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 154
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Using Social Robotics to Identify Educational Behavior: A Survey

2024 , Romero-C. de Vaca, Antonio J. , Melendez-Armenta, Roberto Angel , Ponce, Hiram

The advancement of social robots in recent years has opened a promising avenue for providing users with more accessible and personalized attention. These robots have been integrated into various aspects of human life, particularly in activities geared toward students, such as entertainment, education, and companionship, with the assistance of artificial intelligence (AI). AI plays a crucial role in enhancing these experiences by enabling social and educational robots to interact and adapt intelligently to their environment. In social robotics, AI is used to develop systems capable of understanding human emotions and responding to them, thereby facilitating interaction and collaboration between humans and robots in social settings. This article aims to present a survey of the use of robots in education, highlighting the degree of integration of social robots in this field worldwide. It also explores the robotic technologies applied according to the students’ educational level. This study provides an overview of the technical literature in social robotics and behavior recognition systems applied to education at various educational levels, especially in recent years. Additionally, it reviews the range of social robots in the market involved in these activities. The objects of study, techniques, and tools used, as well as the resources and results, are described to offer a view of the current state of the reviewed areas and to contribute to future research. ©The authors ©MDPI.

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Robótica aplicada con LabVIEW y LEGO

2015 , Ponce, Hiram , HIRAM EREDIN PONCE ESPINOSA;376768 , Campus Ciudad de México

El objetivo de esta obra es mostrar de forma teórica y práctica las técnicas más importantes utilizadas en la academia y la industria para el desarrollo de sistemas robóticos, y para esto se exponen desde los conceptos básicos de robótica hasta los algoritmos de control y las técnicas de planificación de trayectorias. En especial, se hace uso de los sistemas robóticos LEGO Mindstorms NXT junto con la plataforma de desarrollo LabVIEW. Aprenda qué es un robot LEGO NXT y cual es el entorno de programación NXT. Conozca los sistemas de control empleando robots NXT, así como el entorno de programación de los LEGO Mindstorms. Desarrolle sus propias aplicaciones de planificación de ruta de planificación de trayectorias con espacios variantes en el tiempo. ©2015 Alfaomega.

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Preface

2024-01-01 , Ponce, Hiram

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QUÉ MAX-TE-LATTE Personalized Product Recommendations in the Coffee Shop Industry: Enhancing Customer Experience and Loyalty

2024 , Jorge de Jesús Luis Ortiz , Maximino Navarro , Claude Prud’Homme , Fernando Vázquez , Ponce, Hiram

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Control Design for an Electrical Propulsion System in a Drag-Free CubeSat

2021 , Nuñez Martínez, José Pablo , Laguna Juárez, Carlos Daniel , Ponce, Hiram , Zavala Sousa, Andrea , Ribé Viesca, María del Carmen , Blanco, Fernando del , Aparicio Appendini, Juan Pablo , Hernández, Alvaro , Zarate-Villazon, Ángel M.

Drag-free satellites such as TRIAD I, Gravity Probe B, GOCE and LISA-Pathfinder have demonstrated the use of a freefloating test mass as a gravitational reference to the satellite's feedback control system. In drag-free motion, gravity is the only disturbing force and therefore the satellite is not affected by the nonconservative atmospheric drag which dissipates most of the orbital energy in satellites on a geodesic orbit. A drag-free 3U CubeSat equipped with Ionic-Electrospray Thrusters and an off-the-shelf Attitude Control and Determination System (ADCS) has been in development to make atmospheric measurements in a Low Earth Orbit. Ionic-Electrospray thrusters are emitter arrays featuring a highly dense concentration of porous glass emitter tips from which ions are expelled with an applied voltage between two electrodes, controlled with current or voltage. A propulsion and an attitude control are required for countering the drag force at a micro-Newton scale as well as other internal disturbances. In this work, we first achieve an optimal propulsion control using linear quadratic regulation and then analyse the non-linear dynamics of the controlled satellite, determined from the test mass' motion. Changes in air density, environmental noise from the gravity gradient and aerodynamic torques, noise from the thruster arrays (from electrical current and alignment errors), and a pointing error from the ADCS are all considered in the design process. Finally, the propulsion control performance and power consumption have been traded off in radial-transverse coordinate system and Clohessy-Wiltshire-Hill formulation. © 2021 International Astronautical Federation, IAF. 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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Challenges and trends in multimodal fall detection for healthcare

2020 , Ponce, Hiram , Brieva, Jorge , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , HIRAM EREDIN PONCE ESPINOSA;376768 , JORGE EDUARDO BRIEVA RICO;121435

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.

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Advances in Computational Intelligence : Preface

2018 , 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 of Artificial Intelligence (SMIA) since 2000. MICAI is a major international artificial intelligence forum and the main event in the academic life of the country’s growing artificial intelligence community. MICAI conferences publish high-quality papers in all areas of artificial intelligence and its applications. The proceedings of the previous MICAI events have been published by Springer in its Lecture Notes in Artificial Intelligence series, vol. 1793, 2313, 2972, 3789, 4293, 4827, 5317, 5845, 6437, 6438, 7094, 7095, 7629, 7630, 8265, 8266, 8856, 8857, 9413, 9414, 10061, 10062, 10632, and 10633. Since its foundation in 2000, the conference has been growing in popularity and improving in quality. ©2018 Springer Verlag.

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Geolocation system of fire monitoring station through Lorawan

2020 , Gutiérrez, Sebastián , Luna, Pedro , Ponce, Hiram

The evolution of the Internet of Things (IoT) over the last few years has a growing outcome, where LoRaWAN™ based solutions have a remarkable increase in its quality and innovation. This evolvement is pushing the integration of new capabilities, such as real-time responsiveness, resilient systems, massive management of devices, geolocation, tracking, and smarter notification systems. The current work presents a design and an implementation proposal of a LoRaWAN™ solution to monitor the probability of existing fire in a given location. This application is capable of integrating the above-mentioned features by exploiting the benefits of LoRaWAN™ protocol and its capability to interact with cloud services. The system makes use of The Thing Network (TTN) as LoRa™ Network Server (LNS), LoRa™ cloud Geolocation Services for tracking, Amazon Web Service Elastic Cloud Computing (AWS EC2) as a container of the application, and advanced features of Go programming language for the rest of features such as notifications, dashboards and data operations as extraction, transformation and load (ETL). ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved.

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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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