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Selection of spectral data for classification of steels using laser-induced breakdown spectroscopy
Kong HY(孔海洋); Sun LX(孙兰香); Hu JT(胡静涛); Xin Y(辛勇); Cong ZB(丛智博)
Department信息服务与智能控制技术研究室
Source PublicationPlasma Science and Technology
ISSN1009-0630
2015
Volume17Issue:11Pages:964-970
Indexed BySCI ; EI ; CSCD
EI Accession number20154601555150
WOS IDWOS:000367515100014
CSCD IDCSCD:5550997
Contribution Rank1
Funding OrganizationNational High Technology Research and Development Program of China (863 Program) (No. 2012AA040608), National Natural Science Foundation of China (Nos. 61473279, 61004131) and the Development of Scientific Research Equipment Program of Chinese Academy of Sciences (No. YZ201247)
KeywordLaser-induced Breakdown Spectroscopy Classification Of Steel Samples Principal Component Analysis Artificial Neural Networks Selection Of Spectral Data
AbstractPrincipal component analysis (PCA) combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the influence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selected spectral partitions can obtain the best results. A perfect result with 100% classification accuracy can be achieved using the intensive spectral partitions ranging of 357-367 nm.
Language英语
WOS HeadingsScience & Technology ; Physical Sciences
WOS SubjectPhysics, Fluids & Plasmas
WOS KeywordARTIFICIAL NEURAL-NETWORKS ; MULTIVARIATE-ANALYSIS ; QUANTITATIVE-ANALYSIS ; IDENTIFICATION ; LIBS ; CHINA ; MODEL
WOS Research AreaPhysics
Citation statistics
Cited Times:2[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/17293
Collection信息服务与智能控制技术研究室
Corresponding AuthorSun LX(孙兰香)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
3.CAS Key Laboratory of Networked Control System, Shenyang, China
Recommended Citation
GB/T 7714
Kong HY,Sun LX,Hu JT,et al. Selection of spectral data for classification of steels using laser-induced breakdown spectroscopy[J]. Plasma Science and Technology,2015,17(11):964-970.
APA Kong HY,Sun LX,Hu JT,Xin Y,&Cong ZB.(2015).Selection of spectral data for classification of steels using laser-induced breakdown spectroscopy.Plasma Science and Technology,17(11),964-970.
MLA Kong HY,et al."Selection of spectral data for classification of steels using laser-induced breakdown spectroscopy".Plasma Science and Technology 17.11(2015):964-970.
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