Hyeongmeen Baik

Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa et al.

Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal processing from physics enforcement: a three-layer leaky integrate-and-fire SNN estimates passive component parameters while a differentiable ODE solver provides physics-consistent training by decoupling the ODE physics loss from the unrolled spiking loop. On an EMI-corrupted synchronous buck converter benchmark, the SNN reduces lumped resistance error from $25.8\%$ to $10.2\%$ versus a feedforward baseline, within the $\pm 10\%$ manufacturing tolerance of passive components, at a projected ${\sim}270\times$ energy reduction on neuromorphic hardware. Persistent membrane states further enable degradation tracking and event-driven fault detection via a $+5.5$ percentage-point spike-rate jump at abrupt faults. With $93\%$ spike sparsity, the architecture is suited for always-on deployment on Intel Loihi 2 or BrainChip Akida.

Hyeongmeen Baik, Jinia Roy

This paper presents a comprehensive review of dielectric barrier discharge (DBD) power supply topologies, aiming to bridge the gap between DBD applications and power electronics design. Two key aspects are examined: the dependence of the DBD electrical model on reactor geometry, and application-driven requirements for injected waveform characteristics, including shapes, voltage amplitude, frequency, and modulation techniques. On this basis, the paper systematically reviews two major categories of power supplies: sinusoidal types comprising transformerless and transformer-based resonant inverters, and pulsed power supplies (PPSs). The review summarizes performance trade-offs, highlights untested topologies and emerging applications, and offers guidance for advancing high-performance DBD power supply design for next-generation systems.