https://hdl.handle.net/10259/9699 2026-04-18T18:05:11Z 2026-04-18T18:05:11Z Sierra Garcia, Jesús Enrique Peñacoba Yagüe, Mario Cabrera Santana, Pedro Jesús https://hdl.handle.net/10259/10223 2025-02-14T01:05:30Z 2025-02-01T00:00:00Z

XIX Simposio CEA de Control Inteligente: libro de actas Sierra Garcia, Jesús Enrique; Peñacoba Yagüe, Mario; Cabrera Santana, Pedro Jesús Al igual que en las ediciones anteriores, el XIX Simposio CEA de Control Inteligente ha tratado de mantener los objetivos propuestos por el Grupo Temático de CEA y desarrollar unas jornadas de convivencia en las que se han desarrollado actividades científicas de investigación, de formación de doctores, de relaciones con la industria y, por supuesto, actividades culturales y de relaciones sociales de todos los miembros que formamos esta comunidad científica. Este año, el lugar elegido para la celebración del Simposio ha sido la ciudad de Burgos y le ha correspondido la organización al área de Ingeniería de Sistemas y Automática. Durante los últimos años el control inteligente está demostrando ser una herramienta esencial para contribuir a solucionar los grandes retos que se nos van a plantear en el futuro. En estas actas se presentan soluciones de control inteligente aplicadas en energía, robótica, producción y docencia. Simposio presentado en: XIX Reunión anual del grupo de Control Inteligente del comité español de automática (CEA) 2024, durante los días 19-21 de junio en Burgos (España).

2025-02-01T00:00:00Z Cabeza-Lopez, Eduardo Manuel Ruiz González, Rubén Merino Gómez, Alejandro Curiel Herrera, Leticia Elena Rincón Arango, Jaime Andrés https://hdl.handle.net/10259/9761 2025-11-16T23:42:18Z 2024-11-01T00:00:00Z

A Comparison of AI-Enabled Techniques for the Detection of Attacks in IoT Devices Cabeza-Lopez, Eduardo Manuel; Ruiz González, Rubén; Merino Gómez, Alejandro; Curiel Herrera, Leticia Elena; Rincón Arango, Jaime Andrés The purpose of this research is to enhance the security of Internet of Things devices. The deployment of these gadgets has increased exponentially, nowadays they can be found in every sector from industrial environments to applications in residential homes. This technology is important for enterprises due to the ability to manage different kind of data and control critical operations in an efficiently way. Consequently, these devices have become frequent targets of cyberattacks, highlighting the necessity of robust detection methods. However it is a complicated task to find a uniform security solution due to the variety of devices, with different capabilities and requirements. This study evaluates some artificial intelligence supervised learning algorithms such as XGBoost and Random Forest using the TON_IoT dataset, which are new generations of Internet of Things and Industrial datasets for evaluating the fidelity and the efficiency of different cybersecurity applications based on Artificial Intelligence. The analysis shows both algorithms are effective in detecting cyberattacks, achieving accuracies close to 98%, with a minimal variation in terms of precision and recall. These algorithms outperform the results obtained in previous works with the same dataset, then they can provide an additional security layer, allowing accurate identification of potential attacks. This research shows the importance of artificial intelligence algorithms in cybersecurity and their potential to improve the protection of this kind of devices. Comunicación presentada en: 17th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2024) and 15th International Conference on European Transnational Education (ICEUTE 2024), 9-11 October 2024, Salamanca (Spain)

2024-11-01T00:00:00Z Ruiz González, Rubén Gómez Gil, Jaime Gómez Gil, Francisco Javier Navas-Gracia, Luis M. https://hdl.handle.net/10259/9701 2024-11-16T01:05:15Z 2017-12-01T00:00:00Z

Blind 3D localization and separation of multiple vibration and acoustic sources simultaneously active Ruiz González, Rubén; Gómez Gil, Jaime; Gómez Gil, Francisco Javier; Navas-Gracia, Luis M. Signal source localization and separation are key tasks for many applications. In this paper, a new deterministic method is proposed for estimating the 3D location and separating multiple acoustic or vibration sources, simultaneously active. The method is based on TDOA measurements obtained via crosscorrelation. Then, with the information from the estimated locations, source separation is achieved. The performance of the method was evaluated, only through simulations, in terms of accuracy and computational load. The obtained results, with SNR=6dB, showed estimation errors for the localization method always bounded below 10 centimeters, obtaining the best results with a lower number of sources and a higher number of receivers. Furthermore, the computational load was substantially reduced as compared to exhaustive search, being the gain more noticeable for a higher number of sources. The corresponding error for the separation of the original source signals was bounded below 25%, for 2 sources and 6 receivers. Thus, there are strong evidences that the here-proposed method is accurate and robust enough, while being efficient in computational terms, so many applications can benefit from its use. Comunicación presentada en: IEEE Sensors 2017. 29 October - 01 November, Glasgow, UK

2017-12-01T00:00:00Z