本文關(guān)鍵詞：基于向量空間模型的巖屑LIBS光譜分類識別方法　出處：《光譜學(xué)與光譜分析》2017年09期 　論文類型：期刊論文

  更多相關(guān)文章： 激光誘導(dǎo)擊穿光譜 向量空間模型 巖屑 分類識別

【摘要】：向量空間模型最初用于文獻檢索,該模型是通過對文獻內(nèi)容進行特征文本提取后,將文獻轉(zhuǎn)換到文本向量空間,然后在文本向量空間中通過計算文獻的特征文本向量與檢索文本的特征文本向量的相似度,實現(xiàn)文獻的檢索,該方法基于模式識別中模板匹配的最近鄰原則。針對光譜數(shù)據(jù)的特點和模式識別中模板匹配的基本原則,將向量空間模型引入基于樣品光譜的分類識別。通過訓(xùn)練集中光譜數(shù)據(jù)獲得各樣品的光譜數(shù)據(jù)模板,提取訓(xùn)練集中各樣品光譜數(shù)據(jù)模板特征峰的波長和相對強度信息,構(gòu)建特征峰信息數(shù)據(jù)庫,計算獲得特征峰信息權(quán)值,將光譜數(shù)據(jù)轉(zhuǎn)換到特征峰向量空間,獲得各樣品光譜數(shù)據(jù)模板的特征峰向量,構(gòu)建樣品特征峰向量數(shù)據(jù)庫。同理獲得預(yù)測集樣品光譜的特征峰向量,在特征峰向量空間中通過計算預(yù)測集樣品特征峰向量與樣品特征峰向量數(shù)據(jù)庫中各樣品模板特征峰向量的余弦值,完成對預(yù)測集樣品的分類識別。以巖屑樣品的LIBS光譜為研究對象,將向量空間模型應(yīng)用于LIBS光譜的分類識別。分類結(jié)果表明,該方法能夠?qū)崿F(xiàn)對巖屑樣品LIBS全譜的快速分類識別,且在對預(yù)測集光譜數(shù)據(jù)進行平均處理后,分類準確率為100%。提出的基于向量空間模型的LIBS光譜分類方法可以拓展應(yīng)用于其他光譜數(shù)據(jù)的分類識別。
[Abstract]:Vector space model was used for document retrieval, this model is based on the literature content features of text extraction, document conversion to text vector space, and then through the similarity calculation of text vector literature and retrieval of text feature vector text in text vector space, the method of literature retrieval, based on the principle of the nearest neighbor template in pattern recognition, according to the basic principle of template matching. The characteristics of spectral data and pattern recognition in the vector space model is introduced, based on the classification of sample spectra. The spectral data obtained by the training set of spectral data template of each sample, extracting the training set of the spectra data template characteristic peak wavelength and relative intensity information. The construction of characteristic peak information database, obtained the characteristic peaks of weights, convert the spectral data to obtain the characteristic peaks of the vector space. The characteristic peaks of vector sample spectra data template, sample build characteristic peak vector database. This prediction set of characteristic peak vector sample spectra, characteristic peaks in the vector space by calculating the characteristic peaks of each sample prediction vector template sample vector and the sample characteristic peak characteristic peaks in the database vector cosine, complete classification of samples the prediction set. In LIBS spectra of debris samples as the research object, classification of vector space model is applied to the LIBS spectra. The classification results show that this method can achieve the full spectrum of debris samples LIBS rapid classification, and the average spectral data of the prediction set, the classification accuracy of classification and recognition of LIBS spectral classification method based on vector space model can be applied to other spectral data for 100%. is proposed.

【作者單位】： 中國海洋大學(xué)光學(xué)光電子實驗室;
【基金】：國家自然科學(xué)基金項目(41503063,41106080)資助
【分類號】：TN249
【正文快照】： 引言激光誘導(dǎo)擊穿光譜技術(shù)(laser induced breakdown spec-troscopy,LIBS)是一種高能量脈沖激光擊穿樣品產(chǎn)生瞬態(tài)等離子體的原子發(fā)射光譜技術(shù),已經(jīng)越來越多地滲透到各個研究和應(yīng)用領(lǐng)域,如:環(huán)境監(jiān)測[1]、生物組織分析[2-4]、材料成分在線監(jiān)測[5]等。LIBS具有實時、原位、多元素，

本文編號：1363612