Disruption prediction based on fusion feature extractor on J-TEXT Article Swipe
Wei Zheng
,
Fengming Xue
,
Zhongyong Chen
,
Chengshuo Shen
,
Xinkun Ai
,
Yu Zhong
,
Nengchao Wang
,
Ming Zhang
,
Yonghua Ding
,
Zhipeng Chen
,
Zhoujun Yang
,
Yuan Pan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1674-1056/acc7fc
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1674-1056/acc7fc
Predicting disruptions across different tokamaks is necessary for next generation device. Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge, which makes it difficult for current data-driven methods to obtain an acceptable result. A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem. The key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data, and can be easily transferred to other tokamaks. Based on the concerns above, this paper presents a deep feature extractor, namely, the fusion feature extractor (FFE), which is designed specifically for extracting disruption precursor features from common diagnostics on tokamaks. Furthermore, an FFE-based disruption predictor on J-TEXT is demonstrated. The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics. Strong inductive bias on tokamak diagnostics data is introduced. The paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks, as well as a physics-based feature extraction with a traditional machine learning method. Results demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction, and obtain a better result compared with other deep learning methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1674-1056/acc7fc
- OA Status
- hybrid
- Cited By
- 1
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361216555
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4361216555Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1674-1056/acc7fcDigital Object Identifier
- Title
-
Disruption prediction based on fusion feature extractor on J-TEXTWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-28Full publication date if available
- Authors
-
Wei Zheng, Fengming Xue, Zhongyong Chen, Chengshuo Shen, Xinkun Ai, Yu Zhong, Nengchao Wang, Ming Zhang, Yonghua Ding, Zhipeng Chen, Zhoujun Yang, Yuan PanList of authors in order
- Landing page
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https://doi.org/10.1088/1674-1056/acc7fcPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1674-1056/acc7fcDirect OA link when available
- Concepts
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Tokamak, Extractor, Feature (linguistics), Artificial intelligence, Computer science, Artificial neural network, Feature extraction, Machine learning, Nuclear fusion, Deep learning, Pattern recognition (psychology), Physics, Plasma, Engineering, Nuclear physics, Linguistics, Process engineering, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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- Related works (count)
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10Other works algorithmically related by OpenAlex
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