25 citations · 45 across the 9 of their papers we have counts for
5 papers · 1 filter
Extraction of n = 0 pick-up by locked mode detectors based on neural networks in J-TEXT
Chengshuo Shen, Jianchao Li, Yonghua Ding +10
Measurement of locked mode (LM) is important for the physical research of Magnetohydrodynamic (MHD) instabilities and plasma disruption. The n = 0 pick-up need to be extracted and…
Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder
Xinkun Ai, Wei Zheng, Ming Zhang +12
In the initial stages of operation for future tokamak, facing limited data availability, deploying data-driven disruption predictors requires optimal performance with minimal use o…
Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation
Chengshuo Shen, Wei Zheng, Bihao Guo +11
The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in dis…
Critical roles of edge turbulent transport in the formation of high-field-side high-density front and density limit disruption in J-TEXT tokamak
Peng Shi, Yuhan Wang, Li Gao +16
This article presents an in-depth study of the sequence of events leading to density limit disruption in J-TEXT tokamak plasmas, with an emphasis on boudary turbulent transport and…
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
Xinkun Ai, Wei Zheng, Ming Zhang +12
The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-dr…