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

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  • Joanna Michalowska

    (The Institute of Technical Sciences and Aviation, The University College of Applied Science in Chelm, Pocztowa 54, 22-100 Chełm, Poland)

Abstract

Tests on the content of the electrical component of the electromagnetic field (EMF) were carried out with an NHT3DL broadband meter by Microrad using a 01E (100 kHz ÷ 6.5 GHz) measuring probe. Measurements were made during training flights (Cessna C172, Cessna C152, Aero AT3, and Technam P2006T aircrafts). A neural network was used, the task of which was to learn to predict the successive values of average ( E RMS ) and instantaneous ( E PEAK ) electromagnetic fields used here. Such a solution would make it possible to determine the most favorable routes for all aircrafts. This article presents a model of an artificial neural network which aims to predict the intensity of the electrical component of the electromagnetic field. In order to create the developed model, that is, to create a training sequence for the model, a series of measurements was carried out on four types of aircraft (Cessna C172, Cessna C152, Aero AT3, and Technam P2006T). The model was based on long short-term memory (LSTM) layers. The tests carried out showed that the accuracy of the model was higher than that of the reference method. The developed model was able to estimate the electrical component for the vicinity of the routes on which it was trained in order to optimize the exposure of the aircraft to the electrical component of the electromagnetic field. In addition, it allowed for data analysis of the same training flight routes. The reference point for the obtained electric energy results were the normative limits of the electromagnetic field that may affect the crew and passengers during a flight. Monitoring and measuring the electromagnetic field generated by devices is important from an environmental point of view, as well as for the purposes of human body protection and electromagnetic compatibility. In order to improve reliability in general aviation and to adapt to the proposed requirements, aviation training centers are obliged to introduce systems for supervising and analyzing flight parameters.

Suggested Citation

  • Joanna Michalowska, 2023. "Model of a Predictive Neural Network for Determining the Electric Fields of Training Flight Phases," Energies, MDPI, vol. 17(1), pages 1-27, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:126-:d:1307321
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    References listed on IDEAS

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    1. K. R. Sri Preethaa & Akila Muthuramalingam & Yuvaraj Natarajan & Gitanjali Wadhwa & Ahmed Abdi Yusuf Ali, 2023. "A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern," Sustainability, MDPI, vol. 15(17), pages 1-22, August.
    2. Zhiyong Chang & Yunmeng Jiao & Xiaojing Wang, 2023. "Influencing the Variable Selection and Prediction of Carbon Emissions in China," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
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