Comparison of theoretical and machine learning models to estimate gamma ray source positions using plastic scintillating optical fiber detectorComparison of theoretical and machine learning models to estimate gamma ray source positions using plastic scintillating optical fiber detector
- Authors
- Kim, J.; Kim, S.; Song, S.; Park, J.H.; Kim, J.H.; Lim, T.; Pyeon, C.H.; Lee, B.
- Issue Date
- Oct-2021
- Publisher
- Korean Nuclear Society
- Keywords
- Gamma ray detection; Machine learning; Nonlinear regression; Plastic scintillating optical fiber; Position estimation
- Citation
- Nuclear Engineering and Technology, v.53, no.10, pp 3431 - 3437
- Pages
- 7
- Journal Title
- Nuclear Engineering and Technology
- Volume
- 53
- Number
- 10
- Start Page
- 3431
- End Page
- 3437
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/48998
- DOI
- 10.1016/j.net.2021.04.019
- ISSN
- 1738-5733
- Abstract
- In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.
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- Appears in
Collections - College of Engineering > School of Energy System Engineering > 1. Journal Articles
- College of Engineering > School of Civil and Environmental Engineering, Urban Design and Studies > 1. Journal Articles
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