Control and Systems Engineering, Artificial Intelligence, Engineering, Mechanical Engineering
249
Scopus Publications
Scopus Publications
Sustainable Intensification Index: A flexible strategy for assessing the role of process intensification in achieving the 2030 agenda and beyond Enrique A. López-Guajardo, José Ezequiel Santibañez-Aguilar, Juan Gabriel Segovia-Hernández, Eduardo Sánchez-Ramírez, Ruben Morales-Menendez Journal of Cleaner Production, 2026 Sustainability is decisive when designing, evaluating, and modifying new or existing chemical processes. One of the promising approaches in the chemical and biochemical processing plants that consider this factor is Process Intensification (PI). An intensified process could decrease the energy consumption and waste generation while improving its cost-effectiveness and safety. Even though there are several methods that include a the sustainability dimension to assess the performance of a chemical process or different intensified equipment, most of them do not explicitly aligned with the Sustainable Development Goals and usually are difficult to compare it with other metrics. Therefore, this work proposes the Sustainable Intensification Index (SII) as a new multifactorial strategy to assess technology performance based on the United Nations (UN) Sustainable Development Goals (SDG). The SII is obtained from three independent methodologies. One of the main advantages of the SII is that it can be a comparable metric across different methodologies which can be used to define an Overall Sustainable Intensification Index (OSII) that to try to dampen the inherited bias that each method might have. The proposed SII and OSII methods were applied to evaluate different alternatives for lactic acid production from previously reported data as a case study. The obtained SII and OSII results showed which technologies could be adopted to improve the process sustainability and aided in identifying opportunity areas of the evaluated technologies. Finally, the SII and OSII are powerful tools in the decision-making process for enhancing existing technologies in sustainability or developing new ones.
Generative AI as an Enabler for Process Intensification Alejandro Montesinos-Castellanos, Antonio Flores-Tlacuahuac, Enrique A. López Guajardo, Karla M. Esquivel-Ortiz, Ruben Morales-Menendez Industrial and Engineering Chemistry Research, 2026 The advancement and fusion of the value chain, the industrial Internet of Things, big data with advanced analytics, and cloud computing have propelled the adoption of artificial intelligence (AI) in the design and evaluation of Process Intensification (PI), producing better results. Generative AI (GenAI) is an approach capable of generating new content with the proper database. The role of GenAI in the design of PI systems under the vision of Industry 4.0 (PI4.0) was explored. This study identified three primary categories of enablers critical to improving physical performance, digital capabilities, and strategic integration within PI4.0 systems. A brief review identified the potential of different GenAI models in PI4.0; however, through an in-depth case study (distillation column design), the GenAI application in the design of PI4.0 was shown to enable the creation of additional data and scenarios, foster increased creativity and exploration, introduce novel configurations and designs, and accelerate the pace of innovation. The results of the case study highlight the limitations of discriminative AI models and underscore the transformative potential of GenAI approaches. The most significant advantage of GenAI is its ability to expand the range of possibilities, making the PI4.0 design faster, more adaptable, innovative, and robust.
Decoding motivation for leadership in higher education: leveraging machine learning for a future education Fidel Antonio Casillas-Muñoz, Inés Alvarez-Icaza, Michael T. Tworek, Carlos Escobar-Díaz, Gabriela Sanchez-Zuno, Ruben Morales-Menendez, María Soledad Ramírez-Montoya Frontiers in Education, 2026 Decoding motivation for leadership in higher education represents a scientific and talent management imperative, the complexity of which is being rigorously modelled and unveiled through the predictive power of machine learning (ML), promising to catalyse a transformation in the training of future leaders. The study focused on predicting leadership and entrepreneurship among higher education students, analysing seven dimensions: aesthetic, economic, individualistic, political, altruistic, regulatory, and theoretical. ML was used to test three models (logistic regression, random forest, and gradient boost machine) for predicting leadership and entrepreneurial participation among students, using a database of 1,796 subjects. The findings reveal (a) the almost uniform importance of all motivational dimensions in the development of leadership skills, suggesting a multifaceted approach; (b) the significant potential of ML algorithms, especially the Random Forest model, to predict student participation in leadership and entrepreneurship activities, with exceptional accuracy across genders; and (c) applying educational interventions (active, challenger, engaged, proactive learning strategies) with top-down as well as bottom-up approaches based on individual motivational scores. This research contributes personalised, active, and practical approaches to using ML and driving educational strategies and programmes that enhance skills development for the future. Improving leadership development programmes and managerial competencies through the application of ML as a transformative tool encourages navigation through the complexities of contemporary education systems.
Quality 4.0: Learning quality control, the evolution of SQC/SPC Carlos A. Escobar, José Antonio Cantoral-Ceballos, Ruben Morales-Menendez Quality Engineering, 2025 This article marks a significant advancement in the field of quality management, specifically focusing on the evolution from traditional Statistical Quality Control (SQC) and Statistical Process Control (SPC) methods to a more advanced Learning Quality Control (LQC) approach. The research introduces Quality 4.0 (Q4.0) as a novel paradigm that fuses the technologies of the fourth industrial revolution, Manufacturing Big Data (MBD), Industrial Internet of Things (IIoT), Cloud Storage and Computing (CSC) and Artificial Intelligence (AI), with traditional quality management practices. The central theme of this study is exploring the limitations inherent in conventional quality control methods when faced with the complexities of modern manufacturing environments. The authors propose LQC systems as a solution, employing binary classification algorithms to predict and detect defects in manufacturing processes. This represents a shift from reactive to proactive quality measures enabled by AI’s real-time data processing capabilities. The document delves into the evolution of manufacturing data across industrial revolutions, highlighting the exponential growth of unstructured data and its challenges. Through case studies, the authors illustrate the practical applications of LQC systems, demonstrating their ability to learn complex patterns in hyperdimensional spaces and automate tasks traditionally performed visually.
Process intensification of a catalytic-wall Taylor-Couette reactor through unconventional modulation of its angular speed Enrique A. López-Guajardo, Renato Galluzzi, Fernando Delgado-Licona, Ruben Morales-Menendez Chemical Engineering Journal, 2024 This study proposes a non-conventional operation of a Taylor-Couette reactor by introducing a periodic flow perturbation as a modulated angular velocity of its inner cylinder. The applicability of this type of waveform has yet to be explored in Taylor-Couette reactors as a means to enhance mass transfer phenomena in reactive systems. A multiphysics numerical study was carried out considering a mass-transfer limited system with a catalytic reaction at the outer cylinder boundary of the reactor while applying different modulations of the inner cylinder angular speed. Results showed that a modular signal could yield conversions similar to a constant angular-speed operation. However, the use of modulating signals brings two essential benefits. First, it enhances mass transfer, which yields higher conversions by dynamically changing the flow patterns in the reactor. This improvement is demonstrated and discussed in terms of a dynamic mixing index, which accounts for the formation and abrupt disruption of Taylor vortices in the reactor. Second and more importantly, this type of operation leads to an overall reduction in the electrical power required to drive the system (∼25 % reduction). The present study opens the possibility of using intelligent control strategies to optimize reactions and intensify conventional systems with non-conventional operation modes.
The decay of Six Sigma and the rise of Quality 4.0 in manufacturing innovation Carlos A. Escobar, Daniela Macias-Arregoyta, Ruben Morales-Menendez Quality Engineering, 2024 Smart manufacturing (SM) processes exhibit rapidly increasing complexity, nonlinear patterns in hyperdimensional spaces, high volumes of data, transient sources of variations, reduced lifetime, ultrahigh conformance, and non-Gaussian pseudo-chaotic behaviors. Standard quality control techniques and paradigms are not up to handling all these dynamics. Therefore, quality engineers went stagnant, with little innovation to offer to the manufacturing industry. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) have been applied to solve complex engineering problems and drive innovation. This new era where computer science principles are applied to quality control is called Quality 4.0 (Q4.0). However, the Six Sigma five-step problem-solving strategy (define, measure, analyze, improve, and control) does not fit the full ML cycle. The limitations of the Six Sigma techniques and paradigms in driving manufacturing innovation are discussed. A case study where a 3D quality pattern that can be easily detected by an MLA is not detected by traditional process monitoring methods. Early results motivate the development of the new era of Q4.0 without the limitations of Six Sigma and the potential of AI.
