Research on electromagnetic compatibility analysis of automation equipment based on generative adversarial networks and pulse sparse convolution
Wenrui Ding, Deren Feng
TL;DR
This paper introduces a new framework using GANs and pulse sparse convolution to improve EMI analysis in high-speed industrial systems.
Contribution
A novel pulse-aware GAN framework with FPGA implementation for real-time, energy-efficient EMI analysis in industrial automation.
Findings
The framework achieves a Fréchet distance of 0.72 and a global difference metric of 0.18 ± 0.03 on an industrial EMC dataset.
It reduces pulse-width and phase prediction errors by over 40% compared to classical numerical solvers.
The FPGA implementation enables deterministic inference at 5 GS/s with 0.71 W power consumption.
Abstract
Electromagnetic interference (EMI) analysis in high-speed industrial systems is increasingly challenged by multi-gigahertz sampling rates, complex transient behaviors, and stringent real-time constraints. To address these challenges, this paper proposes a pulse-aware generative and analysis framework based on a generative adversarial network (GAN) combined with pulse sparse convolution using leaky integrate-and-fire (LIF) spiking neurons. A multi-scale discriminator and gradient penalty stabilization are employed to improve waveform generation fidelity, achieving a Fréchet distance (FID) of 0.72 and a global difference metric (GDM) of 0.18 ± 0.03 on an industrial-grade Electromagnetic compatibility (EMC) dataset. The proposed framework is further applied to crosstalk prediction, where it reduces pulse-width and phase prediction errors by more than 40% compared with classical numerical…
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Taxonomy
TopicsElectromagnetic Compatibility and Noise Suppression · Lightning and Electromagnetic Phenomena · Power Line Communications and Noise