Self-Supervised Deep Clustering Method for Detecting Abnormal Data of Wastewater Treatment Process-Reference-Cited by-同舟云学术

Self-Supervised Deep Clustering Method for Detecting Abnormal Data of Wastewater Treatment Process

Published:2024-02 Issue:2 Volume:20 Page:1155-1166
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Han Honggui¹^ORCID,Sun Meiting¹,Li Fangyu¹^ORCID,Liu Zezhong²,Wang Chen²

Affiliation:

1. Faculty of Information Technology, the Beijing Key Laboratory of Computational Intelligence and Intelligent System, and the Engineering Research Center of Digital Community, Ministry of Education, Beijing University of Technology, Beijing, China

2. Beijing Aerospace Smart Manufacturing Technology Development Company Ltd, Beijing, China

Funder

National Key Research and Development

National Natural Science Foundation of China

Beijing Outstanding Young Scientist Program

Beijing Natural Science Foundation

Beijing Scholar

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/ielx7/9424/10411967/10106005.pdf?arnumber=10106005

Reference35 articles.

1. Cooperative Fuzzy-Neural Control for Wastewater Treatment Process

2. Univariate imputation method for recovering missing data in wastewater treatment process

3. MPA-RNN: A Novel Attention-Based Recurrent Neural Networks for Total Nitrogen Prediction

4. Data-Driven Hybrid Model for Forecasting Wastewater Influent Loads Based on Multimodal and Ensemble Deep Learning

5. Double-cycle weighted imputation method for wastewater treatment process data with multiple missing patterns

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