|
|
|
| Research on Decomposition of Spectral Overlapping Peaks Based on GMM-ICSA |
| ZHU Dan-dan1,SUN Shi-cheng1,YANG Hui1,ZHANG Can1,ZHU Qi-guang2,LI Kang3,SHI Yan-xin3,CHEN Ying1 |
1. Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, YanshanUniversity, Qinhuangdao, Hebei 066004, China
2. Key Laboratory of Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
3. Center for Hydrogeology & Environmental Geology, Geological Environment Monitoring Engineering Technology Innovation Center of The Ministry of Natural Resources, Baoding, Hebei 071051, China |
|
|
|
|
Abstract Aiming at the difficulties in modeling the element concentration caused by the overlapping peaks of Pb and As in X-ray fluorescence spectroscopy (XRF) and the weak robustness of the crow search algorithm (CSA), the Gaussian mixture model (GMM) is combined with the improved the crow search algorithm (ICSA) to achieve the decomposition of overlapping peaks. Compared with CSA, ICSA has three main improvements: the introduction of the “crow feeding” feature to better connect GMM and ICSA; the fixed probability of awareness is changed to a gradient type to make the population iteration more diverse; appropriate adjustments the global optimization strategy makes the algorithm more stable and converges faster. The comparison experiment between the GMM model and the GMM-ICSA model shows that the decomposition accuracy of the optimized model is increased by 4.93%. At the same time, the mean square error and iteration time are used to measure the quality of the search algorithm, and ICSA is compared with the same type comparative experiments on the four algorithms show that the mean square error and iteration time of ICSA are better than other algorithms, which shows the feasibility of the improved algorithm in dealing with the problem of overlapping peaks.
|
|
Received: 24 August 2020
Published: 18 October 2021
|
|
|
| Fund:NationalKeyResearchandDevelopmentProject;Key R&D Projects in Hebei Province;Natural Science Foundation of Hebei Province;Hebei Province Postdoctoral Selection Funding Project;Science and Technology Research Project of Hebei Province;China Postdoctoral Fund Project |
|
|
|
［1］陈颖, 张灿, 肖春艳, 等. 基于GWO-SVR的土壤镉元素含量含水率校正预测模型研究［J］. 光学学报, 2020, 40(10): 180-187.
Chen Y, Zhang C, Xiao C Y, et al. Study on Prediction Model of Soil Cadmium Content Moisture Content Correction Based on GWO-SVR［J］. Acta Optica Sinica, 2020, 40(10): 180-187.
［2］Solé V, Papillon E, Cotte M, et al. A Multiplatform Code for the Analysis of Energy-dispersive X-ray Fluorescence Spectra［J］. Spectrochimica Acta Part B: Atomic Spectroscopy, 2006, 62(1): 63-68.
［3］叶国阳, 徐科军. 气相色谱仪数据处理方法研究和软件研制［J］. 计量学报, 2017, 38(5): 532-537.
Ye G Y, Xu K J. Research on Data Processing Method and Software Development of Gas Chromatograph［J］. Acta Metrologica Sinica, 2017, 38(5): 532-537.
［4］Li F, Ge L Q, Tang Z Y, et al. Recent developments on XRF Spectra Evaluation［J］. Applied Spectroscopy Reviews, 2020, 55(4) : 263-287.
［5］赵彦涛, 何永强, 贾利颖, 等. 基于时间序列单维卷积神经网络的水泥熟料游离钙软测量方法［J］. 计量学报, 2020, 41(9): 1152-1162.
Zhao Y T, He Y Q, Jia L Y, et al. Soft Measurement Method for Cement Clinker CaO Based on Time Series Single-dimensional Convolutional Neural Network ［J］. Acta Metrologica Sinica, 2020, 41(9): 1152-1162.
［6］韩博, 姜洋海, 高奎贺, 等. 一种新型的地区机动车排放因子计算方法研究［J］. 计量学报, 2019, 40(2): 343-346.
Han B, Jiang Y H, Gao K H, et al. Research on a Novel Calculation Method of Regional Vehicle Emission ［J］. Acta Metrologica Sinica, 2019, 40(2): 343-346.
［7］黄洪全, 丁卫撑, 龚迪琛, 等. 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, 35(8): 2320-2323.
