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Identification of the Mineral Oil Fluorescence Spectroscopy Based on the ICA and SVM

机译:基于ICA和SVM鉴定矿物油荧光光谱法

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摘要

Spectral features of the different kinds of the mineral oil are different from each other, so the oil can be identified by the spectral data based on the principle. The composed of it with aromatic hydrocarbons structure is very often complicated, which caused the three-dimensional fluorescence spectroscopy of the different oil are various. The characteristic of the oil style-book are difficult to be maintained by the simple formula when the three-dimensional fluorescence spectroscopy technology is used to identify the species of the mineral oil. In this paper, the independent component analysis (ICA) is used to do the matrix decomposition from the perspective of independence to extract the main feature of the spectroscopy. The support vector machine (SVM) is used to assort the main characteristic root books which are abstracted by the ICA. The species identification of the mineral oil will be realized by it. The identification result is visualized by the parallel coordinate's graph. The experiment results show that it is effective to extract the main feature of the spectroscopy. The classify speed is greatly increased. The discrimination is 98.56. It can effectively realize the identification of the oils.
机译:不同种类的矿物油的光谱特征彼此不同,因此可以基于原理通过光谱数据识别油。用芳烃结构组成非常复杂,这导致不同油的三维荧光光谱是各种各样的。当三维荧光光谱技术用于识别矿物油种时,简单的公式难以通过简单的公式维持油样式书的特征。在本文中,独立的分量分析(ICA)用于从独立性的角度进行矩阵分解,以提取光谱的主要特征。支持向量机(SVM)用于分配由ICA提取的主要特征根书籍。矿物油的物种鉴定将通过它实现。通过并行坐标的图表可视化识别结果。实验结果表明,提取光谱的主要特征是有效的。分类速度大大增加。歧视为98.56。它可以有效地实现油的鉴定。

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