Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering
Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse dis...
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MDPI AG
2023-08-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/8/1296 |
| _version_ | 1827730412711968768 |
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| author | Ying Han Yalan Li Jing Yuan Jianping Huang Xuhui Shen Zhong Li Li Ma Yanxia Zhang Xinfang Chen Yali Wang |
| author_facet | Ying Han Yalan Li Jing Yuan Jianping Huang Xuhui Shen Zhong Li Li Ma Yanxia Zhang Xinfang Chen Yali Wang |
| author_sort | Ying Han |
| collection | DOAJ |
| description | Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation. |
| first_indexed | 2024-03-11T00:07:52Z |
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| id | doaj.art-cb6942918e8c48b2b4b391ea7b5305b3 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T00:07:52Z |
| publishDate | 2023-08-01 |
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| series | Atmosphere |
| spelling | doaj.art-cb6942918e8c48b2b4b391ea7b5305b32023-11-19T00:13:25ZengMDPI AGAtmosphere2073-44332023-08-01148129610.3390/atmos14081296Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised ClusteringYing Han0Yalan Li1Jing Yuan2Jianping Huang3Xuhui Shen4Zhong Li5Li Ma6Yanxia Zhang7Xinfang Chen8Yali Wang9Institute of Disaster Prevention, Sanhe 065421, ChinaMicroelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, ChinaInstitute of Disaster Prevention, Sanhe 065421, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100085, ChinaInstitute of Disaster Prevention, Sanhe 065421, ChinaFaculty of Arts, Hebei University of Economics and Business, Shijiazhuang 050061, ChinaInstitute of Disaster Prevention, Sanhe 065421, ChinaInstitute of Disaster Prevention, Sanhe 065421, ChinaInstitute of Disaster Prevention, Sanhe 065421, ChinaPulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation.https://www.mdpi.com/2073-4433/14/8/1296China Seismo-Electromagnetic Satellite (CSES)very low frequency (VLF)vertical-line pulse train (VLPT)k-means + +automatic recognition |
| spellingShingle | Ying Han Yalan Li Jing Yuan Jianping Huang Xuhui Shen Zhong Li Li Ma Yanxia Zhang Xinfang Chen Yali Wang Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering Atmosphere China Seismo-Electromagnetic Satellite (CSES) very low frequency (VLF) vertical-line pulse train (VLPT) k-means + + automatic recognition |
| title | Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering |
| title_full | Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering |
| title_fullStr | Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering |
| title_full_unstemmed | Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering |
| title_short | Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering |
| title_sort | automatic recognition of vertical line pulse train from china seismo electromagnetic satellite based on unsupervised clustering |
| topic | China Seismo-Electromagnetic Satellite (CSES) very low frequency (VLF) vertical-line pulse train (VLPT) k-means + + automatic recognition |
| url | https://www.mdpi.com/2073-4433/14/8/1296 |
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