Fostering Research Engagement in Engineering Students via an Industrial-Level Control Device Rodrigo Gutiérrez-Garza, Alejandro Arceo, Jorge de J. Lozoya-Santos, Juan C. Tudon-Martinez, Ruben Morales-Menendez IEEE Global Engineering Education Conference Educon, 2024 Currently, vehicles come equipped with a growing number of electronic devices that enable various driving assis-tance systems. From an academic perspective, it is imperative that engineering students develop the skills and knowledge necessary to handle standardized devices in the automotive industry. To promote interaction with this technology and disseminate theo-retical concepts, innovative ideas, and critical thinking, educative workshops playa crucial role in engaging undergraduate students in the research field. This article presents a case study of an active learning method (project-based learning) in engineering education at Tecnologico de Monterrey, which involves developing an automotive control project using an industrial-level controller, i.e., a Vehicle Control Unit (VCU). This activity helps students to learn signal processing tasks, control design problems, and analyze the capabilities of VCU with simpler environments like Arduino. Typically, students conduct these activities following a research methodology. For this assessment, two groups of engineering students have been defined. One group has previously undergone a control engineering course, while the other group has not. The study evaluated the results obtained from the statisti-cal analysis of the scores of the two groups with respect to their involvement in industrial equipment projects and their perceptions of the project complexity. The first group consisted of undergraduates who had not taken a control course, and the second group included those who have already taken it. Therefore, the first group showed a better understanding of the subject than the second group. However, both groups expressed a preference for conducting engineering projects with real vehicles instead of scaled -down models.
Complex competencies for leader education: artificial intelligence analysis in student achievement profiling Maria Soledad Ramirez-Montoya, Ruben Morales-Menendez, Michael Tworek, Carlos A. Escobar, Rasikh Tariq, Gloria Concepcion Tenorio-Sepulveda Cogent Education, 2024 Future education requires fostering high-level competencies to enhance student talent, and artificial intelligence (AI) can help in profile analysis. The aim was to determine the variables that predict the GPA of students in the ‘Leaders of Tomorrow’ program through an integrated methodology of data analytics, machine learning modeling, and feature engineering in order to generate knowledge about the application of AI in social impact programs. This research focused on 466 graduates of a ‘Leaders of Tomorrow’. A regression analysis was performed to model the relationship between the dependent variable and multiple independent variables. The findings revealed: (a) Analysis of variance (ANOVA) demonstrated exceptional model fit for predicting ‘student.term_Grade Academic Performance (GPA)_program’ with an R-squared of 0.999; (b) Visual analysis showed that significant variables like age and origin-school Grade-Point Average (GPA) affect term GPA; (c) Kendall tau correlation revealed a positive correlation of origin-school GPA with term GPA and a slightly negative one with age; (d) Support Vector Machine (SVM) regression aligned actual and predicted GPAs closely, indicating high accuracy; and (e) Recursive Feature Elimination (RFE) identified ‘student_originSchool.gpa’ as the most predictive feature. This study is intended to be of value to academic communities interested in enhancing the academic profiles of students with complex competencies, as well as communities interested in applying AI in education for predictions that contribute to trajectories for training.
