PCC-Trans: a time series feature selection and model framework for tokamak discharge process in EAST

Binqian Cheng; Chenguang Wan; Xiaojuan Liu; Zhi Yu; Nong Xiang

doi:10.1117/12.3034112

13 June 2024 PCC-Trans: a time series feature selection and model framework for tokamak discharge process in EAST

Binqian Cheng, Chenguang Wan, Xiaojuan Liu, Zhi Yu, Nong Xiang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801R (2024) https://doi.org/10.1117/12.3034112
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Due to the complexity, chaotic behavior, and non-linear nature of tokamak plasma dynamics, modeling tokamak discharges poses a formidable challenge. This modeling task necessitates the accurate prediction of the evolution of tokamak diagnostic signals, entailing the resolution of a sequence-to-sequence time series prediction problem. In this study, we propose a novel framework for tokamak discharge modeling, termed the PCC-Trans framework, which integrates a time series feature selection method based on the Pearson correlation coefficient (PCC) and Transformer, and enhances the predictive accuracy of the model. Specifically, PCC is employed for signals correlation analysis and the selection of relevant, non-redundant features to augment the Transformer. Through analysis and modeling of experimental data from the Experimental Advanced Superconducting Tokamak (EAST), the proposed framework demonstrates superior performance across four case studies compared to five other machine learning approaches, including Multi-layer Perceptron (MLP), Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), and Transformer. Additionally, we validate the efficacy of the feature selection method by assessing the necessity of each element within the feature set and its influence on modeling performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Binqian Cheng, Chenguang Wan, Xiaojuan Liu, Zhi Yu, and Nong Xiang "PCC-Trans: a time series feature selection and model framework for tokamak discharge process in EAST", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801R (13 June 2024); https://doi.org/10.1117/12.3034112

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KEYWORDS

Diagnostics

Feature selection

Transformers

Modeling

Correlation coefficients

Education and training

Data modeling

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