activity
20222024
most citedIDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction

25 citations · 44 across the 7 of their papers we have counts for

collaborators

7 papers

physics.plasm-ph2024

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…

cs.LG2023

Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network

Xinkun Ai, Kun Liu, Wei Zheng +6

Ball mills play a critical role in modern mining operations, making their bearing failures a significant concern due to the potential loss of production efficiency and economic con…

physics.plasm-ph2023★ 6 cited

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…

physics.plasm-ph2023

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…

physics.plasm-ph2023★ 11 cited

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…

physics.plasm-ph2022★ 2 cited

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…

physics.plasm-ph2022★ 25 cited

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…