Prognosis patients with COVID-19 using deep learning José Luis Guadiana-Alvarez, Fida Hussain, Ruben Morales-Menendez, Etna Rojas-Flores, Arturo García-Zendejas, Carlos A. Escobar, Ricardo A. Ramírez-Mendoza, Jianhong Wang BMC Medical Informatics and Decision Making, 2022
Quality 4.0 – an evolution of Six Sigma DMAIC Carlos Alberto Escobar, Daniela Macias, Megan McGovern, Marcela Hernandez-de-Menendez, Ruben Morales-Menendez International Journal of Lean Six Sigma, 2022
Learning analytics: state of the art Marcela Hernández-de-Menéndez, Ruben Morales-Menendez, Carlos A. Escobar, Ricardo A. Ramírez Mendoza International Journal on Interactive Design and Manufacturing, 2022
Service robots: Trends and technology Juan Angel Gonzalez-Aguirre, Ricardo Osorio-Oliveros, Karen L. Rodríguez-Hernández, Javier Lizárraga-Iturralde, Rubén Morales Menendez, Ricardo A. Ramírez-Mendoza, Mauricio Adolfo Ramírez-Moreno, Jorge de Jesús Lozoya-Santos Applied Sciences Switzerland, 2021
Biometric applications in education Marcela Hernandez-de-Menendez, Ruben Morales-Menendez, Carlos A. Escobar, Jorge Arinez International Journal on Interactive Design and Manufacturing, 2021
Learning with Missing Data Carlos A. Escobar, Jorge Arinez, Daniela Macias, Ruben Morales-Menendez Proceedings 2020 IEEE International Conference on Big Data Big Data 2020, 2020
Competencies for Industry 4.0 Marcela Hernandez-de-Menendez, Ruben Morales-Menendez, Carlos A. Escobar, Megan McGovern International Journal on Interactive Design and Manufacturing, 2020
Interactive urban route evaluation system for smart electromobility Luis A. Curiel-Ramirez, Ricardo A. Ramirez-Mendoza, M. Rogelio Bustamante-Bello, Ruben Morales-Menendez, Ariel A. Lopez-Aguilar, Carlos A. Lugo-Galeana, Aaron S. Garcia-Chavez International Journal on Interactive Design and Manufacturing, 2020
Educational experiences with Generation Z Marcela Hernandez-de-Menendez, Carlos A. Escobar Díaz, Ruben Morales-Menendez International Journal on Interactive Design and Manufacturing, 2020
Incorporating the sustainable development goals in engineering education Ricardo A. Ramirez-Mendoza, Ruben Morales-Menendez, Elda M. Melchor-Martinez, Hafiz M. N. Iqbal, Lizeth Parra-Arroyo, Adriana Vargas-Martínez, Roberto Parra-Saldivar International Journal on Interactive Design and Manufacturing, 2020
Process intensification education contributes to sustainable development goals. Part 1 David Fernandez Rivas, Daria C. Boffito, Jimmy Faria-Albanese, Jarka Glassey, Nona Afraz, Henk Akse, Kamelia.V.K. Boodhoo, Rene Bos, Judith Cantin, Yi Wai (Emily) Chiang, Jean-Marc Commenge, Jean-Luc Dubois, Federico Galli, Jean Paul Gueneau de Mussy, Jan Harmsen, Siddharth Kalra, Frerich J. Keil, Ruben Morales-Menendez, Francisco J. Navarro-Brull, Timothy Noël, Kim Ogden, Gregory S. Patience, David Reay, Rafael M. Santos, Ashley Smith-Schoettker, Andrzej I. Stankiewicz, Henk van den Berg, Tom van Gerven, Jeroen van Gestel, Michiel van der Stelt, Mark van de Ven, R.S. Weber Education for Chemical Engineers, 2020
Process intensification education contributes to sustainable development goals. Part 2 David Fernandez Rivas, Daria C. Boffito, Jimmy Faria-Albanese, Jarka Glassey, Judith Cantin, Nona Afraz, Henk Akse, Kamelia V.K. Boodhoo, Rene Bos, Yi Wai Chiang, Jean-Marc Commenge, Jean-Luc Dubois, Federico Galli, Jan Harmsen, Siddharth Kalra, Fred Keil, Ruben Morales-Menendez, Francisco J. Navarro-Brull, Timothy Noël, Kim Ogden, Gregory S. Patience, David Reay, Rafael M. Santos, Ashley Smith-Schoettker, Andrzej I. Stankiewicz, Henk van den Berg, Tom van Gerven, Jeroen van Gestel, R.S. Weber Education for Chemical Engineers, 2020
Advanced Learning Assistant System (ALAS) for engineering education M.A. Ramirez-Moreno, M. Diaz-Padilla, K.D. Valenzuela-Gomez, A. Vargas-Martinez, A. Roman-Flores, R. Morales-Menendez, R. Ramirez-Mendoza, J.J Lozoya-Santos IEEE Global Engineering Education Conference Educon, 2020
Self-Balancing Robot Control Optimization Using PSO Efrain Mendez-Flores, E. Mauricio Martinez-Galicia, Jorge de J. Lozoya-Santos, Ricardo Ramirez-Mendoza, Ruben Morales-Menendez, Israel Macias-Hidalgo, Adriana Vargas-Martinez, Arturo Molina-Gutierrez 2020 5th International Conference on Control and Robotics Engineering Iccre 2020, 2020
