Jose Lisandro Aguilar Castro

About the research project «Autonomic Energy Management Platform based on Data for Smart Buildings»

Research line: Bioinpired efficient energy management

Mentor: Profa. Mª Dolores Rodríguez Moreno (UAH)

Abstract

In recent years, we have developed the concept of “Autonomic Cycles of Big Data Analysis Tasks as a service”, in order to provide autonomic management solutions in different contexts. Particularly, an Autonomic Cycle integrates different machine learning methods, in order to develop a type of autonomous intelligent supervision that allows reaching strategic objectives around a given problem. The autonomic cycles integrate a set of Data Analysis tasks to generate knowledge, which autonomously and collectively work to achieve the strategic objectives pursued by these cycles. These tasks interact with each other, and they have different roles in the cycle: Observing the process, analysing and interpreting what happens in it, and making decisions about the process that allow reaching the objective for which the cycle was designed. This project will define a suit of autonomic cycles, for the general management of the energy used in a building, which considers among other functionalities, the supervision, the optimization, and the control, of the energy. This framework can consider different aspects, like the energy cost, the user comfort, the energy fault tolerance, etc., and must be able be used in any building. In addition, during the project will be developed some of the Data Analysis tasks using novel approaches of soft-computing based on the knowledge as a service (KaaS) approach and new Artificial Intelligence techniques. Finally, the project will propose a general framework (middleware) for the deployment of the autonomic cycles.

About Jose Lisandro Aguilar Castro

Jose Aguilar received the B. S. degree in System Engineering in 1987 (Universidad de los Andes-Venezuela), the M. Sc. degree in Computer Sciences in 1991 (Universite Paul Sabatier-France), and the Ph.D degree in Computer Sciences in 1995 (Universite Rene Descartes-France). He was a Postdoctoral Research Fellow in the Department of Computer Sciences at the University of Houston (1999-2000) and in the Laboratoire d’Analyse et d’Architecture des Systems, France (2010-2011). He is a Titular Professor in the Department of Computer Science at the Universidad de los Andes, Mérida, Venezuela, and contracted professor of the Department of Systems Engineering of the EAFIT University, Medellin, Colombia. Currently, he is a Postdoctoral Research Fellow (GOT ENERGY TALENT fellowship programme of the EU) in the Department of Automatica at the Universidad de Alcala (2020-2022). He is member of the Mérida Science Academy and was member of the IEEE CIS Technical Committee on Neural Networks (2010-2018). Also, he was Coordinator of the Applied Science Doctoral Program, Faculty of Engineering, Universidad de Los Andes and was Prometeo Researcher at the Escuela Politécnica Nacional, Universidad Técnica Particular de Loja and Yachay-EP, Quito, since October 2014 until July 2017. He has published more than 600 papers and 9 books, in the field of parallel and distributed systems, computational intelligence, and science and technology management, among other areas. Dr. Aguilar has been a visiting research/professor in more than 10 universities and laboratories, has been the coordinator or inviting research in more than 30 research or industrial projects, and has supervised more than 50 M. S. and Doctoral students in their thesis.

ORCID ID: orcid.org/0000-0003-4194-6882 

Google Scholar: https://goo.gl/eQu5JB

Some of the recent publications (see www.ing.ula.ve/~aguilar for more details):

• Morales, J. Aguilar, A. Rosales, D. Chávez, P. Leica, “Modeling and control of nonlinear systems using an Adaptive LAMDA approach”, Applied Soft Computing, 2020
• Ouedraogo, S. Medjiah, C Chassot, K. Drira, , J. Aguilar “A Cost-Effective Approach for End-to-End QoS Management in NFV-enabled IoT Platforms”, IEEE Internet of Things Journal, 2020
• Sanchez, E. Exposito, J. Aguilar, “Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context”, Computers in Industry, Vol. 121, 2020
• Morales, J. Aguilar, «An automatic merge technique to improve the clustering quality performed by LAMDA»,  IEEE Access, vol. 8, pp. 162917-162944, 2020
• Perez-García, F. Rivas, L. Rujano, A. Pinto, J. Torres, J. Aguilar, J. Rengel, J. Vélez-Zapata, “New modeling for prediction of the tropospheric scintillation in satellite links”, Electronics Letters, Vol. 56, No. 11, pp. 577–579, 2020.
• Cordero, J. Aguilar, K. Aguilar, D. Chávez, E. Puerto. “Recognition of the Driving Style in Vehicle Drivers”. Sensors, MDPI, Vol. 20, No. 9, 2020.
• Mendonça, N. Perozo, J. Aguilar, “Ontological Emergence Scheme in Self-Organized and Emerging Systems“. Advanced Engineering Informatics, vol. 44, 2020.
• Morales, J. Aguilar, D. Chávez, C. Isaza “LAMDA-HAD, an extension to the LAMDA classifier in the context of supervised learning”, International Journal of Information Technology & Decision Making, World Scientific, Vol. 19, No. 1, pp. 283-316, 2020.
• Sánchez, J. Aguilar, O. Terán, J. Gutiérrez, “Modeling the process of shaping the Public Opinion through Multilevel Fuzzy Cognitive Maps”, Applied Soft Computing, 2019.
• Pacheco, E. Exposito, M. Gineste, C. Baudoin, J. Aguilar, «Towards the deployment of Machine Learning solutions in traffic network classification: A systematic survey», IEEE Communications Surveys and Tutorials, Vol. 21, No. 2, pp. 1988-2014, 2019.
• Aguilar, A. Garces-Jimenez, N. Gallego-Salvador, J. Gutierrez de Mesa, J. Gomez-Pulido, A. Garcia-Tejedor,»Autonomic Management Architecture for Multi-HVAC Systems in Smart
  Buildings». IEEE Access, Vol, 7, pp. 123402 – 123415, 2019.
• Aguilar, O Buendia, A. Pinto, J. Gutierrez, «Social Learning Analytics for determining Learning Styles in a Smart Classroom», Interactive Learning Environments, 2019.
• Puerto, J. Aguilar, D. Chávez, C. López, “Using Multilayer Fuzzy Cognitive Maps to Diagnose Autism Spectrum Disorder”, Applied Soft Computing Journal, Vol. 75, pp. 58–71, 2019.
• Cerrada, J. Aguilar, J. Altamiranda, R. Sanchez, “A hybrid heuristic algorithm for evolving models in simultaneous scenarios of classification and clustering”, Knowledge and Information Systems, Vol. 58, no. 2, pp. 755-798, 2019.
• Puerto, J. Aguilar, “An Ar2p Deep Learning Architecture for the discovery and the selection of features», Neural Processing Letters, Vol. 50, No. 1, pp 623-643, 2019.