Model of a Predictive Neural Network for Determining the Electric Fields of Training Flight Phases

Authors: Michalowska J.

Year: 2024

Category: Neural Networks

Journal: Energies

DOI: 10.3390/en17010126

URL: https://www.mdpi.com/1996-1073/17/1/126

Abstract

Overview

Research involved testing the electromagnetic field (EMF) around various training aircrafts using a predictive neural network model. The study aimed to optimize flight routes by predicting the intensity of the electric component of EMF.

Methodology

  • Measurements were conducted on four different aircraft using Microrad's NHT3DL meter and a 01E probe.
  • A neural network, specifically using LSTM layers, was trained to predict the EMF data to facilitate safer and environmentally friendly flight routes.

Findings

The predictive model displayed superior accuracy compared to traditional methods, providing precise estimations of the electric component of EMF along trained routes. This model also supports enhanced data analysis and electromagnetic compatibility.

Conclusion

This approach could significantly contribute to improving reliability in general aviation, promoting environmental protection, and adhering to aviation safety standards.

neural network electromagnetic fields aviation LSTM environmental monitoring
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