Comparison of theoretical and machine learning models to estimate gamma ray source positions using plastic scintillating optical fiber detector-Reference-Cited by-同舟云学术

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Comparison of theoretical and machine learning models to estimate gamma ray source positions using plastic scintillating optical fiber detector

Published:2021-10 Issue:10 Volume:53 Page:3431-3437
ISSN:1738-5733
Container-title:Nuclear Engineering and Technology
language:en
Short-container-title:Nuclear Engineering and Technology

Author:

Kim Jinhong,Kim Seunghyeon,Song Siwon,Park Jae Hyung,Kim Jin Ho,Lim Taeseob,Pyeon Cheol Ho^ORCID,Lee Bongsoo^ORCID

Funder

Chung-Ang University

National Research Foundation of Korea

Ministry of Science and ICT, South Korea

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference26 articles.

1. DMNR: a new concept for real-time online monitoring of short and medium term radioactive waste;Finocchiaro,2012

2. Classification of Radioactive Waste, IAEA Safety Series No. 111-G-1.1.

3. Performance evaluation of SiPM's for low threshold gamma detection;Pappalardo;Nucl. Phys. B-Proc. Sup.,2011

4. Real-time online monitoring of radwaste storage: a proof-of-principle test prototype;Cosentino;IEEE Trans. Nucl. Sci.,2012

5. Radiation Detection and Measurement;Knoll,2010

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine Learning-based reconstruction of single and double radiation source distribution using plastic scintillation optical fiber;Journal of Instrumentation;2023-08-01

2. Deep learning-based spectrum-dose prediction for a plastic scintillation detector;Radiation Physics and Chemistry;2022-11

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