Research path that improves student engagement Ruben Morales-Menendez, Francisco J. Cantu Ortiz, Nathalie Galeano Ramirez, James Fangmeyer, Ana Marcela Hernandez de Menendez IEEE Global Engineering Education Conference Educon, 2019
Knowledge generation in higher education institutions Jorge de J. Lozoya-Santos, Brenda Edith Guajardo-Leal, Adriana Vargas-Martinez, Indira Elisa Molina-Gaytan, Armando Roman-Flores, Ricardo Ramirez-Mendoza, Ruben Morales-Menendez IEEE Global Engineering Education Conference Educon, 2019
Design, Implementation and Nonlinear Control Analysis of a Furuta Pendulum System Efrain Mendez-Flores, Juan R. Lopez-Gutierrez, Israel Macias-Hidalgo, Miguel de J. Ramirez-Cadena, Adriana Vargas-Martinez, Jorge de J. Lozoya-Santos, Ricardo Ramirez-Mendoza, Ruben Morales-Menendez, Arturo Molina-Gutierrez 4th International Conference on Control and Robotics Engineering Iccre 2019, 2019
Digital twin model proposal for an automotive chassis Proceedings of 30th International Conference on Adaptive Structures and Technologies Icast 2019, 2019
Smart elastomers for the sensing of force and vibration: A proof of concept Proceedings of 30th International Conference on Adaptive Structures and Technologies Icast 2019, 2019
Social collaboration software for virtual teams: case studies Pedro Orta-Castañon, Pedro Urbina-Coronado, Horacio Ahuett-Garza, Marcela Hernández-de-Menéndez, Ruben Morales-Menendez International Journal on Interactive Design and Manufacturing, 2018
Fault estimation methods for semi-active suspension systems Diana Hernandez-Alcantara, Ruben Morales-Menendez, Luis Amezquita-Brooks, Olivier Sename, Luc Dugard 2015 IEEE International Autumn Meeting on Power Electronics and Computing Ropec 2015, 2016
Modeling of Dampers guided by their Characteristic Diagrams Jorge de-J. Lozoya-Santos, Diana Hernández-Alcantara, Ruben Morales-Menendez, Ricardo A. Ramírez-Mendoza Riai Revista Iberoamericana De Automatica E Informatica Industrial, 2015
Plug and play with a QoV model: A research based learning approach Csedu 2015 7th International Conference on Computer Supported Education Proceedings, 2015
State observers for semi-active suspensions: Experimental results Diana Hernandez-Alcantara, Juan C. Tudon-Martinez, Luis Amezquita-Brooks, Carlos Vivas-Lopez, Ruben Morales-Menendez 2014 IEEE Conference on Control Applications Cca Part of 2014 IEEE Multi Conference on Systems and Control Msc 2014, 2014
Experimental ANN-based modeling of an adjustable damper Juan C Tudon-Martinez, Ruben Morales-Menendez, Ricardo Ramirez-Mendoza, Luis Garza-Castanon Proceedings of the International Joint Conference on Neural Networks, 2014
IMC-based control system on a MR damper Jorge de-J. Lozoya-Santos, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza 2009 European Control Conference Ecc 2009, 2014
Inove: A testbench for the analysis and control of automotive vertical dynamics Proceedings of the Mini Conference on Vehicle System Dynamics Identification and Anomalies, 2014
Fault detection for automotive semi-active dampers Diana Hernandez-Alcantara, Luis Amezquita-Brooks, Carlos Vivas-Lopez, Ruben Morales-Menendez, Ricardo Ramirez-Mendoza Conference on Control and Fault Tolerant Systems Systol, 2013
8th SAFEPROCESS 2012: Special section Jan Lunze, Cristina Verde Rodarte, Ruben Morales-Menendez, Arturo Molina Gutierrez, Ricardo Ramirez-Mendoza, Carlos Astorga-Zaragoza Annual Reviews in Control, 2013
Review on global chassis control Carlos A. Vivas-López, Diana Hernández-Alcantara, Juan C. Tudón-Martínez, Ruben Morales-Menendez IFAC Proceedings Volumes IFAC Papersonline, 2013
MR damper identification using ANN based on 1-sensor a tool for semiactive suspension control compliance Ijcci 2012 Proceedings of the 4th International Joint Conference on Computational Intelligence, 2012
Magnetorheological damper - An experimental study Jorge de-J Lozoya-Santos, Ruben Morales-Menendez, Ricardo Ramirez-Mendoza, Juan C Tudón-Martinez, Olivier Sename, Luc Dugard Journal of Intelligent Material Systems and Structures, 2012
Developing research skills in undergraduate students through an internship program in research and innovation International Journal of Engineering Education, 2012
Efficiency of on-off semiactive suspensions in a pick-up truck SAE Technical Papers, 2012
