Advances in Radio Science
Advances in Radio Science
ARS
Articles | Volume 22
Articles | Volume 22
https://doi.org/10.5194/ars-22-53-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ars-22-53-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22
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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Short summary
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. 
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