نتائج البحث

Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder

• Authors : Ai, Xinkun; Zheng, Wei; Zhang, Ming

Subjects: Physics - Plasma Physics

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Performance Comparison of Machine Learning Disruption Predictors at JET

• Authors : Enrico Aymerich; Barbara Cannas; Fabio Pisano

Subjects: tokamak; disruption prediction; machine learning

• Source: Applied Sciences, Vol 13, Iss 3, p 2006 (2023)

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A cost-based criterion for implementing data-driven disruption predictors

• Authors : Parsons, Matthew S.; US Department of Energy Office of Science

• Source: Fusion Engineering and Design ; volume 191, page 113564 ; ISSN 0920-3796

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Global optimization driven by genetic algorithms for disruption predictors based on APODIS architecture

• Authors : Ratta, G. A.; Andersson Sundén, Erik; Asp, E.

Subjects: Disruption prediction; Genetic Algorithms; JET

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Assessment of linear disruption predictors using JT-60U data

• Authors : Vega, J.; Hernández, F.; Dormido Canto, Sebastián

Subjects: Informática

• Source: Fusion Engineering and Design

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Assessment of linear disruption predictors using JT-60U data

• Authors : Vega, Jes??s; Hern??ndez, Francisco; Dormido-Canto, Sebasti??n

Subjects: disruption prediction; signal increments; nearest centroid

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Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)

• Authors : S.M. Gonzalez de Vicente; D. Mazon; M. Xu

Subjects: integrated data analysis; data validation; Bayesian techniques

• Source: Nuclear Fusion, Vol 63, Iss 4, p 047001 (2023)

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Summary report of the 3rd IAEA technical meeting on fusion data processing validation and analysis (FDPVA)

• Authors : Mazon, D.; de Vicente, S. M. Gonzalez; Churchill, M.

Subjects: Physics and Astronomy; integrated data analysis; data validation

• Source: NUCLEAR FUSION ; ISSN: 0029-5515 ; ISSN: 1741-4326

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Progress Toward Interpretable Machine Learning-Based Disruption Predictors Across Tokamaks

• Authors : Rea, C.; Montes, K. J.; Pau, A.

Subjects: diii-d; machine learning; jet

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Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection

• Authors : Ai, Xinkun; Zheng, Wei; Zhang, Ming

Subjects: Physics - Plasma Physics; Computer Science - Artificial Intelligence; Computer Science - Machine Learning

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