Maria Dolores Rodriguez Moreno
My research focuses on developing and applying state of the art techniques in Artificial Intelligence to strategic domain such as Robotics, Energy Management or Cybersecurity.
In collaboration with the Intelligent System Group we have applied state of the art techniques in Machine Learning to perform real-time monitoring of the system’s mechanical condition in Spanish Navy ships to predict when components will fail. It is the first time predictive maintenance is being used in military equipment.
We are also applying state of the art techniques on Deep Learning to predict geomagnetic indices (they measure when geomagnetic storms can occur, being responsible of failures in satellite communication or power grids) or predict energy consumption patterns.
Finally, we are developing cooperative planning techniques to advance in heuristic search planning algorithms for multi-robotic platforms.
- Armando Collado Villaverde (European Space Agency): Deep Neural Networks for Geomagnetic Forecasting.
- Hugo Álvarez Chavez (Spanish Government: Predicting hospital emergency department demand using Machine Learning and automated Planning
- Javier Caballero Testón (Spanish Government): Cooperative planning for heterogeneous robotic platforms
- Muhammad Babar (Postdoc, COFUND - EU): An Optimal Load Scheduling and Fair Pricing Mechanism Using Heuristic Optimization in Smart Grid
- José Aguilar (Postdoc, COFUND - EU): Bioinspired efficient energy management
- Carlos-Javier Hernández-Castro, María D. R-Moreno, David F. Barrero. BASECASS: A methodology for CAPTCHAs security assurance. Journal of Information Security and Applications, Volume 63, December 2021.
- F. Barrero, O. Fontenla-Romero, F. Lamas-López, D. Novoa-Paradela, María D. R-Moreno and D. Sanz. SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets. Applied Sciences, vol. 11, no. 16, 2021
- Aguilar, A. Garces-Jimenez, María D. R-Moreno and Rodrigo García. A Systematic Literature Review on the use of Artificial Intelligence in Energy Self-Management in Smart Buildings. Renewable and Sustainable Energy Reviews, vol. 151, 2021.