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The number of data accumulated by controllable nuclear fusion devices is too large, and a large number of data have not been fully exploited. In such big data processing machine learning can play an important role. Therefore, in this work the spectral clustering method is used to realize the automatic processing of data, which can easily and quickly find the pattern information contained in the data. The discovery of these patterns is of great significance in improving plasma confinement and understanding plasma physics. In addition, in this work the spectral clustering method is applied to the electron cyclotron emission imaging (ECEI), one-dimensional diagnostic system electron cyclotron emissiometer, magnetic probe, soft X-ray, fast radiation (fast bolometer) and other different diagnostic systems on the EAST tokamak device. The sawtooth pattern is identified, the migration of the spectral clustering method is verified, and the problems of poor data processing migration in supervised learning and the need to rely on a large number of labeled data are solved. Finally, in this work, the ECEI and magnetic probe data are used to discover a possible new mode in the time domain and frequency domain respectively, which provides a new idea for exploring new modes.
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Keywords:
- plasma diagnostics /
- spectral clustering /
- pattern recognition /
- coherent mode
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Zhu Y 2019 M. S. Thesis (Hefei: University of Science and Technology of China) (in Chinese)
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Google Scholar
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Zhao Z L 2017 Ph. D. Dissertation (Hefei: University of Science and Technology of China) (in Chinese)
[6] 徐明 2011 博士学位论文 (合肥: 中国科学技术大学)
Xu M 2011 Ph. D. Dissertation (Hefei: University of Science and Technology of China) (in Chinese)
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Google Scholar
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Google Scholar
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Google Scholar
[10] Gaudio P, Murari A, Gelfusa M, Lupelli I, Vega J 2014 Plasma Phys. Control. Fusion 56 114002
Google Scholar
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Google Scholar
[13] Hartigan J A, Wong M A 1979 J. R. Stat. Soc. Ser. C-Appl. Stat. 28 100
Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
[19] Gao B X, Xie J L, Mao Z, et al. 2018 J. Instrum. 13 P02009
Google Scholar
[20] Gao B X 2013 Ph. D. Dissertation (Hefei: University of Science and Technology of China) (in Chinese) [高炳西 2013 博士学位论文(合肥: 中国科学技术大学)]
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Google Scholar
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图 1 谱聚类算法流程图
Figure 1. Flow chart of spectral clustering algorithm.
图 2 聚类识别分类结果
Figure 2. Cluster recognition classification results.
图 3 各信道的信号时序图 (a) 信道A; (b) 信道B; (c) 信道C
Figure 3. Signal timing diagram of the different channel: (a) Channel A; (b) channel B; (c) channel C.
图 5 对于50015炮, 模式频率特征
Figure 5. Mode frequency characteristics for shot 50015.
图 6 聚类识别结果以及模式实际观测到的位置 (a) 聚类识别结果; (b) 模式实际出现的位置; (c) 模式在托卡马克中的位置
Figure 6. Cluster recognition results and the position where the pattern is actually observed: (a) Cluster recognition results; (b) the position where the pattern actually appears; (c) the position of the pattern in the Tokamak.
图 7 对于64960炮, 模式频率特征
Figure 7. Mode frequency characteristics for shot 64960.
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[1] 朱玉 2019 硕士学位论文 (合肥: 中国科学技术大学)
Zhu Y 2019 M. S. Thesis (Hefei: University of Science and Technology of China) (in Chinese)
[2] Boom J E, Wolfrum E, Classen I G J, et al. 2012 Nucl. Fusion 52 114004
Google Scholar
[3] Wesson J A 1986 Plasma Phys. Control. Fusion 28 243
Google Scholar
[4] Zhao Z L, Xie J L, Qu C M, Liao W, Li H, Lan T, Liu A D, Zhuang G, Liu W D 2017 Radiat. Eff. Defects Solids 172 760
Google Scholar
[5] 赵朕领 2017 博士学位论文 (合肥: 中国科学技术大学)
Zhao Z L 2017 Ph. D. Dissertation (Hefei: University of Science and Technology of China) (in Chinese)
[6] 徐明 2011 博士学位论文 (合肥: 中国科学技术大学)
Xu M 2011 Ph. D. Dissertation (Hefei: University of Science and Technology of China) (in Chinese)
[7] Park H K, Mazzucato E, Luhmann N C, et al. 2006 Phys. Plasmas 13 055907
Google Scholar
[8] Yun G S, Lee W, Choi M J, et al. 2011 Phys. Rev. Lett 107 045004
Google Scholar
[9] Tobias B J, Classen I G J, Domier C W, et al. 2011 Phys. Rev. Lett. 106 075003
Google Scholar
[10] Gaudio P, Murari A, Gelfusa M, Lupelli I, Vega J 2014 Plasma Phys. Control. Fusion 56 114002
Google Scholar
[11] Arena P, Basile A, Fortuna L, Mazzitelli G, Rizzo A, Zammataro M 2004 IEEE International Symposium on Circuits and Systems Vancouver, BC, Canada, May 23—26, 2004 p77
[12] Gonzalez S, Vega J, Murari A, Pereira A, Ramirez J M, Dormido-Canto S 2010 Rev. Sci. Instrum. 81 10E123
Google Scholar
[13] Hartigan J A, Wong M A 1979 J. R. Stat. Soc. Ser. C-Appl. Stat. 28 100
Google Scholar
[14] Tian Z, Ramakrishnan R, Livny M 1996 Sigmod. Rec. 25 103
Google Scholar
[15] von Luxburg U 2007 Stat. Comput. 17 395
Google Scholar
[16] Shi J B, Malik J 2000 IEEE Trans. Pattern Anal. Mach. Intell. 22 888
Google Scholar
[17] Nam Y B, Park H K, Lee W, Yun G S, Kim M, Sabot R, Elbeze D, Lotte P, Shen J 2016 Rev. Sci. Instrum. 87 11E135
Google Scholar
[18] Deng B H, Domier C W, Luhmann N C, et al. 2001 Rev. Sci. Instrum. 72 301
Google Scholar
[19] Gao B X, Xie J L, Mao Z, et al. 2018 J. Instrum. 13 P02009
Google Scholar
[20] Gao B X 2013 Ph. D. Dissertation (Hefei: University of Science and Technology of China) (in Chinese) [高炳西 2013 博士学位论文(合肥: 中国科学技术大学)]
[21] Drake J F, Lee Y C 1977 Phys. Fluids 20 1341
Google Scholar
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