电气与电子信息学院学术报告——Virus Machines for Power System fault diagnosis

作者:电气与电子信息学院来源:电气与电子信息学院发布时间:2025-09-16浏览次数:11

一、报告题目:

Virus Machines for Power System fault diagnosis

二、报人:Dr. Antonio Ramírez de Arellano Marrero(Spain) 

三、报告时间:2025917日(本周下午14:30

四、报告地点:6A301

五、主办单位:西华大学电气与电子信息学院

六、报告人简介:

Antonio Ramírez-de-Arellano received his B.Sc. degree in 2020, his M.Sc. degree in 2021, and his Ph.D. degree in 2024 at the University of Seville, Spain. He is a Horizon Europe Marie Curie Postdoctoral Fellow at the Research Institute for Artificial Intelligence, Romanian Academy (Bucharest, Romania).

He was a lecturer at the University of Seville from 2022 to 2025. He was in a 3 month fellowship at Xihua University in 2023, and one month in 2024. His research interests are unconventional computing (especially natural computing), computational complexity theory,  cryptography and artificial intelligence. Despite his short career, He has more than 20 papers in high-ranked journals with top international researchers, including Xihua University professors such as: Prof. Hong Peng, Prof. Jun Wang and Prof. Tao Wang, among others. He has been awarded with the Star of Scientific research by the First Biocomputing Summer School by the Zhejiang Lab in Hangzhou 2023, and the Best Paper Award at the CMC2023 conference in Trieste, Italy.

七、报告内容简介:

Recently, Power system fault diagnosis has reached several bottlenecks, such as effectiveness for special cases and efficiency. In this talk we present virus machines (VM). VM are a young unconventional computing paradigm inspired by the biological viruses life cycle, where the hosts work as memory units that store viruses; how those viruses are replicated and propagated between the hosts will define the computation of the device. This lecture will show the behavior of basic VM and will present some applications of some extensions of VM in different fields, focusing on the last works related to power systems fault diagnosis. Opening a new perspective for power system fault diagnosis within virus machines.

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