The paper addresses the problem of predicting the one set of a disruption on the basis of some known precursors possibly announcing the event. The availability in real time of a set of diagnostic signals allows us to collectively interpret the data in order to decide whether we are near a disruption or during a normal operation scenario. A database of disruptive discharges in JET-Team has been analyzed in this work, as a relevant experimental example. Multi-Class Support Vector Machines have been investigated aiming to gain information about the mechanism which drive to a disruption. The proposed processor will operate by implementing a classification of the shot type, and outputting an integer number that indicates the category of disruption.
|Titolo:||Multi Class Support Vector Machines for Disruption Classification in Tokamak Reactors|
|Data di pubblicazione:||2006|
|Appare nelle tipologie:||1.1 Articolo in rivista|