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Research and Development of Source Term Activity Reconstruction System Based on Deep Learning

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成果类型:
期刊论文
作者:
Zhang, Gema;Song, Yingming;Zhang, Zehuan;Yuan, Weiwei
通讯作者:
Yingming Song
作者机构:
[Zhang, Gema; Song, Yingming] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.
[Zhang, Zehuan] Tsinghua Univ, Inst Nucl Energy & New Energy Technol, Beijing 100084, Peoples R China.
[Zhang, Zehuan] Beijing Key Lab Nucl Detect & Measurement Technol, Beijing 100084, Peoples R China.
[Yuan, Weiwei] Univ South China, Radon Prov Key Lab, Hengyang 421001, Peoples R China.
通讯机构:
[Yingming Song] S
School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
语种:
英文
关键词:
Deep learning;Nuclear facility decommissioning;Raspberry Pi;Source term reconstruction;γ-ray dose detector
期刊:
Annals of Nuclear Energy
ISSN:
0306-4549
年:
2022
卷:
175
页码:
109248
机构署名:
本校为第一机构
院系归属:
核科学技术学院
摘要:
The radiation damage weakens the efficiency of decommissioning nuclear facilities, and the source term is hard to be located and measured because it often distributes in or inside objectives. In practice, the hot spot and source intensity are measured by the gamma camera then the radiation field is estimated by particle transport algorithm as a reference in scheme planning. This technology is expensive in measuring and simulating. Hence, this paper presents a source term activity reconstruction (hereinafter referred to as STAR) method based on deep learning to solve this problem. Firstly, a UN...

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