基于DNN的托卡马克等离子体边界重建研究

Boundary Reconstruction of Tokamak Plasma Based on Deep Neural Networks

摘要:
目的为了实现托卡马克在放电过程中对等离子体位形的实时重建，在对相机标定算法分析的基础上，提出一种基于全连接神经网络的可见光边缘重建算法。
方法该算法的作用是建立像素坐标系和托卡马克坐标系的对应关系进而实现等离子体可见光边缘重建。
结果在该算法的基础上引入小样本学习，来对全连接神经网络重建算法做进一步改进。
结论实验结果表明该算法可以精确地对等离子体可见光边缘进行重建，同时也满足系统对实时性的要求。

Abstract:
Introduction In order to realize the real-time reconstruction of plasma shape and position in tokamak, a visible light edge reconstruction algorithm based on fully connected neural network is proposed based on the analysis of camera calibration algorithm.

Method The function of the algorithm was to establish the corresponding relationship between the pixel coordinate system and the tokamak coordinate system, and then realize the plasma visible light edge reconstruction.
Result On the basis of the algorithm, few-shot learning is added to further improve the reconstruction algorithm of fully connected neural network.

Conclusion Experimental results show that the algorithm can accurately reconstruct the plasma visible light edge, and also meet the real-time requirements of the system.