矿物油三维荧光谱的小波变换奇异值特征  

WT-SVD Feature of Three Dimensional Fluorescence Spectra of Mineral Oil

作　　者：田广军[1] 杨子臣[2] 

机构地区：[1]燕山大学测试计量技术及仪器河北省重点实验室,河北秦皇岛066004 [2]海湾安全技术有限公司,河北秦皇岛066004

出　　处：《激光与光电子学进展》2015年第6期296-301,共6页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(20577038);河北省自然科学基金(D2004000195)

摘　　要：针对矿物油三维荧光谱特征提取的奇异值分解方法(SVD)容易忽略重要小成分特异信息的不足,提出小波变换(WT)和SVD相结合的特征提取方法。利用WT获取矿物油三维荧光谱数据的低频主部近似分量和不同方向的细节分量;用SVD提取综合矩阵的奇异值特征;使用模糊C均值聚类(FCM)方法对矿物油三维荧光谱样本数据进行分类识别,并引入随机噪声进行进一步测试。结果表明WT-SVD特征向量在矿物油分类识别方面比单独SVD特征向量具有准确度高、稳健性强的优势,有助于更好地实现矿物油聚类分析或种类鉴别。Singular value decomposition （SVD） has a shortcoming in feature extraction of mineral oil＇ s 3D fluorescence spectrum, as it easily discards small eigen values that may be important for identification. A new method that combines wavelet transform （WT） and SVD in feature extraction is presented. Wavelet approximation components of mineral oil＇s 3D fluorescence data and detail components in different directions are obtained, and their singular value feature is extracted. The fuzzy clustering method （FCM） is used to classify or discriminate mineral oils, and a further test is carried out with random noise introduced. The result shows that WT-SVD feature vector is superior to SVD in mineral oil classification or oil identification, with higher accuracy and robustness than SVD in anti-jamming performance.

关 键 词：光谱学 小波变换奇异值特征 三维荧光谱 矿物油 

分 类 号：O657.3[理学—分析化学] O433.4[理学—化学]