A review of the most recent advances in deep learning (DL) as applied to electromagnetics
(EM), antennas, and propagation is provided. It is aimed at giving the interested readers and …

The electrified railway system has been rapidly rolled out in many countries and regions in
the past decades, along with the transportation decarbonization agenda. The latest …

Although neural networks have been applied in many fields since they were first introduced,
the feasibility of applying it to predict the solution of Maxwell's equations remains open. In …

Rapid increase in operating frequency of integrated chips and intricacy of electronic
packages outpaces the ability of conventional methods in coping with the growing …

Data-driven predictive models for daily electricity consumption of academic buildings Page 1
AIMS Energy, 8(5): 783–801. DOI: 10.3934/energy.2020.5.783 Received: 27 May 2020 …

With the growing use of embedded systems in our daily lives and the increasing
electromagnetic noise level in the environment in which these systems are exposed, the …

Being able to predict radiated emissions before using an accredited laboratory can be both
time-effective and cost-effective. This study presents a model for predicting radiated …

This article presents a decision-making algorithm based on a modified version of the
Bellman equation to deal with electromagnetic interference errors in a communication …

Electromagnetic interference (EMI) is the presence of unwanted electromagnetic emission
which has the potential to cause disturbances in electronic and electronic devices …

In this paper, a prediction system has been developed using machine learning techniques to
obtain the conduction emission levels ensure they remain below the limit values specified in …