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Kimura, Yoshiki; Tsuchiya, Kenichi*
Radioisotopes, 72(2), p.121 - 139, 2023/07
Rapid and precise radioisotope identification in the scene of nuclear detection and nuclear security incidents is one of the challenging issues for the prompt response on the detection alarm or the incidents. A radioisotope identification algorithm using a deep artificial neural network model applicable to handheld gamma-ray detectors has been proposed in the present paper. The proposed algorithm automatically identifies gamma-emitting radioisotopes based on the count contribution ratio (CCR) from each of them estimated by the deep artificial neural network model trained by simulated gamma-ray spectra. The automated radioisotope identification algorithm can support first responders of nuclear detection and nuclear security incidents without sufficient experience and knowledge in radiation measurement. The authors tested the performance of the proposed algorithm using two different types of deep artificial neural network models in application to handheld detectors having high or low energy resolution. The proposed algorithm showed high performance in identifying artificial radioisotopes for actually measured gamma-ray spectra. It was also confirmed that the algorithm is applicable to identifying 
U and automated uranium categorization by analyzing estimated CCRs by the deep artificial neural network models. The authors also com-pared the performance of the proposed algorithm with a conventional radioisotope identification method and discussed promising ways to improve the performance of the algorithm using the deep artificial neural network.
Sasaki, Miyuki; Sanada, Yukihisa
Journal of Advanced Simulation in Science and Engineering (Internet), 9(1), p.30 - 39, 2022/01
This study presents the evaluation results of the validity of the visualization map of the ambient dose rate at 1 m above the ground level using an artificial neural network. The dose rate map created using the artificial neural network-based method is found to reproduce ground-based survey results better than conventional methods. Suggested to improve the validity of the airborne radiation survey visualization, applying the color data obtained using a photogrammetry system is a new experience.
Sasaki, Miyuki; Sanada, Yukihisa; Katengeza, E. W.*; Yamamoto, Akio*
Scientific Reports (Internet), 11, p.1857_1 - 1857_11, 2021/01
Times Cited Count：13 Percentile：67.27(Multidisciplinary Sciences)This study proposed a new method to visualize the ambient dose rate distribution using artificial neural networks from the results of airborne radiation monitoring. The method used airborne radiation monitoring conducted around Fukushima Daiichi Nuclear Power Plant by an unmanned aerial vehicle. A lot of survey data which had obtained in the past was used as training data for building a network. The reliability of the artificial neural network method was evaluated by comparison with the ground-based survey data. The dose rate map that was created by the artificial neural networks method reproduced the ground-based survey results better than traditional methods.
Nakajima, Norihiro
Nihon Genshiryoku Gakkai-Shi ATOMO
, 59(8), p.34 - 38, 2017/08
It is necessary the reading comprehension of output data to utilize the simulation in a design process, besides of the input data preparation. The simulation introduces enormous big data for evaluation. This paper describes data analysis technology in the analysis and the evaluation process of the output. The technology applies the artificial intelligence to minimize the unpredictable issues and oversight. It is based on the artifact engineering, which is a multi-sight abduction methodology, which derives a hypothesis.
Sasaki, Akira; Jo, Kazuki*; Kashiwagi, Hiroe*; Watanabe, Chiemi*; Suzuki, Manabu*; Lucas, P.*; Oishi, Masatoshi*; Kato, Daiji*; Kato, Masatoshi*; Kato, Takako*
Journal of Plasma and Fusion Research SERIES, Vol.7, p.348 - 351, 2006/00
no abstracts in English
Suzudo, Tomoaki; Nabeshima, Kunihiko; Takizawa, Hiroshi*
Nihon Genshiryoku Gakkai Wabun Rombunshi, 2(4), p.500 - 509, 2003/12
A new methodology to construct distributed computing systems specially targeting nuclear power plant monitoring systems is proposed. In this framework, a monitoring system is composed of multiple modules and a client that administrates them. Each module is designed as a TTY-based program, and therefore has a great flexibility when it is developed. The client holds virtual modules, each of which works as an interface to a module in the remote hosts. Because the virtual modules are defined as a class in the meaning of object-oriented programming, the whole system is easily structured. A prototype of neural-network-based monitoring system has been developed utilizing this methodology, and the expected advantages have been confirmed.
