Research on PSO and MLEM Hybrid Algorithm for NDP Spectrum Unfolding

LI Yuanhui; YANG Rui; ZHANG Qingxian; XIAO Caijin; CHEN Hongjie; XIAO Hongfei; CHENG Zhiqiang

doi:10.7538/yzk.2023.youxian.0642

Atomic Energy Science and Technology > 2024 > 58(5): 1152-1159. > DOI: 10.7538/yzk.2023.youxian.0642

LI Yuanhui, YANG Rui, ZHANG Qingxian, XIAO Caijin, CHEN Hongjie, XIAO Hongfei, CHENG Zhiqiang. Research on PSO and MLEM Hybrid Algorithm for NDP Spectrum Unfolding[J]. Atomic Energy Science and Technology, 2024, 58(5): 1152-1159. DOI: 10.7538/yzk.2023.youxian.0642

Citation:

PDF (5984 KB)

Research on PSO and MLEM Hybrid Algorithm for NDP Spectrum Unfolding

1. Key Lab of Applied Nuclear Techniques in Geoscience Sichuan, Chengdu University of Technology, Chengdu 610059, China;
2. China Nuclear Medical Industry Management Co., Ltd., Beijing 100097, China;
3. Beijing Research Institute of Uranium Geology, Beijing 100029, China;
4. China Institute of Atomic Energy, Beijing 102413, China

More Information

Received Date: September 02, 2023
Revised Date: September 30, 2023
Available Online: May 24, 2024

Graphical Abstract

Abstract

Abstract

Neutron depth profiling (NDP) is a non-destructive analysis method which is widely used in lithium batteries, semiconductors, and other complex and high-precision industries. The NDP spectrum is the second particles of the interaction between neutrons and target nuclides, and then the content and spatial information of the target nuclides in the measured samples are obtained by unfolding the NDP spectrum. At present, the common NDP spectrum unfolding algorithm is the maximum likelihood expectation maximization (MLEM) algorithm. But in some case, the MLEM algorithm falls into the local optimal solution. In this paper, a hybrid PSO-MLEM algorithm by taking advantages of the wide search range of PSO (particle swarm optimization) and the fast convergence speed of MLEM was proposed. In the PSO-MLEM algorithm, the dynamic acceleration factor was used to balance the local optimal and the global optimal on the particle displacement in each iteration, which improved the convergence speed and the accuracy of the algorithm. The PSO-MLEM algorithm was applied to unfold the NDP spectra of lithium batteries with 0, 5, and 10 hours of charging and discharging, which were simulated by Geant4 tool. The unfolding results of PSO-MLEM algorithm were compared to the results of PSO algorithm, MLEM algorithm and singular value decomposition solving least squares (SVDLS) algorithm. The correlation coefficients of the unfolding result by PSO-MLEM algorithm and the reference distributions are 0.993, 0.984, and 0.946, respectively, and the relative average errors are 14.46%, 9.84%, and 9.41%. Compared with PSO algorithm, the convergence speed of PSO-MLEM algorithm is improved from 800 times to 100 times, and the relative error is reduced from about 50% to about 10%. To the MLEM algorithm, the PSO-MLEM algorithm improves the global optimization capability and avoids the problem of local optimal solution caused by the influence of the initial value of the MLEM algorithm, especially with the result of 0 hour. The SVDLS algorithm is worked well in unfolding NDP spectra except the NDP spectrum of lithium battery at 0 hour. Compared to result of SVDLS algorithm, the PSO-MLEM algorithm has better convergence properties and is numerically stable.

FullText(HTML)

References (17)