Fault Diagnosis based on DPCA and CA Celina Rea, Rubén Morales-Menéndez, Juan C. Tudón-Martínez, Ricardo A. Ramírez-Mendoza, Luis E. Garza-Castañón Computer Aided Chemical Engineering, 2012
Model-free controller for a pick-up semi-active suspension system Juan C. Tudón-Martínez, Jorge de Jesús Lozoya-Santos, Carlos A. Vivas, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza ASME International Mechanical Engineering Congress and Exposition Proceedings Imece, 2012
Fault detection for an automotive MR damper Jorge Lozoya-Santos, Juan C. Tudon-Mart nez, Ruben Morales-Menendez, Ricardo Ram rez-Mendoza, Arturo Molina Gutierrez IFAC Proceedings Volumes IFAC Papersonline, 2012
Hardware-in-The-loop testing of on-off controllers in semi-Active suspension systems Proceedings of the Mini Conference on Vehicle System Dynamics Identification and Anomalies, 2012
A research-based learning approach for undergraduate students: The internship program in research and innovation model Csedu 2011 Proceedings of the 3rd International Conference on Computer Supported Education, 2011
Fuzzy-based electric current dependency on an MR damper model Javier A. Ruiz-Cabrera, Ruben Morales-Menendez, Luis E. Garza-Castanon, Ricardo A. Ramirez-Mendoza, Olivier Sename 2011 19th Mediterranean Conference on Control and Automation MED 2011, 2011
LPV-based MR damper modelling Vicente A. Diaz-Salas, Ruben Morales-Menendez, Ricardo Ramirez-Mendoza, Olivier Sename, Luc Dugard 2011 19th Mediterranean Conference on Control and Automation MED 2011, 2011
Comparison of MR damper models Javier A. Ruiz-Cabrera, Vicente A. Diaz-Salas, Ruben Morales-Menendez, Luis Garza-Castanon, Ricardo A. Ramirez-Mendoza 18th Mediterranean Conference on Control and Automation MED 10 Conference Proceedings, 2010
MR-damper based control system Jorge Lozoya-Santos, Ruben Morales-Menendez, Ricardo Ramirez-Mendoza Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, 2009
Fault detection and diagnosis in a heat exchanger Icinco 2009 6th International Conference on Informatics in Control Automation and Robotics Proceedings, 2009
Editorial Arturo Molina, Rubén Morales, Miguel Ramírez Annual Reviews in Control, 2009
Leaks detection in a pipeline using artificial neural networks Ignacio Barradas, Luis E. Garza, Ruben Morales-Menendez, Adriana Vargas-Martínez Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Building training patterns for modelling MR dampers Icinco 2009 6th International Conference on Informatics in Control Automation and Robotics Proceedings, 2009
Time delay in controller area network (CAN)-based networked control systems Transactions of the North American Manufacturing Research Institution of SME, 2009
Design of experiments for MR damper modelling Jorge Lozoya-Santos, Ruben Morales-Menendez, Ricardo Ramirez-Mendoza Proceedings of the International Joint Conference on Neural Networks, 2009
Control strategy for ride improvement Ricardo Prado, Ricardo A. Ramirez Mendoza, Ruben Morales Menendez International Journal of Vehicle Autonomous Systems, 2009
Frequency and current effects in a MR damper Jorge de J. Lozoya Santos, Ruben Morales Menendez, Ricardo A. Ramirez Mendoza, Elvira Nino Juarez International Journal of Vehicle Autonomous Systems, 2009
Multi-leak diagnosis in pipelines - A comparison of approaches C. Verde, R. Morales-Menendez, L. E. Garza, A. Vargas, P. Velasquez-Roug, C. Rea, C. T. Aparicio, J. O. De la Fuente 7th Mexican International Conference on Artificial Intelligence Proceedings of the Special Session Micai 2008, 2008
An educational framework based on collaborative reverse engineering and active learning: A case study International Journal of Engineering Education, 2008
Learning discrete probabilistic models for application in multiple faults detection Icinco 2008 Proceedings of the 5th International Conference on Informatics in Control Automation and Robotics, 2008
A Human Machine Interface for teaching continuous control systems Proceedings of the IASTED International Conference on Modelling Identification and Control Mic, 2008
Minimizing the frequency effect in a black box model of a magneto-rheological damper Proceedings of the Mini Conference on Vehicle System Dynamics Identification and Anomalies, 2008
