<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.本研究通过深度融合数据驱动方法与物理机理模型，创新性地重构了自动化设备电磁兼容性（Electromagnetic Compatibility, EMC）分析的技术范式。生成式对抗网络（Generative Adversarial Networks, 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层面的三重局限，推动电磁兼容性设计从被动防护迈入主动预测的全新阶段。