Juan A. Gomez-Pulido's picture
Real name: 
Juan A. Gomez-Pulido
First Name: 
Juan A.
Last Name: 
Gomez-Pulido
Affiliation: 
Universidad de Extremadura
Job Title: 
Full Professor
Expertise: 
Optimization. Machine Learning. Reconfigurable Computing.
External links: 
Personal webpage
Short Bio: 
Juan A. Gomez-Pulido received the Ph.D. degree from the Complutense University of Madrid, Spain, in 1993. He is currently full professor of design of processors and hardware systems in the Department of Technology of Computers and Communications, University of Extremadura, Spain. For 27 years he has participated in more than two dozens of R+D+i european and national projects researching in intelligent systems, machine learning, optimization, metaheuristics, evolutionary computing, and reconfigurable and embedded computing, among others. He is member of 50-program committee, organizing and editorial boards, 3 research groups and 5 scientific societies and networks. He is IEEE Senior Member and chair of IEEE Spain SIGHT (Special Interest Group on Humanitarian Technology). He has authored or co-authored 80 ISI journals, 100 book chapters, and around 300 peer-reviewed conference proceedings, as well as edited 15 books and journals special issues. He has conducted 6 PhD thesis, 34 degree and 14 master final projects.

Datasets & Competitions

Industrial Internet of Things embedded devices fault detection and classification. A case study

Industries transition to the Industry 4.0 paradigm requires solutions based on devices attached to machines that allow monitoring and control of industrial equipment. Monitoring is essential to ensure devices' proper operation against different aggressions. We propose a novel approach to detect and classify faults, that are typical in these devices, based on machine learning techniques that use as features the energy, the processing, and the time consumed by device main application functionality.

Categories:
IoT
892 Views

Sensor readings for ambient control in academic facilities

Academic spaces are an environment that promotes student performance not only because of the quality of its equipment, but also because of its ambient comfort conditions, which can be controlled by means of actuators that receive data from sensors. Something similar can be said about other environments, such as home, business, or industry environment. However, sensor devices can cause faults or inaccurate readings in a timely manner, affecting control mechanisms. The mutual relationship between ambient variables can be a source of knowledge to predict a variable in case a sensor fails.

Categories:
Artificial Intelligence
Machine Learning
Sensors
487 Views