate their findings, resulting in an emergence intelligence. We implemented our proposal using the combination of five simulated robots with one physical robot for tuning a navigation controller to move freely in a workspace. Results showed that the solution found by this multi-robot system aims using the output controller in the physical robot for successfully achieving the goal to move the robot around a U-maze, without applying any transfer learning approach. We consider this proposal useful in evolutionary robotics, and of great importance to decrease the gap related to transfer knowledge in robotics from simulation to reality. © 2019 Elsevier B.V.

View
No Thumbnail Available
Publication

A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

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

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

View
No Thumbnail Available
Publication

Algotrading R2ED: A Machine Learning Approach

2024 , Ruben Martinez , Diego Tinoco , Rodrigo Huerta , Emilio Flores , Ponce, Hiram

View
No Thumbnail Available
Publication

Analysis of Constraint-Handling in Metaheuristic Approaches for the Generation and Transmission Expansion Planning Problem with Renewable Energy

2018 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Ramírez, Juan Manuel , Marmolejo Saucedo, José Antonio , Hernández, Agustina

A multiperiod generation and transmission expansion planning (G&TEP) problem is considered. This model integrates conventional generation with renewable energy sources, assuming a stochastic approach. The proposed approach is based on a centralized planned transmission expansion. Due to the worldwide recent energy guidelines, it is necessary to generate expansion plans adequate to the forecast demand over the next years. Nowadays, in most energy systems, a public entity develops both the short and long of electricity-grid expansion planning. Due to the complexity of the problem, there are different strategies to find expansion plans that satisfy the uncertainty conditions addressed. We proposed to address the G&TEP problem with a pure genetic algorithm approach. Different constraint-handling techniques were applied to deal with two complex case studies presented. Numerical results are shown to compare the strategies used in the test systems, and key factors such as a prior initialization of population and the estimated minimum number of generations are discussed. ©2018, Wiley/Hindawi.

View
No Thumbnail Available
Publication

Mobile Robot with Movement Detection Controlled by a Real-Time Optical Flow Hermite Transform

2015 , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

This chapter presents a new algorithm inspired in the human visual system to compute optical flow in real-time based on the Hermite Transform. This algorithm is applied in a vision-based control system for a mobile robot. Its performance is compared for different texture scenarios with the classical Horn and Schunck algorithm. The design of the nature-inspired controller is based on the agent-environment model and agent’s architecture. Moreover, a case study of a robotic system with the proposed real-time Hermite optical flow method was implemented for braking and steering when mobile obstacles are close to the robot. Experimental results showed the controller to be fast enough for real-time applications, be robust to different background textures and colors, and its performance does not depend on inner parameters of the robotic system. © Springer International Publishing Switzerland 2016.

View
No Thumbnail Available
Publication

Price Estimation for Pre-owned Vehicles Using Machine Learning

2024 , Mariel Rivera , Bruno Campos , Adrián Galicia , Enrique Noguera , Ponce, Hiram

View
No Thumbnail Available
Publication

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

View
No Thumbnail Available
Publication

Mechatronic Design of a Low-Cost Smart Wheelchair Controlled by Joystick and Voice Commands

2023 , Bobadilla-Rendón, David , Monroy-Rueda de León, Irvine J. , Salazar-Salinas, Gabriel , Stefan-Lepe De Soto, Antonio , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

Elderly people have increased at an accelerated rate in recent years. In Mexico, one of the main problems affecting this population are related to disabilities, specifically limited mobility, i.e., arthritis in older adults. Different technological solutions have been proposed, such as electrical wheelchairs. However, for arthritic people, these wheelchairs are difficult to operate, lacking comfortability, and might be very expensive. In this work, we propose the development of a smart wheelchair for arthritic older adults able to move automatically and controlled by using slight manual movements of the hand and by voice commands. We followed the general methodology of a mechatronic design. A proof-of-concept model of the wheelchair was implemented. For validation, we tested the prototype in different real indoor scenarios. Results conclude that our proposed smart wheelchair complies with the user requirements, it is easy to operate, and the cost is reduced considerably. We anticipate this is a low-cost efficient smart wheelchair prototype that can be further considered for real technological solutions.

View