<b>Generative Adversarial Networks and Pulse Sparse Convolution for Electromagnetic Compatibility in Automated Systems</b>
<p dir="ltr">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 network...
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2025
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| Summary: | <p dir="ltr">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.</p> |
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