Density limit disruption prediction using a long short-term memory network on EAST
文献类型：期刊论文
| 作者 | Zhang, Kai1; Chen, Dalong2 ; Guo, Bihao2; Chen, Junjie1; Xiao, Bingjia2
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| 刊名 | PLASMA SCIENCE & TECHNOLOGY
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| 出版日期 | 2020-11-01 |
| 卷号 | 22 |
| 关键词 | disruption prediction tokamak neural networks flattop phase |
| ISSN号 | 1009-0630 |
| DOI | 10.1088/2058-6272/abb28f |
| 通讯作者 | Chen, Dalong(cdalong@ipp.ac.cn) |
| 英文摘要 | Disruption prediction using a long short-term memory (LSTM) algorithm has been developed on EAST, due to its inherent advantages in time series data processing. In the present work, LSTM is used as the model and the AUC (area under receiver operation characteristic curve) is used as the evaluation index. When the model is trained on data from the plasma current flattop phase and tested on data from the same period multiple times, the highest AUC is 0.8646 and the training time is about 6900 s per epoch. For comparison, the last 1000 ms of the flattop phases are intercepted as short time sequences. When the model is trained on data from short time sequences and tested on data from the same period, the highest AUC is increased to 0.9379 and the training time is restricted to 36 s per epoch. When the best model trained on the short time sequences is applied to the flattop phase for testing, the AUC is up to 0.9189. The experiment results show that it is possible for LSTM to train the model on data from short time sequences and migrate the model to the entire flattop phase, with a shorter training time and higher AUC value. |
| WOS关键词 | GENETIC ALGORITHMS ; FEATURE-SELECTION ; JET ; DATABASES |
| 资助项目 | National Magnetic Confinement Fusion Energy R&D Program of China[2018YFE0304100] ; National Magnetic Confinement Fusion Energy R&D Program of China[2018YFE0302100] ; Anhui Provincial Natural Science Foundation[1808085MA25] |
| WOS研究方向 | Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000573296700001 |
| 出版者 | IOP PUBLISHING LTD |
| 资助机构 | National Magnetic Confinement Fusion Energy R&D Program of China ; Anhui Provincial Natural Science Foundation |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/104383]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Chen, Dalong |
| 作者单位 | 1.Univ Sci & Technol China, Sch Phys Sci, Dept Engn & Appl Phys, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Inst Plasma Phys, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Kai,Chen, Dalong,Guo, Bihao,et al. Density limit disruption prediction using a long short-term memory network on EAST[J]. PLASMA SCIENCE & TECHNOLOGY,2020,22. |
| APA | Zhang, Kai,Chen, Dalong,Guo, Bihao,Chen, Junjie,&Xiao, Bingjia.(2020).Density limit disruption prediction using a long short-term memory network on EAST.PLASMA SCIENCE & TECHNOLOGY,22. |
| MLA | Zhang, Kai,et al."Density limit disruption prediction using a long short-term memory network on EAST".PLASMA SCIENCE & TECHNOLOGY 22(2020). |
入库方式： OAI收割
来源：合肥物质科学研究院
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