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Found 6 papers, 0 papers with code
Extraction of n = 0 pick-up by locked mode detectors based on neural networks in J-TEXT
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.
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network
This paper presents an anomaly detection method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for addressing the issue of ball mill bearing fault detection.
Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation
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).
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
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
IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction
Understanding why a predictor makes a certain prediction can be as crucial as the prediction's accuracy for future tokamak disruption predictors.
Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor
Based on the feature extractor trained on J-TEXT, the disruption prediction model was transferred to EAST data with mere 20 discharges from EAST experiment.
