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An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and

Jinhua Guo1,2,3, Jiaquan Wang2, Fang Xiao2

  • 1School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

A new lightning convolutional stack autoencoder (LCSAE) model effectively compresses lightning electromagnetic pulse (LEMP) data. This method significantly improves data transmission efficiency for lightning sensor networks, achieving high waveform reconstruction accuracy.

Keywords:
autoencoderconvolutional neural networkdeep learningfeature compressionlightning

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Atmospheric Science

Background:

  • Technological advancements enable real-time collection of lightning electromagnetic pulse (LEMP) signals using VLF/LF instruments.
  • Efficient data storage and transmission are critical for processing large volumes of lightning data.

Purpose of the Study:

  • To design and evaluate a novel deep learning model for compressing LEMP waveform data.
  • To enhance the efficiency of remote data transmission from lightning sensors.

Main Methods:

  • Development of a Lightning Convolutional Stack Autoencoder (LCSAE) model.
  • Utilizing an encoder to generate low-dimensional feature vectors and a decoder for waveform reconstruction.
  • Investigating compression performance across various compression ratios.

Main Results:

  • The LCSAE model demonstrates effective compression of LEMP data.
  • Compression performance correlates positively with the neural network's minimum feature extraction.
  • An average R-squared value of 96.7% was achieved for waveform reconstruction when the minimum feature was 64.

Conclusions:

  • The LCSAE model offers a viable solution for compressing LEMP signals.
  • This approach significantly improves the efficiency of remote data transmission for lightning monitoring systems.