no code implementations • 23 Nov 2023 • Chengshuo Shen, Jianchao Li, Yonghua Ding, Jiaolong Dong, Nengchao Wang, Dongliang. Han, Feiyue Mao, Da Li, Zhipeng Chen, Zhoujun Yang, Zhongyong Chen, Yuan Pan, J-TEXT team
A new method to extract this pick-up has been developed by predicting the n = 0 pick-up brn=0 by the LM detectors based on Neural Networks (NNs) in J-TEXT.
no code implementations • 11 Sep 2023 • Chengshuo Shen, Wei Zheng, Bihao Guo, Yonghua Ding, Dalong Chen, Xinkun Ai, Fengming Xue, Yu Zhong, Nengchao Wang, Biao Shen, Binjia Xiao, Zhongyong Chen, Yuan Pan, J-TEXT team
The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamak (source domain) based on a domain adaptation algorithm called CORrelation ALignment (CORAL).
no code implementations • 27 Mar 2023 • Xinkun Ai, Wei Zheng, Ming Zhang, Dalong Chen, Chengshuo Shen, Bihao Guo, Bingjia Xiao, Yu Zhong, Nengchao Wang, Zhoujun Yang, Zhipeng Chen, Zhongyong Chen, Yonghua Ding, Yuan Pan, J-TEXT team
Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors.
Semi-supervised Anomaly Detection Supervised Anomaly Detection
no code implementations • 28 Aug 2022 • Chengshuo Shen, Wei Zheng, Yonghua Ding, Xinkun Ai, Fengming Xue, Yu Zhong, Nengchao Wang, Li Gao, Zhipeng Chen, Zhoujun Yang, Zhongyong Chen, Yuan Pan, J-TEXT team
Understanding why a predictor makes a certain prediction can be as crucial as the prediction's accuracy for future tokamak disruption predictors.