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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.
Kimura, Yoshiki; Tsuchiya, Kenichi*
no journal, ,
A nuclear security event involving nuclear and other radioactive materials out of regulatory control (MORC) has potential severe consequence on public health, environments, economics and society. When a nuclear security event caused by MORC would be occurred, it is essential to identify the hazardous substances such as nuclear materials and radioisotopes as the initial response activity at the event scene. In this study, automated radioisotope identification algorism by Machine-Learning (ML) based gamma-ray spectrum analysis using handheld type detectors have been developed. The training data set for ML-based algorism has been developed based on detector simulation and the usability of the simulation-based data set for ML model training to perform radioisotope identification has been discussed.
Kimura, Yoshiki; Tsuchiya, Kenichi*; Okubo, Ayako*; Tanabe, Kosuke*; Kakuda, Hidetoshi*; Akiba, Norimitsu*; Tomikawa, Hirofumi
no journal, ,
Kimura, Yoshiki; Tsuchiya, Kenichi*
no journal, ,
no abstracts in English
