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清华大学学报(自然科学版)  2021, Vol. 61 Issue (11): 1308-1315    DOI: 10.16511/j.cnki.qhdxxb.2020.22.033
  核能与新能源工程 本期目录 | 过刊浏览 | 高级检索 |
基于径向基神经网络仿真γ能谱模板库的核素识别方法
杜晓闯1,2, 涂红兵1, 黎岢2, 张洁1, 王康3, 刘鹤敏1, 梁漫春2, 汪向伟2
1. 中广核工程有限公司 核电安全监控技术与装备国家重点实验室, 深圳 518172;
2. 清华大学 工程物理系, 公共安全研究院, 北京 100084;
3. 北京辰安科技股份有限公司, 北京 100094
Radionuclide identification method based on a gamma-spectra template library simulated by radial basis function neural networks
DU Xiaochuang1,2, TU Hongbing1, LI Ke2, ZHANG Jie1, WANG Kang3, LIU Hemin1, LIANG Manchun2, WANG Xiangwei2
1. State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China;
2. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
3. Beijing Global Safety Technology Co., Ltd., Beijing 100094, China
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摘要 传统的γ能谱分析方法存在计算复杂、耗时较长等问题。该文通过Geant4软件模拟生成26种放射性核素γ能谱,并基于径向基神经网络进行拟合,建立γ能谱模板库。针对未知γ能谱,利用最小二乘法、非线性规划算法在模板库中寻找放射性核素的最优组合,利用集成学习的思想,集成两种算法计算结果,并建立客观的识别标准。运用所提方法识别单核素γ能谱、双核素混合γ能谱以及三核素混合γ能谱,识别结果表明该方法识别核素种类的准确率较高,具有可行性与有效性。
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杜晓闯
涂红兵
黎岢
张洁
王康
刘鹤敏
梁漫春
汪向伟
关键词 放射性核素识别径向基神经网络核素γ能谱模板库最小二乘法非线性规划    
Abstract:The traditional gamma ray spectrum analysis methods are usually computationally complex and time-consuming. This paper simulated 26 radionuclide spectra using Geant4 to develop a gamma-spectra template library by fitting the spectra with radial basis function neural networks. Unknown gamma ray spectra were then identified using a least-squares algorithm and a nonlinear programing algorithm to find the optimal combination of radionuclide spectra in the library that matched the unknown spectrum with ensemble learning used to integrate the results of the two algorithms for identification. A single spectrum and mixed spectra containing 2 or 3 kinds of radionuclides were generated to evaluate the method. The results show that this method can accurately identify the radionuclides in an efficient and effective way.
Key wordsradionuclide identification    radial basis function neural networks    radionuclide gamma-spectra template library    least-squares method    nonlinear programming
收稿日期: 2020-08-10      出版日期: 2021-10-19
基金资助:核电安全监控技术与装备国家重点实验室长期基础课题(K-A2019.403)
通讯作者: 梁漫春,副研究员,E-mail:lmc@tsinghua.edu.com     E-mail: lmc@tsinghua.edu.com
引用本文:   
杜晓闯, 涂红兵, 黎岢, 张洁, 王康, 刘鹤敏, 梁漫春, 汪向伟. 基于径向基神经网络仿真γ能谱模板库的核素识别方法[J]. 清华大学学报(自然科学版), 2021, 61(11): 1308-1315.
