文献详情
Laser induced breakdown spectroscopy for quantitative analysis based on low-rank matrix approximations
作者Yi, Cancan[1];Lv, Yong[2];Xiao, Han[3];Tu, Shan[4]
文献类型期刊
机构
通讯作者Xiao, H (reprint author), Wuhan Univ Sci & Technol, Key Lab Met Equipment & Control Technol, Minist Educ, Wuhan 430081, Hubei, Peoples R China.; Xiao, H (reprint author), Wuhan Univ Sci & Technol, Hubei Key Lab Mech Transmiss & Mfg Engn, Wuhan 430081, Hubei, Peoples R China.
期刊名称JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY影响因子和分区
2017
32
11
页码范围2164-2172
增刊正刊
收录情况SCI(E)(WOS:000414343900006)  EI(20174504372818)  
期刊等级A1
所属部门耐火材料与冶金国家重点实验室
百度学术Laser induced breakdown spectroscopy for quantitative analysis based on low-rank matrix approximations
语言外文
ISSN0267-9477
DOI10.1039/c7ja00178a
被引频次9
人气指数5586
浏览次数1309
基金National Natural Science Foundation of China [51475339, 51105284, 11664003]; Natural Science Foundation of Hubei province [2016CFA042]; State Key Laboratory of Refractories and Metallurgy, the Wuhan University of Science and Technology [ZR201603]; Guangxi Key Laboratory of Optoelectronic Information Processing Open Foundation of China [KFJJ2017-01, KFJJ2016-01]; Research Project of Hubei Education [Q20131104]
关键词laser-induced breakdown spectroscopy; low-rank matrix approximation; convex optimization; quantitative analysis.
摘要In quantitative laser-induced breakdown spectroscopy (LIBS) analysis, spectral signals are usually represented by the linear combination of characteristic peaks with useful spectral information and unwanted noise components. All of the existing regression analysis methods are related to a spectral data matrix, which is composed of certified samples with different spectral intensity. Therefore, spectral data matrix processing is critical for quantitative LIBS analysis. A prevalent assumption when constructing a matrix approximation is that the partially observed matrix is of low-rank. Moreover, the low-rank structure always reflects the useful information and is regarded as a powerful data preprocessing method. In this paper, a novel and quantitative LIBS analysis method based on a sparse low-rank matrix approximation via convex optimization is proposed. Based on the sparsity of the spectral signals, we present a convex objective function consisting of a data-fidelity term and two parameterized penalty terms. To improve the accuracy of the quantitative analysis, a new non-convex and non-separable penalty based on the Moreau envelope is proposed. Then, the alternating direction method of multipliers (ADMM) algorithm was utilized to solve the optimization problem. The proposed method was applied to the quantitative analysis of 23 high alloy steel samples. Both of the performances of the Partial Least Squares (PLS) and Support Vector Machine (SVM) regression models are improved by using the low-rank matrix approximation scheme, which proves the effectiveness of the proposed method.
全部评论(0 条评论)
作者其他论文

基于四分位偏差分形维与高斯混合模型的故障识别算法研究.

肖涵;李友荣;吕勇.振动工程学报.2008,21(1),79-83.

基于加权相空间重构降噪及样本熵的齿轮故障分类.

吕勇;李友荣;肖涵,等.振动工程学报.2009,22(5),462-466.

基于递归定量分析与高斯混合模型的齿轮故障识别.

肖涵;李友荣;吕勇.振动工程学报.2011,24(1),84-88.

基于相空间重构与独立分量分析的局部独立投影降噪算法.

黄艳林;李友荣;肖涵,等.振动与冲击.2011,30(1),33-36.

Review of Bolted Connection Monitoring.

Wang, Tao;Song, Gangbing;Liu, Shaopeng,等.INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS.2013.

登录