A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies
Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper...
Saved in:
Main Authors: | Yakun Wang, Jianglei Gong, Jie Zhang, Xiaodong Han |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | International Journal of Aerospace Engineering |
Online Access: | http://dx.doi.org/10.1155/2022/1676933 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Unsupervised learning trajectory anomaly detection algorithm based on deep representation
by: Zhongqiu Wang, et al.
Published: (2020-12-01) -
Fake Detect: A Deep Learning Ensemble Model for Fake News Detection
by: Nida Aslam, et al.
Published: (2021-01-01) -
A Three-Stage Anomaly Detection Framework for Traffic Videos
by: Junzhou Chen, et al.
Published: (2022-01-01) -
Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments
by: Hanan Abbas Mohammad
Published: (2024-12-01) -
Anomaly Monitoring Method for Key Components of Satellite
by: Jian Peng, et al.
Published: (2014-01-01)