【摘要】：激光誘導(dǎo)擊穿光譜技術(shù)(Laser-induced breakdown spectroscopy, LIBS)是一種新型的元素分析技術(shù),具有實(shí)時(shí)在線、非接觸、多種元素同時(shí)探測等優(yōu)點(diǎn),是光譜分析領(lǐng)域的一種前沿性分析手段,在石油、冶金、地質(zhì)、環(huán)保、軍事、航天等領(lǐng)域都有廣泛的應(yīng)用。但由于其昂貴的成本,核心技術(shù)的壟斷以及可能涉及到的重要戰(zhàn)略作用,使其引進(jìn)受限。因此研制開發(fā)功能更加完善,具備自主知識產(chǎn)權(quán),并且成本較為低廉的創(chuàng)新型激光光譜儀器具有重要的理論意義和現(xiàn)實(shí)意義。 論文在綜述LIBS光譜數(shù)據(jù)分類算法的基礎(chǔ)上,對光譜預(yù)處理、光譜定性和定量分析以及光譜識別與解析等方面進(jìn)行優(yōu)化設(shè)計(jì)、算法實(shí)現(xiàn)及軟件集成。主要研究工作包括：(1)針對LIBS光譜數(shù)據(jù)特點(diǎn),引入魯棒的主成份分析算法(Robust Principal Component Analysis, RPCA)對光譜數(shù)據(jù)濾噪和降維,并利用支持向量機(jī)(Support Vector Machine,SVM)分類模型進(jìn)行分類,提高了預(yù)測準(zhǔn)確率,通過對比實(shí)驗(yàn)驗(yàn)證了方法的有效性和可行性；(2)針對單一分類器分類效果不穩(wěn)定的缺陷,通過AdaBoost組合分類模型將偏最小二乘法和支持向量機(jī)混合構(gòu)建分類模型,實(shí)現(xiàn)了9種牌號圓鋼的有效分類,實(shí)驗(yàn)驗(yàn)證了文中算法可大大提高模型分類正確率和分類模型的穩(wěn)定性；(3)最終設(shè)計(jì)并開發(fā)了集譜圖解析、定性與定量分析、識別與感知功能于一體的“LIBS光譜預(yù)處理與分析系統(tǒng)”。 研究結(jié)果可應(yīng)用于光譜數(shù)據(jù)分類的研究,并為創(chuàng)新型激光光譜儀器的數(shù)據(jù)分析與處理提供所需的支持。
[Abstract]:Laser induced breakdown spectroscopy (Laser-induced breakdown spectroscopy, LIBS) is a new element analysis technology, which has the advantages of real-time on-line, non-contact, simultaneous detection of multiple elements and so on. It is a leading analytical method in the field of spectral analysis, which is used in petroleum, metallurgy, and so on. Geology, environmental protection, military, aerospace and other fields have been widely used. However, due to its high cost, monopoly of core technology and the important strategic role that may be involved, its import is limited. Therefore, it is of great theoretical and practical significance to develop innovative laser spectrometer with more perfect functions, independent intellectual property rights and low cost. On the basis of summarizing the classification algorithms of LIBS spectral data, this paper optimizes the design, algorithm realization and software integration of spectral preprocessing, spectral qualitative and quantitative analysis, spectral identification and analysis. The main research work includes: (1) according to the characteristics of LIBS spectral data, a robust principal component analysis (Robust Principal Component Analysis, RPCA) algorithm is introduced to filter noise and reduce the dimension of spectral data, and the support vector machine (SVM) classification model is used to classify the spectral data, which improves the prediction accuracy. The effectiveness and feasibility of the method are verified by comparative experiments. (2) aiming at the unstable effect of single classifier, the partial least square method and support vector machine are combined to construct the classification model by AdaBoost combined classification model. The experimental results show that the algorithm can greatly improve the classification accuracy and stability of the model. (3) finally, we design and develop the set spectrum analysis, qualitative and quantitative analysis. LIBS spectral preprocessing and analysis system with recognition and sensing functions. The results can be applied to the classification of spectral data and provide the necessary support for the data analysis and processing of the innovative laser spectrometer.
【學(xué)位授予單位】：西北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.4;TP301.6

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