Tesis Leídas
Curso 2025-2026
TÍTULO: Integración del dato y análisis predictivo de procesos en fábrica digital-industria 4.0
- AUTOR: Manuel José Rodriguez Aguilar
- DIRECTOR: Ismael Abad Cardiel / José Antonio Cerrada Somolinos
- PUBLICACIONES
- M. J. Rodríguez Aguilar, I. Abad Cardiel and J. A. Cerrada, “IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines” Applied Sciences, Volume 14, 2023, 323.
https://doi.org/10.3390/app14010323.
- M. J. Rodríguez Aguilar, I. Abad Cardiel and J. A. Cerrada, “IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines” Applied Sciences, Volume 14, 2023, 323.
- FECHA LECTURA: 09/12/2025
- PROGRAMA: RD 99/2011
TÍTULO: Gamificación e Inteligencia Artificial como Herramientas para la Formación en Procesos Logísticos
- AUTOR: Juan José Romero Marras
- DIRECTOR: Luis de la Torre / Dictino Chaos García
- PUBLICACIONES
- J. J. Romero marras, L. de la Torre and D. Chaos García «WarehouseGame Training: A Gamified Logistics Training Platform Integrating ChatGPT, DeepSeek, and Grok for Adaptive Learning,» in Applied Sciences, vol. 15, 6392, 2025. 10.3390/app15126392.
- FECHA LECTURA: 13/04/2026
- PROGRAMA: RD 99/2011
TÍTULO: Análisis forense digital en sistemas de control industrial basado en técnicas de aprendizaje automático
- AUTOR: Francisco Javier Alonso Villalobos
- DIRECTOR: Sebastián Dormido Canto / Gonzalo Farias Castro
- PUBLICACIONES
- Francisco Alonso, Benjamín Samaniego, Gonzalo Farias and Sebastián Dormido-Canto, «Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector», Applied Sciences, 14(2964), 2024.
https://doi.org/10.3390/app14072964
- Francisco Alonso, Benjamín Samaniego, Gonzalo Farias and Sebastián Dormido-Canto, «Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector», Applied Sciences, 14(2964), 2024.
- FECHA LECTURA: 17/06/2026
- PROGRAMA: RD 99/2011
TÍTULO: An integrated explainable machine learning framework for demand-side flexibility characterization based on smart meter analytics and price responsiveness
- AUTOR: Santiago Bañales López
- DIRECTOR: Raquel Dormido Canto / Natividad Duro Carralero
- PUBLICACIONES
- S. Bañales, R. Dormido, N. Duro, «Smart Meters Time Series Clustering for Demand response Applications in the Context of High Penetration of Renewable Energy Resources», Applied Science, vol. 14, 2021, pp 3457-3469. https://doi.org/10.3390/en14123458
- Bañales, S., Dormido, R., Duro, N. (2025). Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers. Computer Modeling in Engineering & Sciences, 142(1), 869–907. Doi: 10.32604/cmes.2024.054946
- Bañales, S., Dormido, R., Duro, N. (2026). Explainable AI for predicting household demand flexibility: Insights from smart meter data and price-based programs. Energyand AI, 24, 100686. doi: 10.1016/j.egyai.2026.100686
- Bañales S, Dormido R, Duro N. A model-based smart meters time series decomposition approach for demand flexibility characterization of SMEs and households. Energy Reports. 2026;15:109153. doi: 10.1016/j.egyr.2026.109153
- FECHA LECTURA: 22/06/2026
- PROGRAMA: RD 99/2011