A fault evolution knowledge-driven adversarial meta-learning method for few-shot tool state recognition under variable working conditions
City University of Hong Kong · Nanjing University of Science and Technology · +1 more institution
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Abstract
No abstract available for this paper.
Citation impact
4
total citations
- FWCI
- 48.70
- Percentile
- 99%
- References
- 40
Too recent for citation history.
Authors
4- CYChen Yin
City University of Hong Kong, Nanjing University of Science and Technology
- YDYining DongCorresponding
City University of Hong Kong
- JHJianliang He
Henan Normal University
- YWYulin WangCorresponding
Nanjing University of Science and Technology
Topics & keywords
Topics
Keywords
- Adversarial system
- Fault (geology)
- Domain knowledge
- Domain (mathematical analysis)
- Feature (linguistics)
- State (computer science)
- Deep learning
- Condition monitoring
No related works found for this paper.
Funding
- NNNational Natural Science Foundation of ChinaAwards: 62502150, 22322816
- NMNational Major Science and Technology Projects of ChinaAward: 2024ZD0713801
- HPHenan Provincial Science and Technology Research ProjectAward: 252102221039
- BABasic and Applied Basic Research Foundation of Guangdong ProvinceAward: 2023A1515110533