R
Ricardo A. Ramirez-Mendoza
Researcher at Monterrey Institute of Technology and Higher Education
Publications - 234
Citations - 2069
Ricardo A. Ramirez-Mendoza is an academic researcher from Monterrey Institute of Technology and Higher Education. The author has contributed to research in topics: Damper & Computer science. The author has an hindex of 15, co-authored 195 publications receiving 1139 citations. Previous affiliations of Ricardo A. Ramirez-Mendoza include University of Monterrey.
Papers
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Journal ArticleDOI
A Logic Architecture for 360 ADAS-Alerts for Hazards Detection Based in Driver Actions
Javier Izquierdo-Reyes,Luis A. Curiel-Ramirez,Ricardo A. Ramirez-Mendoza,M. Rogelio Bustamante-Bello +3 more
TL;DR: A novel approach for passive safety in vehicles by Advanced Driver Assistance Systems (ADAS) alert emission in 360° around driver to notify about hazards nearby the vehicle depending on the actions taken by driver per the context is presented.
Proceedings ArticleDOI
Advanced Learning Assistant System (ALAS) for Engineering Education
Mauricio Adolfo Ramírez-Moreno,M. Diaz-Padilla,K.D. Valenzuela-Gomez,Adriana Vargas-Martínez,Armando Roman-Flores,Ruben Morales-Menendez,Ricardo A. Ramirez-Mendoza,Jorge de J. Lozoya-Santos +7 more
TL;DR: Electroencephalographic signals were recorded in two groups during learning tasks, and performance was evaluated with an exam, showing better performance on the video group, as well as power changes in theta and beta bands, mainly in frontal and occipital cortices.
Book ChapterDOI
Toward a New Approach for Online Fault Diagnosis Combining Particle Filtering and Parametric Identification
TL;DR: This paper proposes a new approach for online fault diagnosis in dynamic systems, combining a Particle Filtering algorithm with a classic Fault Detection and Isolation framework, resulting in an online algorithm with the advantages of both methods.
Book ChapterDOI
Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis
Juan C. Tudon-Martinez,Ruben Morales-Menendez,Luis E. Garza-Castañón,Ricardo A. Ramirez-Mendoza +3 more
TL;DR: In this paper, the authors compared Dynamic Principal Component Analysis (DPCA) and Artificial Neural Networks (ANN) in the fault diagnosis task, which do not assume any form of model structure and rely only on process historical data.
Journal ArticleDOI
Synthesis analysis for data driven model predictive control*
TL;DR: In this article , data driven model predictive control, such as persistent excitation, optimal state feedback controller, output predictor, and stability are presented, whose state information and output variable are generated by measured data online.