Improvement of training data for dose rate distribution using an artificial neural network-Reference-Cited by-同舟云学术

Improvement of training data for dose rate distribution using an artificial neural network

Published:2022 Issue:1 Volume:9 Page:30-39
ISSN:2188-5303
Container-title:Journal of Advanced Simulation in Science and Engineering
language:en
Short-container-title:JASSE

Author:

Sasaki Miyuki¹,Sanada Yukihisa¹

Affiliation:

1. Sector of Fukushima Research and Development, Japan Atomic Energy Agency

Publisher

Japan Society for Simulation Technology

Subject

General Engineering

Link

https://www.jstage.jst.go.jp/article/jasse/9/1/9_30/_pdf

Reference11 articles.

1. [1] Nuclear Regulation Authority in Japan: Monitoring information of environmental radioactivity level, https://radioactivity.nsr.go.jp/en/ (Accessed at 4 October, 2021).

2. [2] M. Sasaki, A. Ishizaki, Y. Sanada: Development of analysis method for airborne radiation monitoring using the inverse problem solutions, Prog. Nucl. Sci. Tech., 6 (2019), 63-67.

3. [3] M. Sasaki, Y. Sanada, A. Yamamoto: Application of the forest shielding factor to the maximum-likelihood expectation maximization method for airborne radiation monitoring, Radiat. Protect. Dosim., 184 (2019), 400-404.

4. [4] M. Sasaki, Y. Sanada, E. W. Katengeza, A. Yamamoto: New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks, Scientific Reports, 11 (2021), 1857.

5. [5] GSI (Geospatial information authority of Japan): Maps & Geospatial Information, https://www.gsi.go.jp/ENGLISH/page_e30031.html (Accessed at 4 October, 2021).

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