<b>Generative Adversarial Networks and Pulse Sparse Convolution for Electromagnetic Compatibility in Automated Systems</b>

This study innovatively reconstructs the technical paradigm of electromagnetic compatibility (EMC) analysis for automation equipment by deeply integrating data-driven methods with physical mechanism models. The collaborative architecture of generative adversarial networks (GANs) and pulse sparse convolution overcomes the triple limitations of traditional methods in real-time performance, generalization, and quantitative decision-making, advancing electromagnetic compatibility design from passive protection to a new stage of active prediction.本研究创新性地重构了自动化设备电磁兼容性（EMC）分析的技术范式，将数据驱动方法与物理机理模型进行深度融合。生成式对抗网络（GANs）与脉冲稀疏卷积的协同架构，克服了传统方法在实时性能、泛化能力与定量决策方面的三重局限，推动电磁兼容性设计从被动防护迈向主动预测的全新阶段。