| Citation: |
Jianchao LI, Xiaoqing ZHANG, Yu ZHANG, Abba Alhaji BALA, Huiping LIU, Guohong ZHOU, Nengchao WANG, Da LI, Zhongyong CHEN, Zhoujun YANG, Zhipeng CHEN, Jiaolong DONG, Yonghua DING, the J-TEXT Team. Investigation of the J-TEXT plasma events by k-means clustering algorithm[J]. Plasma Science and Technology, 2023, 25(8): 085103. DOI: 10.1088/2058-6272/acc3d1
|
Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals, which can be applied to identify these events. A semi-supervised machine learning algorithm, the k-means clustering algorithm, is utilized to investigate and identify plasma events in the J-TEXT plasma. This method can cluster diverse plasma events with homogeneous features, and then these events can be identified if given few manually labeled examples based on physical understanding. A survey of clustered events reveals that the k-means algorithm can make plasma events (rotating tearing mode, sawtooth oscillations, and locked mode) gathering in Euclidean space composed of multi-dimensional diagnostic data, like soft x-ray emission intensity, edge toroidal rotation velocity, the Mirnov signal amplitude and so on. Based on the cluster analysis results, an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma. The cluster analysis method is conducive to data markers of massive diagnostic data.
The authors are very grateful for the help of the J-TEXT team. This work was supported by the National Magnetic Confinement Fusion Science Program of China (Nos. 2018YFE0301104 and 2018YFE0301100), and National Natural Science Foundation of China (Nos. 12075096 and 51821005).
| [1] |
Yang Z et al 2020 Nucl. Fusion 60 016017 doi: 10.1088/1741-4326/ab4b6f
|
| [2] |
Hu W H et al 2021 Nucl. Fusion 61 066034 doi: 10.1088/1741-4326/abf74d
|
| [3] |
Zhu J et al 2021 Nucl. Fusion 61 114005 doi: 10.1088/1741-4326/ac28ae
|
| [4] |
Liu Z J et al 2021 Fusion Eng. Des. 172 112722 doi: 10.1016/j.fusengdes.2021.112722
|
| [5] |
Meneghini O et al 2021 Nucl. Fusion 61 026006 doi: 10.1088/1741-4326/abb918
|
| [6] |
Dong G et al 2021 Nucl. Fusion 61 126061 doi: 10.1088/1741-4326/ac32f1
|
| [7] |
Montes K J et al 2021 Nucl. Fusion 61 026022 doi: 10.1088/1741-4326/abcdb9
|
| [8] |
Smith D R et al 2016 Plasma Phys. Control. Fusion 58 045003 doi: 10.1088/0741-3335/58/4/045003
|
| [9] |
Lloyd S 1982 IEEE Trans. Inform. Theory 28 129 doi: 10.1109/TIT.1982.1056489
|
| [10] |
Liang Y et al 2019 Nucl. Fusion 59 112016 doi: 10.1088/1741-4326/ab1a72
|
| [11] |
Li J C et al 2014 Rev. Sci. Instrum. 85 11E414 doi: 10.1063/1.4886432
|
| [12] |
Luo J et al 2014 Rev. Sci. Instrum. 85 11E427 doi: 10.1063/1.4892898
|
| [13] |
Guo D J et al 2017 Rev. Sci. Instrum. 88 123502 doi: 10.1063/1.4996360
|
| [14] |
Gao L et al 2012 Rev. Sci. Instrum. 83 10E303 doi: 10.1063/1.4728310
|
| [15] |
Rao B et al 2014 Fusion Eng. Des. 89 378 doi: 10.1016/j.fusengdes.2014.03.038
|
| [16] |
Dunn J C 1973 J. Cybernet. 3 32 doi: 10.1080/01969727308546046
|
| [17] |
Dong J L et al 2021 Plasma Sci. Technol. 23 085101 doi: 10.1088/2058-6272/ac0685
|
| [18] |
Li J C et al 2017 Plasma Phys. Control. Fusion 59 085005 doi: 10.1088/1361-6587/aa726c
|
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