An Unsupervised Long- and Short-Term Sparse Graph Neural Network for Multisensor Anomaly Detection-Reference-Cited by-同舟云学术

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An Unsupervised Long- and Short-Term Sparse Graph Neural Network for Multisensor Anomaly Detection

Published:2024-07-15 Issue:14 Volume:24 Page:23088-23097
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Miao Qiucheng¹^ORCID,Wang Dandan²,Xu Chuanfu³^ORCID,Zhan Jun³,Wu Chengkun³

Affiliation:

1. Center for High Performance Computing, Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, China

2. Huawei Technologies Company Ltd., Shenzhen, China

3. Institute for Quantum Information and State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology (NUDT), Changsha, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

National Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7361/10599846/10547103.pdf?arnumber=10547103

Reference25 articles.

1. Concurrent Fault Diagnosis Based on Bayesian Discriminating Analysis and Time Series Analysis With Dimensionless Parameters

2. A New Wavelet Shrinkage Approach for Denoising Nonlinear Time Series and Improving Bearing Fault Diagnosis

3. A Transformer-based Framework for Multivariate Time Series Representation Learning

4. Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series

5. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

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