We are pleased to share that Prof. Jinia Roy presented our work, “Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks,” at the International Conference on Neuromorphic Systems (ICONS 2026).

Led by first author Hyeongmeen Baik, this collaborative work with Hamed Poursiami, Maryam Parsa, and Prof. Jinia Roy explores the use of spiking neural networks for parameter estimation and health monitoring in power electronic converters.

The study investigates how neuromorphic computing can support efficient and robust identification of converter parameters from measured electrical signals. By leveraging the temporal processing capabilities of spiking neural networks, the work offers a promising pathway toward real-time condition monitoring, fault detection, and intelligent maintenance of power electronic systems.

ICONS 2026 provided an excellent opportunity to connect the fields of neuromorphic computing and power electronics and to exchange ideas with researchers working on brain-inspired computing, intelligent sensing, and hardware-efficient machine learning.

We are grateful for the opportunity to share this work with the neuromorphic computing community and look forward to further advancing intelligent and reliable power conversion systems.