Design and optimization of a soft X-ray tomography system on Keda Torus eXperiment

Zu, Yiming; Mao, Wenzhe; Lan, Tao; Ding, Weixing; Zhuang, G.; Zhang, Sen; Xu, Henghui; Zhu, Jiawei; Xie, Jinlin; Li, Hong; Liu, Adi; Zhang, Shoubiao; Zhou, Chu; Liu, Zixi; Wei, Zhuangzhuang; Wu, Zhengwei; Xiao, C.; Liu, Wandong

doi:10.1016/j.fusengdes.2020.112174

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Over the past few decades, machine learning has been developed and applied to solve complex problems in engineering and science. It also has a wide application in the field of nuclear fusion, such as disruption prediction [15][16][17][18][19], plasma control [20][21][22][23], equilibrium reconstruction [24,25], and parameter inversion by Bayesian inference [26][27][28][29]. In this study, the support vector machine (SVM) [30] was introduced into the fringe jump detection and correction of electron density data from the POINT diagnostics system [31] in the EAST.…”

Section: Introductionmentioning

confidence: 99%

Phase jump detection and correction based on the support vector machine

Wang

Hanada²,

Sakurai

et al. 2023

Plasma Phys. Control. Fusion

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In general, interferometers are used to perform electron density measurements in magnetically confined plasma, where the electron density is dependent on the refractive index of the plasma. Measurements can be made through comparisons of the phase shift variation between the probe and reference laser beam. The plasma electron density should vary continuously during discharge; however, the fringe jump is a step-like change of the apparent electron density caused by a sudden jump of the measured phase shift. The appearance of fringe jump will degrade the interferometric measurements accuracy. This study attempted to solve the fringe jump problem on the polarimeter-interferometer system of the Experiment Advanced Superconducting Tokamak by proposing a support vector machine model for electron density fringe jump correction. The established model can efficiently classify the fringe jump data from the raw measurement data in a manner robust to noise and interference, and subsequently correct the jump. This model greatly improves the correction efficiency and precision of electron density data from the polarimeter-interferometer diagnostics system (POINT) in the Experiment Advanced Superconducting Tokamak (EAST), and is expected to be embedded into the plasma control system to perform more accurate real-time electron density feedback control. Moreover, the algorithm is not limited to specific fusion devices or interferometer diagnostics, and is applicable to other interferometric measurement systems.

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Section: Introductionmentioning

confidence: 99%

Phase jump detection and correction based on the support vector machine

Wang

Hanada²,

Sakurai

et al. 2023

Plasma Phys. Control. Fusion

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“…have been widely applied to fusion data processing,. Bayesian inference and a fully connected (FC) neural network have been applied to parameter inversion [19][20][21][22][23][24]. In this paper, convolutional neural networks (CNNs) are introduced into the density profile reconstruction of interferometers.…”

Section: Introductionmentioning

confidence: 99%

Electron density profile reconstruction with convolutional neural networks

Lan¹,

Liu²,

Ren³

et al. 2022

Plasma Phys. Control. Fusion

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Convolutional Neural Networks (CNN) are introduced into reconstructing electron density proﬁles from the line-integrated density measurements of inter-ferometers in EAST tokamak. Diagnostic data from the POlarimeter/INTerferometer (POINT) and the HCN interferometer diagnostic systems are integrated to improve the reconstruction performance. By training and optimization with unreliable measurements in the dataset, the robustness of this algorithm is enhanced. The established model can predict the probability distribution of density proﬁles accurately, fast, and robustly to noise and interference. This algorithm is not restricted to speciﬁc equilibrium conﬁgurations and can be transferred easily between diﬀerent fusion devices.

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