中文版 | English
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
Author
Corresponding AuthorYe ShengYi
Publication Years
2023-02-01
DOI
Source Title
ISSN
0001-5733
Volume66Issue: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
SUSTech Authorship
First ; Corresponding
WOS Research Area
Geochemistry & Geophysics
WOS Subject
Geochemistry & Geophysics
WOS Accession No
WOS:000934497300003
Publisher
ESI Research Field
GEOSCIENCES
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/513393
DepartmentDepartment of Earth and Space Sciences
Affiliation
Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Peoples R China
First Author AffilicationDepartment of Earth and Space Sciences
Corresponding Author AffilicationDepartment of Earth and Space Sciences
First Author's First AffilicationDepartment 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).
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).
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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