An Optimal Unsupervised Domain Adaptation Approach With Applications to Pipeline Fault Diagnosis: Balancing Invariance and Variance-Reference-Cited by-同舟云学术

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An Optimal Unsupervised Domain Adaptation Approach With Applications to Pipeline Fault Diagnosis: Balancing Invariance and Variance

Published:2024-08 Issue:8 Volume:20 Page:10019-10030
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Wang Chuang¹^ORCID,Wang Zidong²^ORCID,Liu Hongjian³^ORCID,Dong Hongli¹^ORCID,Lu Guoping⁴^ORCID

Affiliation:

1. Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya, China

2. Department of Computer Science, Brunel University London, Uxbridge, U.K.

3. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu, China

4. School of Electrical Engineering, Nantong University, Nantong, China

Funder

National Natural Science Foundation of China

Hainan Province Science and Technology Special Fund of China

AHPU High-End-Equipment Intelligent Control Innovation Team

Alexander Von Humboldt Foundation of Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10623532/10517281.pdf?arnumber=10517281

Reference48 articles.

1. Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation;Chen,2019

2. Nearest neighbor pattern classification

3. A survey on hidden Markov jump systems: asynchronous control and filtering

4. A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization

5. Domain-Adversarial Training of Neural Networks

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