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  1. Published Papers
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Neural Network Data Analysis in the Large Helical Device Thomson Scattering System

http://hdl.handle.net/10655/00013337
http://hdl.handle.net/10655/00013337
78bf2d23-ce1f-42ae-9291-1f1a3d3f759a
名前 / ファイル ライセンス アクション
13224_PFR.pdf 13224_PFR (446.2 kB)
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Item type 学術雑誌論文 / Journal Article(1)
公開日 2022-07-05
タイトル
タイトル Neural Network Data Analysis in the Large Helical Device Thomson Scattering System
言語
言語 eng
キーワード
主題Scheme Other
主題 Thomson scattering, Large Helical Device (LHD)
キーワード
主題Scheme Other
主題 neural network method
キーワード
主題Scheme Other
主題 real-time data analysis
キーワード
主題Scheme Other
主題 electron temperature
キーワード
主題Scheme Other
主題 error estimation
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者 YAMADA, Ichihiro

× YAMADA, Ichihiro

YAMADA, Ichihiro

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FUNABA, Hisamichi

× FUNABA, Hisamichi

FUNABA, Hisamichi

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LEE, Jong-ha

× LEE, Jong-ha

LEE, Jong-ha

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HUANG, Yuan

× HUANG, Yuan

HUANG, Yuan

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LIU, Chunhua

× LIU, Chunhua

LIU, Chunhua

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著者ID
内容記述タイプ Other
内容記述 0000-0002-7023-1539
抄録
内容記述タイプ Abstract
内容記述 In Thomson scattering diagnostics systems, a combination of the lookup table and the minimum χ2 methods has been widely used to determine electron temperature. The concept of the minimum χ2 method is based on clearly defined mathematical statistics. However, the minimum χ2 method calculation requires a large amount of time because all χ2 values have to be calculated at all temperatures included in the lookup table. Thus, this method is unsuitable for the real-time data analysis required for the next generation of fusion devices, e.g., the International Thermonuclear Experimental Reactor in France. To establish real-time data analysis for Thomson scattering diagnostics, we have developed a neural network program for the large helical device (LHD) Thomson scattering (TS) system. First, we systematically studied the number of nodes and training cycles required to obtain satisfactory results, and then applied them to the LHD TS system. The calculation time was successfully reduced by approximately 1/50 - 1/100 of the χ2 method calculation time. In addition, experimental error estimation has been performed according to the concept of the neural network method used in this study.
書誌情報 Plasma and Fusion Research

巻 17, 号 Special Issue 1, p. 2402061, 発行日 2022-06-22
書誌的事項
the 30th International Toki Conference on Plasma and Fusion Research (ITC30)
出版者
出版者 The Japan Society of Plasma Science and Nuclear Fusion Research
ISSN
収録物識別子タイプ ISSN
収録物識別子 1880-6821
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AA12346675
DOI
関連タイプ isIdenticalTo
識別子タイプ DOI
関連識別子 10.1585/pfr.17.2402061
権利
権利情報 (c) 2022 The Japan Society of Plasma Science and Nuclear Fusion Research
関連サイト
識別子タイプ DOI
関連識別子 https://doi.org/10.1585/pfr.17.2402061
関連名称 Publisher version
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
NAIS
13224
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Cite as

YAMADA, Ichihiro, FUNABA, Hisamichi, LEE, Jong-ha, HUANG, Yuan, LIU, Chunhua, 2022, Neural Network Data Analysis in the Large Helical Device Thomson Scattering System: The Japan Society of Plasma Science and Nuclear Fusion Research , 2402061– p.

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