DU Xiaochuang, TU Hongbing, LI Ke, ZHANG Jie, WANG Kang, LIU Hemin, LIANG Manchun, WANG Xiangwei. Radionuclide identification method based on a gamma-spectra template library simulated by radial basis function neural networks. Journal of Tsinghua University(Science and Technology), 2021, 61(11): 1308-1315.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2020.22.033  或          http://jst.tsinghuajournals.com/CN/Y2021/V61/I11/1308
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
[1] 钱晋. 基于遗传神经网络的γ能谱分析研究[D]. 杭州:中国计量学院, 2013. QIAN J. Gamma spectrum analysis based on genetic neural network[D]. Hangzhou:China Jiliang University, 2013. (in Chinese)
[2] 弟宇鸣, 许伟, 许鹏, 等. 一种基于BP神经网络的γ能谱识别方法[J]. 核电子学与探测技术, 2006, 26(4):397-399, 413. DI Y M, XU W, XU P, et al. A method of γ spectrum identification based on the BP neural network[J]. Nuclear Electronics & Detection Technology, 2006, 26(4):397-399, 413. (in Chinese)
[3] 史东生, 弟宇鸣, 周春林. 粒子群优化算法在神经网络识别γ能谱中的应用[J]. 核技术, 2007, 30(7):615-618. SHI D S, DI Y M, ZHOU C L. Application of particle swarm optimization to identify gamma spectrum with neural network[J]. Nuclear Techniques, 2007, 30(7):615-618. (in Chinese)
[4] 王崇杰, 贾慧慧, 冯琳懿, 等. 基于RBF人工神经网络的γ能谱分析[J]. 核电子学与探测技术, 2016, 36(1):56-59. WANG C J, JIA H H, FENG L Y, et al. γ-spectra analysis based on RBF artificial neural network[J]. Nuclear Electronics & Detection Technology, 2016, 36(1):56-59. (in Chinese)
[5] 刘议聪, 王伟, 牛德青. 基于人工神经网络的核素识别分析方法[J]. 兵工自动化, 2015, 34(11):86-91. LIU Y C, WANG W, NIU D Q. Nuclide identification and analysis using artificial neural network[J]. Ordnance Industry Automation, 2015, 34(11):86-91. (in Chinese)
[6] ROEMER K, SAUCKE K, PAUSCH G, et al. Simulation of template spectra for scintillator based radionuclide identification devices using GEANT4[C]//2006 IEEE Nuclear Science Symposium Conference Record. San Diego, USA:IEEE, 2006.
[7] HOGAN M A, YAMAMOTO S, COVELL D F. Multiple linear regression analysis of scintillation gamma-ray spectra:Automatic candidate selection[J]. Nuclear Instruments and Methods, 1970, 80(1):61-68.
[8] CARLEVARO C M, WILKINSON M V, BARRIOS L A. A genetic algorithm approach to routine gamma spectra analysis[J]. Journal of Instrumentation, 2008, 3(1):01001.
[9] ALAMANIOTIS M, MATTINGLY J, TSOUKALAS L H. Pareto-optimal gamma spectroscopic radionuclide identification using evolutionary computing[J]. IEEE Transactions on Nuclear Science, 2016, 60(3):2222-2231.
[10] IAEA. Identification of radioactive sources and devices. Reference manual[M]. Vienna, Austria. IAEA, 2011.
[11] 仇小鹏, 杨平利, 田传艳. 基于VC++. Net开发Geant4数值模拟程序[J]. 计算机仿真, 2007, 24(6):255-258, 262. QIU X P, YANG P L, TIAN C Y. Development of Geant4 numerical simulation program with VC++.Net[J]. Computer Simulation, 2007, 24(6):255-258, 262. (in Chinese)
[12] DING S, CHANG X H. A MATLAB-based study on the realization and approximation performance of RBF neural networks[J]. Applied Mechanics and Materials, 2013, 325-326:1746-1749.
[13] RAWLINGS J O, PANTULA S G, DICKEY D A. Applied regression analysis[M]. New York, USA:Springer, 1998.
[14] 陈雄达. 数学实验:下[M]. 上海:同济大学出版社, 2018. CHEN X D. Mathematical experiments:Part II[M]. Shanghai:Tongji University Press, 2018. (in Chinese)
[15] BERTSEKAS D P. Nonlinear programming[M]. 2nd ed. Cambridge, UK:Athena Scientific, 1999.
[16] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of International Conference on Neural Networks. Perth, Australia, 1995:1942-1948.
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