Title | 基于深度学习的空间尘埃碰撞实时自动检测 |
Alternative Title | Real-time automatic detection of signals triggered by space dust's impact based on deep learning
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Author | |
Corresponding Author | Ye ShengYi |
Publication Years | 2023-02-01
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DOI | |
Source Title | |
ISSN | 0001-5733
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Volume | 66Issue:2 |
Abstract | Accurate and rapid detection of dust impact events on spacecraft can help us better understand the dust distribution in the space and reduce the damage to spacecraft due to dust impacts. Although the existing methods of manual identification or machine identification of dust impact events based on the waveform characteristics of potential difference signals caused by dust impacts have high accuracy, their efficiency is low, and high-precision and automated methods are urgently needed to identify the massive potential difference signals collected by spacecraft. The deep learning model has strong ability in signal classification and recognition. In this paper, the problem of potential difference signals caused by dust impacts detection is modeled as a signal classification problem, and a convolutional neural network model is constructed, which can automatically extract signal features and classify signals according to the features. At the same time, in order to train the model and test the prediction accuracy of the model, a data set composed of potential difference signals caused by dust impacts and potential difference signals caused by other events was constructed. The accuracy rate of the model on training set is 99.46% and on the test set is 98. 68%, the recall rate is 99.44%, the precision rate is 97.95%, and the threat score is 97.41%, High-precision and automatic dust collision events detection is realized. |
Keywords | |
URL | [Source Record] |
Indexed By | |
Language | Chinese
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SUSTech Authorship | First
; Corresponding
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WOS Research Area | Geochemistry & Geophysics
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WOS Subject | Geochemistry & Geophysics
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WOS Accession No | WOS:000934497300003
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Publisher | |
ESI Research Field | GEOSCIENCES
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Data Source | Web of Science
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Citation statistics |
Cited Times [WOS]:0
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/513393 |
Department | Department of Earth and Space Sciences |
Affiliation | Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Peoples R China |
First Author Affilication | Department of Earth and Space Sciences |
Corresponding Author Affilication | Department of Earth and Space Sciences |
First Author's First Affilication | Department of Earth and Space Sciences |
Recommended Citation GB/T 7714 |
Liu RunYi,Zhu Feng,Wang Jian,等. 基于深度学习的空间尘埃碰撞实时自动检测[J]. CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION,2023,66(2).
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APA |
Liu RunYi,Zhu Feng,Wang Jian,&Ye ShengYi.(2023).基于深度学习的空间尘埃碰撞实时自动检测.CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION,66(2).
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MLA |
Liu RunYi,et al."基于深度学习的空间尘埃碰撞实时自动检测".CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION 66.2(2023).
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