Estimation of radiation source distribution using machine learning with γ ray energy spectra

Uemura Takero; Yamaguchi Katsuhiko

Art

J-GLOBAL ID：202002270542215483 Reference number：20A1723449

Estimation of radiation source distribution using machine learning with γ ray energy spectra

γ線エネルギースペクトルを用いた機械学習による放射線源分布の推定

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Author (2)： ,
Material：
Volume： 7 Issue： 1 Page： 71-81(J-STAGE) Publication year： 2020
JST Material Number： U0612A ISSN： 2188-5303 Document type： Article
Article type：原著論文 Country of issue： Japan (JPN) Language： ENGLISH (EN)

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Author keywords (3)： , ,

Computer simulation , Radiation protection in general

Reference (4)：

[1] Y. Sanada, T. Sugita, Y. Nishizawa, A. Kondo, T. Torii: The aerial radiation monitoring in Japan after the Fukushima Daiichi nuclear power plant accident, Progress in Nuclear Science and Technology, Volume 4 (2014) pp. 76-80
[2] M. Sasaki, A. Ishizaki, Y Sanada: Development of analysis method for airborne radiation monitoring using the inverse problem solutions, Progress in Nuclear Science and Technology, Volume: 6 (2019), pp. 63-67
[3] D. E. Rumelhart, G. E. Hinton, R. J. Williams : Learning representations by back-propagating errors, Nature, volume : 323 (1986), pp. 533-536
[4] M. J. Berger, J. H. Hubbell, S. M. Seltzer, J. Chang, J. S. Coursey, R. Sukumar, D. S. Zucker, K. Olsen : “NIST XCOM : Photon Cross Sections Database” available from https://www.nist.gov/pml/xcom-photon-cross-sections-database.

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