References

[1]	唐婵娟,肖才锦,姚永刚,等. CARR中子深度剖面分析系统设计[J]. 核技术,2019,42(4):040402. TANG Chanjuan, XIAO Caijin, YAO Yonggang, et al. Development of neutron depth profiling system at CARR[J]. Nuclear Techniques, 2019, 42(4):040402(in Chinese).
[2]	杨鑫,李润东,王冠博,等. 中子深度分析反演算法研究[J]. 计算物理,2017,34(5):603-610. YANG Xin, LI Rundong, WANG Guanbo, et al. Study on unfolding algorithms for neutron depth profiling[J]. Chinese Journal of Computational Physics, 2017, 34(5):603-610(in Chinese).
[3]	SHI C, XIAO C J, YAO Y G, et al. Inverse iteration algorithm for neutron depth profiling[J]. Journal of Radioanalytical and Nuclear Chemistry, 2018, 317(1):81-85.
[4]	杨鑫,李润东,刘汉刚,等. 基于概率迭代的NDP反演方法[J]. 计算物理,2012,29(6):891-900. YANG Xin, LI Rundong, LIU Hangang, et al. An unfolding method of NDP based on probability iteration[J]. Chinese Journal of Computational Physics, 2012, 29(6):891-900(in Chinese).
[5]	KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ICNN'95:International Conference on Neural Networks. Australia:[s. n.], 1995.
[6]	王建龙,孙合明. 基于EM算法和PSO算法的混合优化算法[J]. 计算机仿真,2013,30(5):330-333. WANG Jianlong, SUN Heming. Hybrid optimization algorithm based on EM algorithm and PSO algorithm[J]. Computer Simulation, 2013, 30(5):330-333(in Chinese).
[7]	EBERHART R C, SHI Y H. Particle swarm optimization:Developments, applications and resources[C]//Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat No 01TH8546).[S. l.]:[s. n.], 2001.
[8]	滕志军,吕金玲,郭力文,等. 基于动态加速因子的粒子群优化算法研究[J]. 微电子学与计算机,2017,34(12):125-129. TENG Zhijun, LV Jinling, GUO Liwen, et al. Research on particle swarm optimization based on dynamic acceleration coefficients[J]. Microelectronics & Computer, 2017, 34(12):125-129(in Chinese).
[9]	汤峥,宋余庆,刘哲. 基于粒子群优化和EM算法的图像聚类研究[J]. 小型微型计算机系统,2015,36(7):1602-1606. TANG Zheng, SONG Yuqing, LIU Zhe. Research on clustering based on improved particle swarm optimization and expectation maximization algorithm[J]. Journal of Chinese Computer Systems, 2015, 36(7):1602-1606(in Chinese).
[10]	戴前伟,江沸菠. 基于混沌振荡PSO-BP算法的电阻率层析成像非线性反演[J]. 中国有色金属学报,2013,23(10):2897-2904. DAI Qianwei, JIANG Feibo. Nonlinear inversion for electrical resistivity tomography based on chaotic oscillation PSO-BP algorithm[J]. The Chinese Journal of Nonferrous Metals, 2013, 23(10):2897-2904(in Chinese).
[11]	雷开友. 粒子群算法及其应用研究[D]. 重庆:西南大学,2006.
[12]	NICKABADI A, EBADZADEH M M, SAFABAKHSH R. A novel particle swarm optimization algorithm with adaptive inertia weight[J]. Applied Soft Computing, 2011, 11(4):3658-3670.
[13]	SHAHABINEJAD H, SOHRABPOUR M. A novel neutron energy spectrum unfolding code using particle swarm optimization[J]. Radiation Physics and Chemistry, 2017, 136:9-16.
[14]	ZHENG X, QU G R, ZHOU J J. Accelerated strategy for the MLEM algorithm[J]. Journal of X-Ray Science and Technology, 2021, 29(1):135-149.
[15]	LIU J, QIU X. A novel hybrid PSO-BP algorithm for neural network training[C]//2009 International Joint Conference on Computational Sciences and Optimization. Sanya, China:[s. n.], 2009.
[16]	白彦辉,李旭超. EM与PSO算法在图像统计模型拟合中的应用[J]. 科学技术与工程,2013,13(1):236-239. BAI Yanhui, LI Xuchao. The application of EM and PSO algorithm to fitting the image statistical model[J]. Science Technology and Engineering, 2013, 13(1):236-239(in Chinese).
[17]	LIU D X, WANG J H, PAN K, et al. In situ quantification and visualization of lithium transport with neutrons[J]. Angewandte Chemie, 2014, 126(36):9652-9656.

Cited By

Get Citation

PDF

XML

Article views (39) PDF downloads (21)

Turn off MathJax

Article Contents

Abstract

References

Research on PSO and MLEM Hybrid Algorithm for NDP Spectrum Unfolding

Abstract

References

Catalog

Export File

Citation

Format

Content