IEEE Open Journal of the Computer Society
The paper “Hyperbolic Deep Learning System for Intelligent Gate-Driving and Health Monitoring of SiC MOSFETs”
has been accepted for publication in the IEEE Open Journal of the Computer Society (Impact Factor 8.2 — Q1 in Computer Science (misc)). 
Our contribution was driven by a central key-technical pillar: Hyperbolic deep learning combined with Lipschitz-controlled layers. This approach allows us to build geometry-aware, stable, and continually adaptable models capable of learning even under domain shifts and evolving operating conditions. 
We applied this novel backbone to the world of power electronics: Physics-aware modeling and intelligent health monitoring of power devices. By analyzing standard gate-driving data, our system can track device degradation and support reliable operation of Silicon/Silicon-Carbide MOSFETs, while remaining light-weight enough for real-time, embedded deployment. 
Authors: Francesco Rundo, Ph.D., Angelo Alberto Messina, Michele Fiore, Michele Calabretta, Pasquale Coscia, Sebastiano Battiato
Read the IEEE OJCS – Early Access version here: https://lnkd.in/d9-qyynRThis work is part of ongoing research led by AI4Industry@IPLAB, University of Catania, in partnership with industrial players such as STMicroelectronics, where we push the frontiers of deep learning on un-conventional spaces and bio-inspired models for next-generation industrial applications.
Stay tuned! 