A Semisupervised Approach for Industrial Anomaly Detection via Self-Adaptive Clustering-Reference-Cited by-同舟云学术

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A Semisupervised Approach for Industrial Anomaly Detection via Self-Adaptive Clustering

Published:2024-02 Issue:2 Volume:20 Page:1687-1697
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Ma Xiaoxue¹^ORCID,Keung Jacky¹^ORCID,He Pinjia²^ORCID,Xiao Yan³^ORCID,Yu Xiao⁴^ORCID,Li Yishu¹^ORCID

Affiliation:

1. Department of Computer Science, City University of Hong Kong, Hong Kong

2. School of Data Science, The Chinese University of Hong Kong, Shenzhen, China

3. School of Cyber Science and Technology, Sun Yat-Sen University, Shenzhen, China

4. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China

Funder

General Research Fund of the Research Grants Council of Hong Kong

City University of Hong Kong

other industry projects

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10411967/10138120.pdf?arnumber=10138120

Reference33 articles.

1. Integrated Generative Model for Industrial Anomaly Detection via Bi-directional LSTM and Attention Mechanism

2. Security Log Analysis in Critical Industrial Systems Exploiting Game Theoretic Feature Selection and Evidence Combination

3. LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT

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

5. Log-based Anomaly Detection Without Log Parsing

Cited by 3 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 the relative value of clustering techniques for Unsupervised Effort-Aware Defect Prediction;Expert Systems with Applications;2024-07

2. KAD: a knowledge formalization-based anomaly detection approach for distributed systems;Software Quality Journal;2024-05-10

3. Severity-Oriented Multiclass Drone Flight Logs Anomaly Detection;IEEE Access;2024

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