Work

Using Autoencoders to Classify EMC Problems in Electronic System Development

Year: 2024

Type: article

Abstract: This paper is a direct continuation of “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development” (Maalouly et al., 2022). The previous paper aimed to classify the electromagnetic compatibility (EMC) problem classification, while this paper addresses two primary issues: the data and the technique. The technique used in the previous study involved a principal component analysis (PCA) (Pearson, 1901) to generate input features for the neural network. However, since PCA only encodes linear relations from the samples, autoencoder (AE) models are now used to encode the data into a latent vector that better represents the data. The latent vectors will ultimately be used as input to classify the EMC problems. A neural network and a random forest classifier were utilized to develop a classification model, wherein the random forest demonstrated superior performance in comparison to the neural network.

Source: Advances in radio science

DOI: 10.5194/ars-22-53-2024

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

Institutions: Fraunhofer Institute for Applied Information Technology, Fraunhofer Institute for Electronic Nano Systems, Hamm-Lippstadt University of Applied Sciences

Language: English

Topic: Electromagnetic Compatibility and Noise Suppression

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $226

Funder: Bundesministerium für Wirtschaft und Klimaschutz