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

2024 · Unclear · Insufficient evidence

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Electric-field measurements were collected during training flights on four aircraft types using a broadband meter/probe (100 kHz–6.5 GHz). An LSTM-based neural network model was developed to predict successive values of ERMS and EPEAK; the reported model accu…

Study snapshot

Publicationmanual · 2024
Study typeengineering
Reported directionUnclear
Evidence ratingInsufficient

Abstract

Model of a Predictive Neural Network for Determining the Electric Fields of Training Flight Phases Michalowska J. Model of a Predictive Neural Network for Determining the Electric Fields of Training Flight Phases. Energies. 2024; 17(1):126. doi: 10.3390/en17010126. 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 (ERMS) and instantaneous (EPEAK) 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. Open access paper: mdpi.com

Machine Enhanced Logic

AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeengineering
Effect directionunclear
PopulationCrew and passengers during training flights (general aviation aircraft)
Sample sizeNot supplied
ExposureNot supplied
Evidence strengthInsufficientConfidence: 74% · Peer reviewed: yes

Main findings

Electric-field measurements were collected during training flights on four aircraft types using a broadband meter/probe (100 kHz–6.5 GHz). An LSTM-based neural network model was developed to predict successive values of ERMS and EPEAK; the reported model accuracy was higher than a reference method and was used to estimate the electric-field component in the vicinity of trained routes to support exposure optimization relative to normative limits.

Outcomes measured

  • Electric field measurements (electrical component of EMF)
  • Average electric field (ERMS)
  • Peak/instantaneous electric field (EPEAK)
  • Predictive model accuracy vs reference method
  • Route-based estimation/optimization of electric-field exposure

Limitations

  • No quantitative exposure levels (e.g., ERMS/EPEAK values) reported in the abstract
  • No sample size, number of flights, or measurement duration reported in the abstract
  • Reference method not described in the abstract
  • Health outcomes not assessed; focus is measurement/modeling and comparison to normative limits
View raw extracted JSON
{
  "study_type": "engineering",
  "exposure": {
    "band": null,
    "source": "occupational (training flights/aircraft environment)",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": null
  },
  "population": "Crew and passengers during training flights (general aviation aircraft)",
  "sample_size": null,
  "outcomes": [
    "Electric field measurements (electrical component of EMF)",
    "Average electric field (ERMS)",
    "Peak/instantaneous electric field (EPEAK)",
    "Predictive model accuracy vs reference method",
    "Route-based estimation/optimization of electric-field exposure"
  ],
  "main_findings": "Electric-field measurements were collected during training flights on four aircraft types using a broadband meter/probe (100 kHz–6.5 GHz). An LSTM-based neural network model was developed to predict successive values of ERMS and EPEAK; the reported model accuracy was higher than a reference method and was used to estimate the electric-field component in the vicinity of trained routes to support exposure optimization relative to normative limits.",
  "effect_direction": "unclear",
  "limitations": [
    "No quantitative exposure levels (e.g., ERMS/EPEAK values) reported in the abstract",
    "No sample size, number of flights, or measurement duration reported in the abstract",
    "Reference method not described in the abstract",
    "Health outcomes not assessed; focus is measurement/modeling and comparison to normative limits"
  ],
  "evidence_strength": "insufficient",
  "confidence": 0.74,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "electromagnetic field",
    "electric field",
    "ERMS",
    "EPEAK",
    "broadband measurements",
    "NHT3DL",
    "Microrad",
    "01E probe",
    "100 kHz–6.5 GHz",
    "training flights",
    "general aviation",
    "Cessna C172",
    "Cessna C152",
    "Aero AT3",
    "Tecnam P2006T",
    "LSTM",
    "neural network",
    "route optimization",
    "normative limits",
    "electromagnetic compatibility"
  ],
  "suggested_hubs": [
    {
      "slug": "occupational-exposure",
      "weight": 0.78,
      "reason": "Measurements and exposure optimization are discussed in the context of crew/passengers during training flights (aviation environment)."
    }
  ]
}
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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.3390/en17010126
PubMed: Not supplied

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