This paper describes the implementation of a real-time disruption predictor that is based on support vector machine (SVM) classifiers. The implementation was performed under the MARTe framework on a six-core x86 architecture. The system is connected via JET's Real-time Data Network (RTDN). The online results show a high degree of successful predictions and a low rate of false alarms, thus confirming the usefulness of this approach in a disruption mitigation scheme. The implementation shows a low computational load, which will be exploited in the immediate future to increase the prediction's temporal resolution.

Implementation of the Disruption Predictor APODIS in JET's Real-Time Network Using the MARTe Framework

Murari A;
2014

Abstract

This paper describes the implementation of a real-time disruption predictor that is based on support vector machine (SVM) classifiers. The implementation was performed under the MARTe framework on a six-core x86 architecture. The system is connected via JET's Real-time Data Network (RTDN). The online results show a high degree of successful predictions and a low rate of false alarms, thus confirming the usefulness of this approach in a disruption mitigation scheme. The implementation shows a low computational load, which will be exploited in the immediate future to increase the prediction's temporal resolution.
2014
Istituto gas ionizzati - IGI - Sede Padova
Machine learning
Real time systems
Learning systems
False alarms
Low computational loads
Low rates
Mitigation schemes
Real-time data
Real-time networks
Temporal resolution
Support vector machines
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/255555
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