核能谱测量中重叠谱峰解析的算法研究
发布时间：2018-08-25 11:09
【摘要】：在核科学技术给人类的生活带来便捷服务和清洁能源的同时,人们逐渐开始关注核辐射给环境和身体带来的影响。通常辐射环境中存在的放射性物质都会释放出γ射线,通过对γ射线的测量,可以了解放射性物质中的核素种类,判断核素的含量和活度等。然而现实测量中,环境或其他干扰射线的影响会导致谱信号出现频繁的重叠现象。常用的γ射线探测器中,NaI(Tl)探测仪由于其探测效率高、维护方便、价格适中等优点被广泛使用,但其对能量相近的重叠峰分辨能力不强,这使得重叠峰的分解成为谱分析中的难题。因此基于此背景,本文根据γ能谱的统计分布规律,在MATLAB平台上运用期望最大值法、遗传算法和粒子群算法完成了对模拟重叠峰的分解。论文主要工作及成果如下:1、首先讨论了能谱及其数学模型,然后根据能谱的统计涨落特性,在MATLAB平台上模拟出了原始的重叠峰谱线,并作为后续算法的研究对象,为误差分析依据。2、针对期望最大值法在解决重叠峰分解问题时计算时间过长的缺陷,提出一种快速算法,并且有效的利用该算法完成了重叠峰的分解任务。3、简单阐述了遗传算法的优越性并对重叠峰进行分解:将待求的解集空间和解集空间中的解看作遗传算法中的染色体和基因,结合遗传算法工具箱,通过一系列的选择及遗传操作后,在全局模式找出最符合原始重叠峰的参数组合。4、寻找粒子群算法与重叠峰分解之间的联系,完成初始参数的讨论、适应度函数的选择、粒子评价、粒子的位置更新以及个体极值与全局极值的更新等工作,最终得到良好的分解效果;其次运用该算法完成232Th和226Ra核素的实际重叠峰的分解任务。5、运用期望最大值算法、遗传算法及粒子群算法均实现了双峰重叠峰及三峰重叠峰的分解工作。双峰重叠峰分解中:当初始参数均未知时三种方法可以分解的最小的峰位间距分别为17KeV、13KeV和5KeV;当初始峰位已知时最大期望值法可以完成8KeV道值窗的重叠峰分解,且遗传算法的权重和标准偏差的误差均有所降低。在三峰重叠峰分解中:利用峰位与偏差的关联性可以提高最大期望值法和遗传算法的参数精度;对于粒子群算法,即使在初始参数均未知时,也可以完成185KeV、195KeV及203KeV三峰位的分解,且分解结果较好。理论上本文研究的三种算法均可以实现能量相近的多峰重叠峰的分解工作,且解效果较好,对于实际的分辨率低的重叠峰分解问题具有一定的参考价值。
[Abstract]:While nuclear science and technology bring convenient service and clean energy to human life, people begin to pay more and more attention to the influence of nuclear radiation on environment and body. Usually the radioactive material in the radiation environment releases 纬-ray. Through the measurement of 纬-ray the types of nuclides in the radioactive material can be understood and the content and activity of the radionuclides can be judged. However, the influence of environmental or other interference rays will lead to frequent overlapping of spectral signals in real measurement. The commonly used 纬 -ray detector, Nai (Tl) detector, is widely used because of its high detection efficiency, convenient maintenance and moderate price, but its resolution ability to overlapping peaks with similar energy is not strong. This makes the decomposition of overlapping peaks a difficult problem in spectral analysis. Therefore, based on the statistical distribution of 纬 energy spectrum, the expected maximum value method, genetic algorithm and particle swarm optimization algorithm are used to decompose the overlapped peaks on MATLAB platform. The main work and results are as follows: firstly, the energy spectrum and its mathematical model are discussed. Then, according to the statistical fluctuation characteristics of energy spectrum, the original overlapping peak lines are simulated on the MATLAB platform, which is regarded as the research object of the subsequent algorithms. In order to solve the problem of overlapping peak decomposition, a fast algorithm is proposed to solve the problem of overlapping peak decomposition. And effectively use the algorithm to complete the overlapping peak decomposition task. 3, the advantages of genetic algorithm and decomposition of overlapping peaks: the solution set space and solution set space solution space as the chromosomes and genes in genetic algorithm. Combined with genetic algorithm toolbox, after a series of selection and genetic operation, the parameter combination. 4 is found out in the global mode, and the relation between particle swarm optimization algorithm and overlapping peak decomposition is found, and the discussion of initial parameters is completed. The selection of fitness function, particle evaluation, the update of particle position and the update of individual extremum and global extremum, etc., finally get a good decomposition effect. Secondly, the algorithm is used to decompose the actual overlapping peaks of 232Th and 226Ra. The expected maximum value algorithm, genetic algorithm and particle swarm optimization algorithm are used to decompose the overlapping peaks of two peaks and three peaks. When the initial parameters are unknown, the minimum peak spacing of the three methods is 17KeV 13KeV and 5KeV, respectively. When the initial peak position is known, the maximum expectation value method can complete the overlapping peak decomposition of the 8KeV channel window. The weight and the error of standard deviation of genetic algorithm are reduced. In the three-peak overlapping peak decomposition, the maximum expectation value method and genetic algorithm can be improved by using the correlation between peak position and deviation. For particle swarm optimization, even when the initial parameters are unknown, the decomposition of the three peaks of 185KeV and 203KeV can be completed. The results of decomposition are good. Theoretically, the three algorithms studied in this paper can realize the decomposition of multi-peak overlapped peaks with similar energy, and the results are good, which has a certain reference value for the practical overlapping peak decomposition problem with low resolution.
