TY - GEN
T1 - Machine Learning Approach for Gamma-ray Spectra Identification for Radioactivity Analysis
AU - Kin, T.
AU - Goto, J.
AU - Oshima, M.
PY - 2019/10
Y1 - 2019/10
N2 - We have proposed a machine learning model for efficient gamma-ray spectrometry for environmental recovery from the Fukushima Daiichi Power Plant Accident. In the present study, we focus on a radioactive nuclide identification by the machine learning in a screening measurement. A simple deep neural network having two hidden layer is proposed, and the identification accuracy is achieved more than 95% for single gamma-ray spectra.
AB - We have proposed a machine learning model for efficient gamma-ray spectrometry for environmental recovery from the Fukushima Daiichi Power Plant Accident. In the present study, we focus on a radioactive nuclide identification by the machine learning in a screening measurement. A simple deep neural network having two hidden layer is proposed, and the identification accuracy is achieved more than 95% for single gamma-ray spectra.
UR - http://www.scopus.com/inward/record.url?scp=85083570300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083570300&partnerID=8YFLogxK
U2 - 10.1109/NSS/MIC42101.2019.9059618
DO - 10.1109/NSS/MIC42101.2019.9059618
M3 - Conference contribution
AN - SCOPUS:85083570300
T3 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
BT - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
Y2 - 26 October 2019 through 2 November 2019
ER -