Next Article in Journal
Influence of Temperature Variation on the Vibrational Characteristics of Fused Silica Cylindrical Resonators for Coriolis Vibratory Gyroscopes
Previous Article in Journal
Giant Goos-Hänchen Shifts in Au-ITO-TMDCs-Graphene Heterostructure and Its Potential for High Performance Sensor
Previous Article in Special Issue
Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods
Open AccessArticle

Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods

1
School of Physics and Technology, Wuhan University, Wuhan 430072, China
2
Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(4), 1030; https://doi.org/10.3390/s20041030
Received: 15 January 2020 / Revised: 11 February 2020 / Accepted: 12 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Artificial Intelligence and Sensors)
Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning. View Full-Text
Keywords: VLF/LF lightning waveform; automatic classification; deep learning; convolutional neural network (CNN) VLF/LF lightning waveform; automatic classification; deep learning; convolutional neural network (CNN)
Show Figures

Figure 1

MDPI and ACS Style

Wang, J.; Huang, Q.; Ma, Q.; Chang, S.; He, J.; Wang, H.; Zhou, X.; Xiao, F.; Gao, C. Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods. Sensors 2020, 20, 1030.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop