A5047916417- Intelligent Tutoring Systems and Adaptive Learning
- AI-based Problem Solving and Planning
- Ethics and bioethics in healthcare
- Psychological Treatments and Disorders
- Ethics in medical practice
- Rough Sets and Fuzzy Logic
- Bayesian Modeling and Causal Inference
- Energy Load and Power Forecasting
- Fault Detection and Control Systems
- Fuzzy Logic and Control Systems
- Psychology Research and Bibliometrics
- Neuroethics, Human Enhancement, Biomedical Innovations
- Innovative Teaching and Learning Methods
- Data Mining Algorithms and Applications
- Online Learning and Analytics
- Power Transformer Diagnostics and Insulation
- Electricity Theft Detection Techniques
- Smart Grid Security and Resilience
- Social Robot Interaction and HRI
- Semantic Web and Ontologies
- Mental Health and Psychiatry
- Power Systems and Technologies
- Smart Grid and Power Systems
- Network Security and Intrusion Detection
- Big Data and Business Intelligence
Instituto Nacional de Electricidad y Energías Limpias
2016-2024
Ineos (United States)
2023-2024
Universidad Del Valle De Cuernavaca
2009-2024
University of Valparaíso
2011-2022
Universidad de La Frontera
2017
Instituto de Investigaciones Eléctricas
2006-2016
Valparaiso University
2002-2013
Universidad Nacional Autónoma de México
2010
Universitat Politècnica de València
2007-2010
The application of virtual reality (VR) technologies is beneficial to the training related industrial processes. Mainly because allow complex threatening tasks within a safe environment. interactive three-dimensional (3D) representation real world seems be more effective learning medium than other traditional tools. This paper presents development and implementation environment based on VR, applied maintenance medium-tension overhead live-lines in power distribution networks. architecture...
A key component for the performance, availability, and reliability of power grids is transformer. Although transformers are very reliable assets, early detection incipient degradation mechanisms important to preventing failures that may shorten their residual life. In this work, a comparative analysis standard machine learning (ML) algorithms (such as single ensemble classification algorithms) automatic (autoML) classifiers presented fault diagnosis transformers. The goal research determine...
The application of Virtual Reality technologies in training process has been shown as an powerful tool. 3D representation real world and interactivity with it seems to be a more natural learning media than other traditional tools. This paper presents the virtual reality technology underground lines operators Distribution Power System. system is composed three main modules: catalog, mode evaluation mode. includes management assess operators' progress. incorporates forty different maintenance...
Emotions have been identified as important players in motivation, and motivation is very for learning. When a tutor recognizes the affective state of student responds accordingly, may be able to motivate students improve learning process. We propose general behavior model which integrates information from student's pedagogical state, tutorial situation, decide best action, considering preferences point view. Our proposal based on emotions models, personality theories teachers' expertise. The...
Temporal Nodes Bayesian Networks (TNBNs) and of Probabilistic Events in Discrete Time (NPEDTs) are two different types Event (EBNs). Both based on the representation uncertain events, alternatively to Dynamic Networks, which deal with real-world dynamic properties. In a previous work, Arroyo-Figueroa Sucar applied TNBNs diagnosis prediction temporal faults that may occur steam generator fossil power plant. We present an NPEDT for same domain, along comparative evaluation networks. examine...
A key component for the proper functioning, availability, and reliability of power grids is trans- former. Although these are very reliable assets, early detection incipient degradation mechanisms important to prevent failures that may shorten their lifetime. In this work a review comparative analysis, classical Machine Learning algorithms (such as single ensemble classification algorithms) two automatic machine learning classifiers, presented fault diagnosis transformers. The goal determine...
Electricity load-forecasting is an essential tool for effective power grid operation and energy markets. However, the lack of accuracy on estimation electricity demand may cause excessive or insufficient supply which can produce instabilities in load cuts. Hence, probabilistic methods have become more relevant since these allow understanding not only load-point forecasts but also uncertainty associated with it. In this paper, we develop a method based Association Rules Artificial Neural...