Articles | Volume 22
https://doi.org/10.5194/ars-22-53-2024
https://doi.org/10.5194/ars-22-53-2024
29 Nov 2024
 | 29 Nov 2024

Using Autoencoders to Classify EMC Problems in Electronic System Development

Jad Maalouly, Dennis Hemker, Christian Hedayat, Marcel Olbrich, Sven Lange, and Harald Mathis

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Adv. Radio Sci., 22, 61–75, https://doi.org/10.5194/ars-22-61-2024,https://doi.org/10.5194/ars-22-61-2024, 2024
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Cited articles

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This paper aims to classify electromagnetic compatibility (EMC) issues using autoencoders, a dimensionality reduction technique, and machine learning models. The process begins by generating EMC measurement data that closely reflects real-world measurements. The samples are then reduced using autoencoders and used as input for the machine-learning models. The results demonstrate that the machine learning techniques were able to accurately classify between the different EMC classes.