Tsuji, Hirokazu; Fujii, Hidetoshi*
Tahenryo Kaiseki Jitsurei Handobukku, p.107 - 114, 2002/00
no abstracts in English
Nabeshima, Kunihiko
JAERI 1342, 119 Pages, 2001/03
no abstracts in English
Nabeshima, Kunihiko; Inoue, K.*; Kudo, Kazuhiko*; Suzuki, Katsuo*
International Journal of Knowledge; Based Intelligent Engineering Systems, 4(4), p.208 - 212, 2000/10
no abstracts in English
*
JNC TJ7400 2000-015, 203 Pages, 2000/08
JNC-TJ7400-2000-015.pdf:6.07MB
no abstracts in English
Nabeshima, Kunihiko; Suzudo, Tomoaki; Takizawa, Hiroshi*; Ono, Tomio*; Kudo, Kazuhiko*
Proceedings of International Topical Meeting on Nuclear Plant Instrumentation, Controls, and Human-Machine Interface Technologies (NPIC&HMIT 2000) (CD-ROM), 9 Pages, 2000/00
no abstracts in English
Nabeshima, Kunihiko; Inoue, K.*; Suzuki, Katsuo; *
Proc. of 5th Int. Conf. on Neural Information Processing (ICONIP'98), 2, p.1102 - 1105, 1998/00
no abstracts in English
Yoshino, Ryuji; Koga, J. K.*; Takeda, Tatsuoki
Fusion Technology, 30(2), p.237 - 250, 1996/11
no abstracts in English
Nabeshima, Kunihiko; Nose, Shoichi*; *; Suzuki, Katsuo
JAERI-Research 96-051, 46 Pages, 1996/10
JAERI-Research-96-051.pdf:1.51MB
no abstracts in English
Kishimoto, Maki; Sakasai, Kaoru; Ara, Katsuyuki; Fujita, Takaaki; *
IEEE Transactions on Plasma Science, 24(2), p.528 - 538, 1996/04
Times Cited Count：1 Percentile：4.4(Physics, Fluids & Plasmas)no abstracts in English
Kishimoto, Maki; Sakasai, Kaoru; Ara, Katsuyuki
Journal of Applied Physics, 79(1), p.1 - 7, 1996/01
Times Cited Count：10 Percentile：48.63(Physics, Applied)no abstracts in English
Nabeshima, Kunihiko; Suzuki, Katsuo; Shinohara, Yoshikuni*; E.Tuerkcan*
JAERI-Research 95-076, 33 Pages, 1995/11
JAERI-Research-95-076.pdf:1.03MB
no abstracts in English
Ugolini; Yoshikawa, Shinji; Ozawa, Kenji
PNC TN9410 95-253, 13 Pages, 1995/10
This report presents the implementation of the a model reference adaptive control system based on the artificial neural network technique (MRAC
) in a fast breeder reactor (FBR) building block type (BBT) simulator representing the Monju prototype reactor. The purpose of this report is to improve the control of the outlet steam temperature of the three evaporators of the Monju prototype reactor. The connection between the MRAC system and the BBT simulator is achieved through an external shared memory accessible by both systems. The MRAC system calculates the demand for the position of the feedwater valve replacing the signal of a PID controller collocated inside the heat transport system model of the Monju prototype reactor. Two series of simulation tests havc been performed, one with one loop connected to the MRAC system (leaving the remaining two connected to the original PID controller), and the other with three loops connected to the MRAC system. In both simulation tests the MRAC system performed better than the PID controller, keeping the outlet steam temperature of the evaporators closer to the required set point value through all the transients.
Nabeshima, Kunihiko
Tokei Suri Kenkyujo Kyodo Kenkyu Ripoto 68, 0, p.43 - 52, 1995/03
no abstracts in English
R.Kozma*; Nabeshima, Kunihiko
Annals of Nuclear Energy, 22(7), p.483 - 496, 1995/00
Times Cited Count：12 Percentile：74.11(Nuclear Science & Technology)no abstracts in English
