基于径向基神经网络仿真γ能谱模板库的核素识别方法  

Radionuclide identification method based on a gamma-spectra template library simulated by radial basis function neural networks

作　　者：杜晓闯 涂红兵 黎岢 张洁 王康 刘鹤敏 梁漫春 汪向伟 DU Xiaochuang;TU Hongbing;LI Ke;ZHANG Jie;WANG Kang;LIU Hemin;LIANG Manchun;WANG Xiangwei(State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment,China Nuclear Power Engineering Co.,Ltd.,Shenzhen 518172,China;Institute of Public Safety Research,Department of Engineering Physics,Tsinghua University,Beijing 100084,China;Beijing Global Safety Technology Co.,Ltd.,Beijing 100094,China)

机构地区：[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

出　　处：《清华大学学报（自然科学版）》2021年第11期1308-1315,共8页Journal of Tsinghua University(Science and Technology)

基　　金：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)。

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。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.

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 

分 类 号：TL929[核科学技术—核燃料循环与材料]