25 citations · 45 across the 9 of their papers we have counts for
9 papers
Healing of the edge magnetic island in the island divertor configuration on J-TEXT
Zhangrong Hou, Song Zhou, Nengchao Wang +18
The phenomena of island healing and configuration transition induced by high-power electron cyclotron resonance heating (ECRH) have been investigated in the island divertor configu…
Adaptive Anomaly Detection Disruption Prediction Starting from First Discharge on Tokamak
Xinkun Ai
Plasma disruption presents a significant challenge in tokamak fusion, where it can cause severe damage and economic losses. Current disruption predictors mainly rely on data-driven…
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…
Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor
Wei Zheng, Fengming Xue, Ming Zhang +12
Predicting disruptions across different tokamaks is a great obstacle to overcome. Future tokamaks can hardly tolerate disruptions at high performance discharge. Few disruption disc…
IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction
Chengshuo Shen, Wei Zheng, Yonghua Ding +10
Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. Understanding why a predictor makes a certain prediction can be as…