摘要:
针对超短脉冲激光与气体相互作用产生的非线性荧光光谱的特点,提出了一种新的光谱去噪、特征提取、特征匹配的有效方法.通过对三种气体不同浓度的荧光光谱进行小波压缩去噪,得到5层664个小波系数,重构后获得除噪效果满意而特征信息保持不变的光谱.对重构后的光谱进行光谱峰/谷沿相似度的分析,构造了三种气体的特征谱.在此基础上,经对三种气体光谱进行特征峰,谷沿的匹配分析,提出一种利用同种物质不同浓度下的光谱总体相似度来构造标准特征谱新方法,通过与标准特征谱的匹配分析得到分类结果.实验结果表明,该算法的分类结果受浓度变化影响小,对低至0.05%浓度条件下的全部27条光谱均获得100%的分类识别结果.
Abstract:
A pre-process of feature extraction and classification approach based on spectrum edges matching is proposed to analyze the complicated nonlinear fluorescence spectra emitted by the interaction between femto-second (fs) laser and the impurities in air. The spectra data is denoised and compressed from 3979 points to 664 points using wavelet (WT) transform. By similarity analysis we create the characteristic spectra of 3 kinds of gases and the weights for classification. A new method of classification is proposed based on the edges matching and the comparison with characteristic spectra. Compared with existing methods, our method can not only get 100% classification accuracy, but also gives the characteristic position and the matching degree. The analysis of the matching degree shows that our method works well at low concentrations and has a potential application of identifying gases of lower concentration.
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