Results 1 to 10 of about 401,655 (316)
Adaptive anomaly detection disruption prediction starting from first discharge on tokamak [PDF]
Nuclear FusionPlasma disruption presents a significant challenge in tokamak fusion, especially in large-size devices like ITER, where it causes severe damage. While current data-driven machine learning methods perform well in disruption prediction, they require ...
X.K. Ai +14 more
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Investigation of Machine Learning Techniques for Disruption Prediction Using JET Data [PDF]
Plasma, 2023 Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared.
Joost Croonen +2 more
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Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time [PDF]
Nuclear FusionThis article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks.
J. Vega +5 more
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Big data analytics and anomaly prediction in the cold chain to supply chain resilience [PDF]
FME Transactions, 2021 The purpose of the research was to develop a prediction method to prevent disruption related to temperature anomaly in the cold chain supply. The analysed data covers the period of the entire working cycle of the thermal container.
Lorenc Augustyn +2 more
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Nuclear Fusion, 2023 The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors ...
J.X. Zhu +6 more
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IDP-PGFE: an interpretable disruption predictor based on physics-guided feature extraction
Nuclear Fusion, 2023 Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us
C. Shen +12 more
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Performance Comparison of Machine Learning Disruption Predictors at JET
Applied Sciences, 2023 Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant ...
Enrico Aymerich +8 more
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Nuclear Fusion, 2023 Plasma disruption is a very dangerous event for future tokamaks and fusion reactors. Therefore, predicting disruption is crucial for ensuring the safety and performance of reactors.
B.H. Guo +10 more
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Predicting Resilience of Interdependent Urban Infrastructure Systems
IEEE Access, 2022 Climate change is increasing the frequency and the intensity of weather events, leading to large-scale disruptions to critical infrastructure systems. The high level of interdependence among these systems further aggravates the extent of disruptions.
Beatrice Cassottana +5 more
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Initial analytical theory of plasma disruption and experimental evidence
Scientific Reports, 2023 It is a great physical challenge to achieve controlled nuclear fusion in magnetic confinement tokamak and solve energy shortage problem for decades. In tokamak plasma, large-scale plasma instability called disruption will halt power production of reactor
Huibin Qiu +13 more
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