Product life-cycle management tools and collaborative tools applied to an automotive case study International Journal of Engineering Education, 2008
Drag-and-drop graphical user interface for process control education ASEE Annual Conference and Exposition Conference Proceedings, 2008
An intelligent hybrid decision support algorithm for cutting tool replacement in high performance machining operations Proceedings of the IASTED International Conference on Artificial Intelligence and Applications Aia 2008, 2008
AI approaches for cutting tool diagnosis in machining processes Proceedings of the IASTED International Conference on Artificial Intelligence and Applications Aia 2007, 2007
Pattern recognition approaches for diagnosis of cutting tool wear condition Transactions of the North American Manufacturing Research Institution of SME, 2007
Multi sensor data fusion for high speed machining Antonio Vallejo, Ruben Morales-Menendez, Miguel Ramírez, J. R. Alique, Luis E. Garza Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
Neural networks and statistical based models for surface roughness prediction Proceedings of the IASTED International Conference on Modelling Identification and Control Mic, 2006
Learning and using Bayesian networks for diagnosis and user profiling Association for Information Systems 12th Americas Conference on Information Systems Amcis 2006, 2006
Optimal sampling for feature extraction in iris recognition systems Luis E. Garza Castañon, Saul Montes de Oca, Rubén Morales-Menéndez Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006
An application of random and hammersley sampling methods to iris recognition Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006
Feature characterization in iris recognition with stochastic autoregressive models Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006
A Bayesian Reasoning Framework for on-line business information systems Association for Information Systems 11th Americas Conference on Information Systems Amcis 2005 A Conference on A Human Scale, 2005
Enabling intelligent organizations: An electronic institutions approach for controlling and executing problem solving methods Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
Tool-wear monitoring based on continuous hidden markov models Antonio G. Vallejo, Juan A. Nolazco-Flores, Rubén Morales-Menéndez, L. Enrique Sucar, Ciro A. Rodríguez Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
Neural nets modelling for automotive welding process Proceedings of the IASTED International Conference on Modelling and Simulation, 2005
Comparison of servocontrol and regulatory approaches based on linear quadratic optimal control for continuous glucose control in diabetic patients Proceedings of the IASTED International Conference on Modelling Identification and Control Mic, 2005
A mini-baja active suspension modelling using ADAMS/CAR® Proceedings of the IASTED International Conference on Modelling and Simulation, 2005
A fault detection approach based on machine learning models Luis E. Garza Castañon, Francisco J. Cantú Ortiz, Rubén Morales-Menéndez, Ricardo Ramírez Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
Sensor-fusion system for monitoring a CNC-milling center Rubén Morales-Menéndez, M. Sheyla Aguilar, Ciro A. Rodríguez, Federico Guedea Elizalde, Luis E. Garza Castañon Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
Fault diagnosis in mobile robots using particle filtering algorithms Proceedings of the IASTED International Conference on Intelligent Systems and Control, 2004
Towards autonomous robotic systems using wrapper components Proceedings of the IASTED International Conference on Robotics and Applications, 2004
Dynamic modelling of processes using jump markov linear gaussian models Proceedings of the IASTED International Conference on Modelling Identification and Control, 2004
Estimation and control of industrial processes with particle £lters Proceedings of the American Control Conference, 2003
Real-time monitoring of complex industrial processes with particle filters Advances in Neural Information Processing Systems, 2003
Real-Time Monitoring of Complex Industrial Processes with Particle Filters Nips 2002 Proceedings of the 15th International Conference on Neural Information Processing Systems, 2002
