Cross machine investigation of magnetic tokamak dust: Morphological and elemental analysis
Sign Up to like & getrecommendations! Published in 2021 at "Fusion Engineering and Design"
DOI: 10.1016/j.fusengdes.2021.112315
Abstract: Abstract The presence of magnetic dust can be an important issue for future fusion reactors where plasma breakdown is critical. Magnetic dust has been collected from contemporary fusion devices (FTU, Alcator C-Mod, COMPASS and DIII-D)… read more here.
Keywords: magnetic dust; morphological elemental; elemental analysis; cross machine ... See more keywords
Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A
Sign Up to like & getrecommendations! Published in 2021 at "Plasma Physics and Controlled Fusion"
DOI: 10.1088/1361-6587/abfa74
Abstract: Using machine learning (ML) techniques to develop disruption predictors is an effective way to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption predictors have made great progress regarding accuracy, but… read more here.
Keywords: disruption prediction; model; cross machine; disruption ... See more keywords
Deep Transfer Network With Multi-Kernel Dynamic Distribution Adaptation for Cross-Machine Fault Diagnosis
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3053075
Abstract: Recently, various deep learning models, which are mainly based on data-driven algorithms, have received more and more attention in the field of intelligent fault diagnosis and prognostics. However, there are two major assumptions accepted by… read more here.
Keywords: diagnosis; machine fault; cross machine; transfer ... See more keywords