【学位授予单位】：成都理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TL81;TP18
本文编号：2202697
[Abstract]:While nuclear science and technology bring convenient service and clean energy to human life, people begin to pay more and more attention to the influence of nuclear radiation on environment and body. Usually the radioactive material in the radiation environment releases 纬-ray. Through the measurement of 纬-ray the types of nuclides in the radioactive material can be understood and the content and activity of the radionuclides can be judged. However, the influence of environmental or other interference rays will lead to frequent overlapping of spectral signals in real measurement. The commonly used 纬 -ray detector, Nai (Tl) detector, is widely used because of its high detection efficiency, convenient maintenance and moderate price, but its resolution ability to overlapping peaks with similar energy is not strong. This makes the decomposition of overlapping peaks a difficult problem in spectral analysis. Therefore, based on the statistical distribution of 纬 energy spectrum, the expected maximum value method, genetic algorithm and particle swarm optimization algorithm are used to decompose the overlapped peaks on MATLAB platform. The main work and results are as follows: firstly, the energy spectrum and its mathematical model are discussed. Then, according to the statistical fluctuation characteristics of energy spectrum, the original overlapping peak lines are simulated on the MATLAB platform, which is regarded as the research object of the subsequent algorithms. In order to solve the problem of overlapping peak decomposition, a fast algorithm is proposed to solve the problem of overlapping peak decomposition. And effectively use the algorithm to complete the overlapping peak decomposition task. 3, the advantages of genetic algorithm and decomposition of overlapping peaks: the solution set space and solution set space solution space as the chromosomes and genes in genetic algorithm. Combined with genetic algorithm toolbox, after a series of selection and genetic operation, the parameter combination. 4 is found out in the global mode, and the relation between particle swarm optimization algorithm and overlapping peak decomposition is found, and the discussion of initial parameters is completed. The selection of fitness function, particle evaluation, the update of particle position and the update of individual extremum and global extremum, etc., finally get a good decomposition effect. Secondly, the algorithm is used to decompose the actual overlapping peaks of 232Th and 226Ra. The expected maximum value algorithm, genetic algorithm and particle swarm optimization algorithm are used to decompose the overlapping peaks of two peaks and three peaks. When the initial parameters are unknown, the minimum peak spacing of the three methods is 17KeV 13KeV and 5KeV, respectively. When the initial peak position is known, the maximum expectation value method can complete the overlapping peak decomposition of the 8KeV channel window. The weight and the error of standard deviation of genetic algorithm are reduced. In the three-peak overlapping peak decomposition, the maximum expectation value method and genetic algorithm can be improved by using the correlation between peak position and deviation. For particle swarm optimization, even when the initial parameters are unknown, the decomposition of the three peaks of 185KeV and 203KeV can be completed. The results of decomposition are good. Theoretically, the three algorithms studied in this paper can realize the decomposition of multi-peak overlapped peaks with similar energy, and the results are good, which has a certain reference value for the practical overlapping peak decomposition problem with low resolution.
【学位授予单位】：成都理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TL81;TP18
【参考文献】
相关期刊论文 前10条
1 王丽;冯燕;;基于粒子群优化的图像稀疏分解算法研究[J];计算机仿真;2015年11期
2 黄洪全;丁卫撑;龚迪琛;方方;;基于统计遗传算法的X射线荧光重叠峰分解[J];光谱学与光谱分析;2015年08期
3 曾立晖;王南萍;田贵;;NaI(Tl)伽玛能谱全谱数据高斯分解软件的实现与应用[J];核电子学与探测技术;2011年12期
4 沈晴;徐筠;康慧珍;卜建平;郭文刚;;通过数学方法进行重叠峰分解的国内外研究现状综述[J];价值工程;2011年04期
5 黄洪全;何子述;方方;龚迪琛;丁卫撑;;多重谱峰的分解方法[J];原子能科学技术;2010年09期
6 黄洪全;方方;龚迪琛;丁卫撑;;GMM模型在核能谱平滑滤波中的应用[J];核技术;2010年05期
7 秦小辉;;Matlab遗传算法工具箱在物流网络设计中的应用[J];铁道运输与经济;2009年01期
8 杜祥琬;;让核技术为国家可持续发展再创辉煌[J];中国工程科学;2008年01期
9 裴少英;王南萍;;航空γ能谱全谱数据的高斯分解方法[J];科技导报;2006年11期
10 屈国普,凌球,郭兰英,赵立宏,陈坚祯;NaI(Tl)闪烁谱仪谱漂移原因分析[J];南华大学学报(自然科学版);2005年01期
相关博士学位论文 前1条
1 刘永刚;γ能谱谱数据分解方法研究[D];中国地质大学（北京）;2011年
相关硕士学位论文 前5条
1 魏晋军;粒子群优化算法的改进及应用[D];太原理工大学;2015年
2 居凤霞;粒子群优化算法的改进及应用[D];华南理工大学;2014年
3 张宏东;EM算法及其应用[D];山东大学;2014年
4 曹道友;基于改进遗传算法的应用研究[D];安徽大学;2010年
5 岳佳;基于EM算法的模型聚类的研究及应用[D];江南大学;2007年
，本文编号：2202697
本文链接：https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2202697.html
