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Automatic disruption classification based on manifold learning for real-time applications on JET

机译:基于流形学习的自动中断分类,用于JET的实时应用

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摘要

Disruptions remain the biggest threat to the safe operation of tokamaks. To efficiently mitigate the negative effects, it is now considered important not only to predict their occurrence but also to be able to determine, with high probability, the type of disruption about to occur. This paper reports the results obtained using the nonlinear generative topographic map manifold learning technique for the automatic classification of disruption types. It has been tested using an extensive database of JET discharges selected from JET campaigns from C15 (year 2005) up to C27 (year 2009). The success rate of the classification is extremely high, sometimes reaching 100%, and therefore the prospects for the deployment of this tool in real time are very promising.
机译:中断仍然是托卡马克安全运行的最大威胁。为了有效减轻负面影响,现在认为重要的是,不仅要预测它们的发生,而且要能够以很高的可能性确定即将发生的干扰的类型。本文报告了使用非线性生成地形图流形学习技术对干扰类型进行自动分类的结果。已使用从C15(2005年)至C27(2009年)的JET战役中选择的广泛的JET排放数据库进行了测试。分类的成功率极高,有时达到100%,因此实时部署此工具的前景非常可观。

著录项

  • 来源
    《Nuclear fusion》 |2013年第9期|093023.1-093023.11|共11页
  • 作者单位

    Electrical and Electronic Engineering Department, University of Cagliari, Italy;

    Electrical and Electronic Engineering Department, University of Cagliari, Italy;

    Consorzio RFX-Associazione EUR ATOM ENEA per la Fusione, 1-35127 Padova, Italy;

    Electrical and Electronic Engineering Department, University of Cagliari, Italy;

    Electrical and Electronic Engineering Department, University of Cagliari, Italy;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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