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题名: Selection of spectral data for classification of steels using laser-induced breakdown spectroscopy
作者: Kong HY(孔海洋); Sun LX(孙兰香); Hu JT(胡静涛); Xin Y(辛勇); Cong ZB(丛智博)
作者部门: 信息服务与智能控制技术研究室
关键词: laser-induced breakdown spectroscopy ; classification of steel samples ; principal component analysis ; artificial neural networks ; selection of spectral data
刊名: Plasma Science and Technology
ISSN号: 1009-0630
出版日期: 2015
卷号: 17, 期号:11, 页码:964-970
收录类别: SCI ; EI ; CSCD
产权排序: 1
项目资助者: National 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)
摘要: Principal 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.
语种: 英语
WOS记录号: WOS:000367515100014
WOS标题词: Science & Technology ; Physical Sciences
类目[WOS]: Physics, Fluids & Plasmas
关键词[WOS]: ARTIFICIAL NEURAL-NETWORKS ; MULTIVARIATE-ANALYSIS ; QUANTITATIVE-ANALYSIS ; IDENTIFICATION ; LIBS ; CHINA ; MODEL
研究领域[WOS]: Physics
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/17293
Appears in Collections:信息服务与智能控制技术研究室_期刊论文

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Recommended Citation:
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.
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