کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
271324 504992 2012 4 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Learning on probabilistic manifolds in massive fusion databases: Application to confinement regime identification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
Learning on probabilistic manifolds in massive fusion databases: Application to confinement regime identification
چکیده انگلیسی

We present an integrated framework for (real-time) pattern recognition in fusion data. The main premise is the inherent probabilistic nature of measurements of plasma quantities. We propose the geodesic distance on probabilistic manifolds as a similarity measure between data points. Substructure induced by data dependencies may further reduce the dimensionality and redundancy of the data set. We present an application to confinement mode classification, showing the distinct advantage obtained by considering the measurement uncertainty and its geometry.


► We present an integrated framework for pattern recognition in fusion data.
► We model measurement uncertainty through an appropriate probability distribution.
► We use the geodesic distance on probabilistic manifolds as a similarity measure.
► We apply the framework to confinement mode classification.
► The classification accuracy benefits from uncertainty information and its geometry.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fusion Engineering and Design - Volume 87, Issue 12, December 2012, Pages 2068–2071
نویسندگان
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