Deep Learning (DL) components are increasing their presence in safety and mission-critical software systems. To ensure a high dependability of DL systems, robust verification methods are required, for which automation is ...

Accurate temperature estimation is essential for ensuring the reliability and performance of power semiconductor devices. This paper presents various techniques used in the industry and focuses on a comparative analysis ...

Significant efforts have been dedicated to optimising the performance of battery systems by improving energy management and battery sizing strategies. Recent advancements have shifted their focus on model-based optimisations ...

Federated Learning (FL) is an emerging machine learning paradigm that enables multiple parties to collaboratively train models without sharing raw data, ensuring data privacy. In Vertical FL (VFL), where each party holds ...

High-power-density converters concentrate heat in smaller areas of printed circuit boards (PCBs), a challenge exacerbated by Gallium Nitride (GaN)-based components in surface-mount device (SMD) packages. While these ...

Cyber-Physical Systems (CPSs) combine digital cyber technologies with physical processes. As in any other software system, in the case of CPSs, the use of Artificial Intelligence (AI) techniques in general, and Deep Neural ...

Linear switched-flux machines are a very promising technology thanks to their high thrust density and permanent magnet immunity. However, they usually have to deal with a high detent force and a high thrust force ripple. ...

A common problem that arises when facing classification tasks is the class imbalance problem, which happens when one or more classes are heavily underrepresented compared to the rest, being usually those minority classes ...

Permanent Magnets (PM) containing Rare Earth Elements (REE) are widely used in the automotive industry to achieve power-dense and efficient motor designs. However, their supply chain is highly vulnerable and subject to ...