Robust Anomaly Detection for Multivariate Data of Spacecraft Through Recurrent Neural Networks and Extreme Value Theory-Reference-Cited by-同舟云学术

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Robust Anomaly Detection for Multivariate Data of Spacecraft Through Recurrent Neural Networks and Extreme Value Theory

Published:2021 Issue: Volume:9 Page:167447-167457
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Xiang Gang^ORCID,Lin Ruishi

Funder

National Basic Scientific Research

Equipment Pre-Research Key Laboratory Funds

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9312710/09656170.pdf?arnumber=9656170

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On-Board Anomaly Detection on a Flight-Ready System;2023 European Data Handling & Data Processing Conference (EDHPC);2023-10-02

2. Robustness Evaluation Method for Spacecraft Anomaly Detection Models;2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS);2023-09-22

3. Self-Supervised Learning Based Ship Target Classification Under Open Set Condition¹;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

4. Forecasting Maximum Mechanism Temperature in Advanced Technology Microwave Sounder (ATMS) Data Using a Long Short-Term Memory (LSTM) Neural Network;Atmosphere;2023-03-04

5. Assurance Guidance for Space Mission use of Data-Driven Machine Learning;2023 IEEE Aerospace Conference;2023-03-04

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