Comparative Study on Semi-Supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System-Reference-Cited by-同舟云学术

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Comparative Study on Semi-Supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System

Published:2023-10-01 Issue: Volume: Page:1702-1708
ISSN:
Container-title:2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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Author:

Dong Yongqi¹,Chen Kejia²,Ma Zhiyuan³

Affiliation:

1. Delft University of Technology,Department of Transport and Planning,Delft,the Netherlands

2. Zhejiang University,Department of Software Engineering,Hangzhou,China

3. Shanghai Normal University,Department of Information and Electrical Engineering,Shanghai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10391856/10393862/10394193.pdf?arnumber=10394193

Reference19 articles.

1. Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data

2. Deep Learning for Anomaly Detection

3. Anomaly Detection for IoT Time-Series Data: A Survey

4. Machine Learning Approaches for Anomaly Detection in IoT: An Overview and Future Research Directions

5. A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms

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