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Visualization of plasma shape in the LHD-type helical fusion reactor, FFHR, by a deep learning technique
http://hdl.handle.net/10655/00012979
http://hdl.handle.net/10655/0001297910f50437-235b-401a-a4f5-4b2b9f28d12d
名前 / ファイル | ライセンス | アクション |
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J.Vis.24_pp1141 (6.3 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2022-02-03 | |||||
タイトル | ||||||
タイトル | Visualization of plasma shape in the LHD-type helical fusion reactor, FFHR, by a deep learning technique | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Visualization | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Deep learning | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Plasma shape | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | 3D model | |||||
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 |
HU, Kunqi
× HU, Kunqi× KOYAMADA, Koji× OHTANI, Hiroaki× GOTO, Takuya× MIYAZAWA, Junichi |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | A magnetic field is used to confine the plasma to achieve controlled fusion. Therefore, since the movement of the plasma follows magnetic field lines, a plurality of magnetic field lines is calculated from electromagnetic field simulation results in a fusion reactor. Because of the complicated distribution of magnetic field lines in three-dimensional (3D) space, existing analysis measures which are mostly based on two-dimensional poloidal plasma cross-sections are unsatisfactory for domain experts. To solve this problem, we propose a technique for reconstructing a regular scalar field from the magnetic field lines. First, on poloidal plasma cross-sections, intersection points of magnetic field lines are used to make annotations of learning the plasma shape. Then, a deep neural network is built to approximate the scalar field that represents the probability of the existence of magnetic field lines. Consequently, a 3D model of plasma shape has managed to be constructed by applying the marching cubes method. The effectiveness of the proposed method is demonstrated by comparing it with the conventional method and domain experts’ reviews. | |||||
書誌情報 |
Journal of Visualization 巻 24, p. 1141, 発行日 2021-08-18 |
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出版者 | ||||||
出版者 | Springer Link | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 13438875 | |||||
書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA11268703 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1007/s12650-021-00768-w | |||||
権利 | ||||||
権利情報 | (c) EDP Sciences, Societ a Italiana di Fisica, Springer-Verlag 2021 | |||||
情報源 | ||||||
関連名称 | Hu, K., Koyamada, K., Ohtani, H. et al. Visualization of plasma shape in the LHD-type helical fusion reactor, FFHR, by a deep learning technique. J Vis 24, 1141–1154 (2021). https://doi.org/10.1007/s12650-021-00768-w | |||||
関連サイト | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1007/s12650-021-00768-w | |||||
関連名称 | Publisher version | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
NAIS | ||||||
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