Neural networks based neutron emissivity tomography at JET with real-time capabilities

Autor:	M. Weiszflog, Carl Hellesen, E. Ronchi, M. Gatu Johnson, Henrik Sjöstrand, E. Andersson Sundén, Göran Ericsson, Sean Conroy
Rok vydání:	2010
Předmět:	Physics Nuclear and High Energy Physics Jet (fluid) Tomographic reconstruction Artificial neural network Scale (ratio) Joint European Torus ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computational science Nuclear magnetic resonance Emissivity Neutron Tomography Instrumentation
Zdroj:	Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 613:295-303
ISSN:	0168-9002
DOI:	10.1016/j.nima.2009.12.023
Popis:	Tomographic reconstruction techniques typically require computationally intensive algorithms which are not suitable for real-time application. This paper describes a framework to perform neutron emissivity tomography at the Joint European Torus (JET) using neural networks with successful results over a broad range of magnetic configurations, heating and fueling schemes. Application times in the μ s time scale allows for real-time applicability of the method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::228e1c3ae21655812ba0f82c0aa07e09 https://doi.org/10.1016/j.nima.2009.12.023 Zobrazit plný text záznamu Full Text from ScienceDirect