Huang H Q, Ding W C, Gong D T, et al. X-ray Fluorescence Overlapping Peak Decomposition Based on Statistical Genetic Algorithm［J］. Spectroscopy and Spectral Analysis, 2015, 35(8): 2320-2323.
［8］陶维亮, 刘艳, 王先培, 等. 时频域分形维数分析的光谱信号重叠峰解析算法［J］. 光谱学与光谱分析, 2017, 37(12): 3664-3669.
Tao W L, Liu Y, Wang X P, et al. Resolution Algorithm for Overlapping Peaks of Spectral Signal Based on Fractal Dimension Analysis in Time-frequency Domain［J］. Spectroscopy and Spectral Analysis, 2017, 37(12): 3664-3669.
［9］曾国强, 罗耀耀, 葛良全, 等. 种群算法在能量色散X荧光重叠谱拟合中的应用［J］. 光谱学与光谱分析, 2014, 34(2): 562-564.
Zeng G Q, Luo Y G, Ge L Q, et al. The Application of Population Algorithm in the Fitting of Energy Dispersive X Fluorescence Overlapping Spectra［J］. Spectroscopy and Spectral Analysis, 2014, 34(2): 562-564.
［10］赵世杰, 高雷阜, 于冬梅, 等. 基于变因子加权学习与邻代维度交叉策略的改进CSA算法［J］. 电子学报, 2019, 47(1): 40-48.
Zhao S J, Gao L F, Yu D M, et al. Improved CSA Algorithm Based on Variable Factor Weighted Learning and Adjacent Generation Dimension Cross Strategy［J］. Electronic Journal, 2019, 47(1): 40-48.
［11］Qiao J, Cai X Z, Xiao Q, et al. Data on MRI Brain Lesion Segmentation Using K-means and Gaussian Mixture Model-Expectation Maximization［J］. Data in Brief, 2019, 27: 104628.
［12］李婧. 基于GMM的EM优化算法的应用与研究［D］. 哈尔滨: 哈尔滨工程大学, 2018.
［13］钟金琴, 辜丽川, 檀结庆, 等. 基于分裂EM算法的GMM参数估计［J］. 计算机工程与应用, 2012, 48(34): 28-32.
Zhong J Q, Gu L C, Shan J Q, et al. GMM Parameter Estimation Based on Split EM Algorithm［J］. Computer Engineering and Applications, 2012, 48(34): 28-32.
［14］Askarzadeh A. A Novel Metaheuristic Method for Solving Constrained Engineering Optimization Problems: Crow Search Algorithm［J］. Computers and Structures, 2016, 169: 1-12.
［15］Zamani H, Nadimi-Shahraki H M, Gandomi A H. CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems［J］. Applied Soft Computing Journal, 2019, 85: 1-48.
［16］Aljarah I, Mafarja M, Heidari A A, et al. Asynchronous Accelerating Multi-leader Salp Chains for Feature Selection［J］. Applied Soft Computing Journal, 2018.
［17］Khosravi V, Ardejani F D, Yousefi S, et al. Monitoring Soil Lead and Zinc Contents Via Combination of Spectroscopy with Extreme Learning Machine and Other Data Mining Methods［J］. Geoderma, 2018, 318: 29-41.
［18］Wu W, Chen Y L. Application of Isolation Forest to Extract Multivariate Anomalies from Geochemical Exploration Data［J］. Global Geology, 2018, 21(1): 36-47.
［19］Silva E A, Weindorf D C, Silva S H G, et al. Advances in Tropical Soil Characterization Via Portable X-ray Fluorescence Spectrometry［J］. Pedosphere, 2019, 29(4): 468-482.
［20］刘浩然, 常金凤, 庞娜娜, 等. 基于改进混合遗传细菌觅食算法的贝叶斯结构学习算法［J］. 计量学报, 2020, 41(9): 1122-1126.
Liu H R, Chang J F, Pang N N, et al. Bayesian Network Structure Learning Based on Improved Hybrid Genetic Bacterial Foraging Algorithm ［J］. Acta Metrologica Sinica, 2020, 41(9): 1122-1126. |
|
|
|
2021, Vol. 42 
