| 引用本文: | 张江梅,季海波,王坤朋,冯兴华.稀疏表示与多任务学习的复杂核素识别[J].哈尔滨工业大学学报,2018,50(10):72.DOI:10.11918/j.issn.0367-6234.201711013 |
| ZHANG Jiangmei,JI Haibo,WANG Kunpeng,FENG Xinghua.Sparse representation and multi-task learning based complex nuclide identification[J].Journal of Harbin Institute of Technology,2018,50(10):72.DOI:10.11918/j.issn.0367-6234.201711013 |
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| 摘要: |
| 为提高核探测器在复杂环境下测量的适应性，提出了一种能谱校正和核素识别方法.针对核信号探测过程中，由于环境温度的交替变化会出现γ能谱偏移导致多核素识别率低的问题，提出了一种基于稀疏表示和多任务学习的核素识别方法.首先建立一个用于描述环境变量对于当前测量能谱影响的迁移矩阵，其次对测量能谱进行建模，该模型可以表示为标准能谱中独立核素能谱的瞬时叠加，由此核素识别问题就转化为多种核素能谱稀疏分解的问题，为求解该非凸优化问题采用交替方向乘子法(ADMM)的多任务学习方法同时优化迁移矩阵并进行稀疏分解，实现多核素识别.为验证该方法的可行性和有效性，利用高低温交变试验箱对CsI(Tl)探测器的测量环境进行模拟，分别测量得到11种核素和典型混合核素的实际放射性元素能谱数据，以及基于蒙特卡洛分析软件Geant4仿真IAEA规定的27种核素的单一与混合核素数据进行实验.结果表明，提出的方法即使在温度为：-20℃~50℃的环境下依然可以准确地识别多种常用核素.
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| 关键词: 核素识别 能谱校正 多任务学习 稀疏表示 ADMM |
| DOI：10.11918/j.issn.0367-6234.201711013 |
| 分类号:TL817 |
| 文献标识码:A |
| 基金项目:国家自然科学基金 (61501385); 四川省科技支撑计划(2016GZ0210); 四川省科技厅应用基础项目(2016JY0242) |
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| Sparse representation and multi-task learning based complex nuclide identification |
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ZHANG Jiangmei1,2,JI Haibo1,WANG Kunpeng2,FENG Xinghua2
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(1.School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China; 2.School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China)
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| Abstract: |
| A spectra calibration method and a radionuclide identification method are developed for the improvement of the adaptability to the nuclear detector measurement in complex environment. In view of the low identification rate of multiple nuclides caused by γ-ray energy spectrum shifting with temperature change, we propose a radionuclide identification method based on sparse representation and multi-task learning. Firstly, a transfer matrix was constructed to represent the environment variation affecting currently measured spectra. Then, the model of the measurement spectra was established, which was used to describe the instantaneous superposition of scale copies of individual nuclide sub-spectra in standard spectra library. Thus, the problem of radionuclide identification was transformed into the problem of sparse decomposition of various radionuclides. In order to solve this non-convex optimization problem, the multi-task learning method based on alternating direction multiplier method (ADMM) was developed to optimize the transfer matrix and decompose the sparse matrix simultaneously. The feasibility and effectiveness of the developed method were verified by some experiments, in which the measurement environment of CsI (Tl) detector was simulated by using the programmable temperature and humidity chamber and the real radioactive spectrum data of 11 kinds of nuclides and typical mixed nuclides were measured, respectively. Meanwhile, the single and mixed nuclide data of 27 kinds of nuclides were used, which are specified by the simulation IAEA of Monte Carlo analysis software Geant4. The experiment results show that the developed method can accurately identify a variety of commonly used nuclides even at temperature range of -20~50℃.
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| Key words: nuclide identification spectra calibration multi-task learning sparse representation ADMM |
