JESTIE Joint Special Section on “Application of Advanced Signal Processing, Artificial Intelligence and Machine Learning techniques to Electric Machines”
Organized by: Prof. Jose Antonino-Daviu, Prof. Thomas Wolbank, Dr. Shafigh Nategh, Prof. Radu Bojoi
The application of advanced signal processing and Artificial Intelligence (AI) techniques to electric machines has proliferated during recent years. Many different areas related to the design, modelling, control, diagnosis or optimization of electric machines, among many others, have used, in some extent, many of these recent technologies to enhance the results that years ago were not achievable with conventional methods. On the other hand, in the new era of Internet of Things (IoT), machine learning, big data analysis and deep learning methods have proliferated; they take advantage of the electric machines data to build intelligent algorithms that help to optimize and automatize diverse aspects related to these energy conversion devices. In this context, an intense research effort is developed over recent years, merging AI and other IoT-related methods.
The aim of the special section is to provide a timely opportunity for researchers, practicing engineers, and other stakeholders to share their latest discoveries related to the application of advanced artificial intelligence and machine learning techniques to electric machines.
We encourage all researchers working in this area to submit papers to this Special Section. Topics of interest include, but are not limited to:
- New signal processing algorithms applied to the control, design, monitoring and/or diagnostics of electric machines.
- Advanced AI techniques applied to electrical machines.
- Application of big data technologies to the control, design, monitoring and/or diagnostics of electric machines.
- IoT technologies applied to electric machines areas.
- Automatization of electric machines-related areas systems using modern technologies.
- Deep learning methods applied to electric machines.
- Design to digital twin-based systems adapted to electric machines applications.
- Blockchain and machine learning technologies applied to electric machines areas.
- Knowledge-driven intelligent optimization methods applied to electric machines areas.
- Educational-related applications of AI and machine learning algorithms to electric machines.