TITLE

Feature extraction for improved disruption prediction analysis at JET

AUTHOR(S)
Rattá, G. A.; Vega, J.; Murari, A.; Johnson, M.; JET-EFDA Contributors
PUB. DATE
October 2008
SOURCE
Review of Scientific Instruments;Oct2008, Vol. 79 Issue 10, p10F328
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Disruptions are major instabilities and remain one of the main problems in tokomaks. Using Joint European Torus database, a disruption predictor is developed by computational methods including supervised learning techniques. The main objectives of the work are to develop accurate automatic classifiers, to test their performances, and to determine how much in advance of the disruption they can operate with acceptable reliability.
ACCESSION #
35150084

 

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