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

Viewed

Total article views: 157 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
114 39 4 157 8 7
  • HTML: 114
  • PDF: 39
  • XML: 4
  • Total: 157
  • BibTeX: 8
  • EndNote: 7
Views and downloads (calculated since 29 Nov 2024)
Cumulative views and downloads (calculated since 29 Nov 2024)

Viewed (geographical distribution)

Total article views: 164 (including HTML, PDF, and XML) Thereof 164 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Apr 2025
Download
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. 
Share