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Published on: May 13, 2020
Generic neural network model for estimating exposure levels in industrial environments.
David Plets1, Christos Apostolidis2, Peter Gajšek3
1Ghent University-imec-WAVES, Tech Lane Science Park 126, 9052 Ghent, Belgium.
Summary
This study presents a user-friendly neural network model to estimate radio frequency exposure levels in industrial settings. The accessible method simplifies exposure assessment for workers, enhancing workplace safety.
Area of Science:
- Electromagnetics and Computational Physics
- Artificial Intelligence and Machine Learning
- Occupational Health and Safety
Background:
- Estimating radio frequency (RF) exposure in industrial environments is complex, often requiring specialized expertise and software.
- Existing methods for RF exposure assessment can be inaccessible to non-experts and on-site workers.
- There is a need for simplified, reliable tools to assess RF exposure in diverse industrial settings.
Purpose of the Study:
- To develop and validate a neural network-based method for estimating RF exposure levels (E50 and E95) in industrial environments.
- To create an accessible tool usable by laymen and industrial workers without requiring detailed technical inputs.
- To demonstrate the feasibility of accurate RF exposure prediction using a simplified, broadly accessible approach.
Main Methods:
- A simulation pipeline using Blender and MATLAB ray-tracing was employed to generate over 20,000 wireless configurations.
- Eleven input parameters, including transmit power, clutter density, and transmitter location/height, were varied.
- A multi-layer fully connected neural network regression model was developed and trained on simulated data.
Main Results:
- Correlation analysis identified transmit power, clutter density, transmitter density, and transmitter location/height as key influencing factors.
- The neural network model achieved high prediction accuracy on unseen data (RMSE < 0.173 V/m, R² > 95%).
- Validation with real-world measurements in three industrial environments showed an average absolute deviation of 20.4%.
Conclusions:
- The developed neural network model reliably estimates RF exposure levels in industrial environments with high accuracy.
- This novel approach offers a broadly accessible solution for RF exposure assessment, reducing reliance on experts and complex software.
- The method empowers industrial workers and layman personnel to understand and manage RF exposure risks effectively.
