基于提升小波变换的矿物油荧光光谱去噪研究

发布时间：2018-06-25 14:43

本文选题：提升小波 + 去噪　；参考：《光谱学与光谱分析》2016年07期

【摘要】：矿物油的使用是造成雾霾等空气污染问题的重要原因。矿物油荧光光谱检测系统光谱消噪处理的有效性和快速性是在线实时监测系统的热点问题。研究应用提升算法小波变换(LWT)矿物油荧光光谱去噪的方法。与传统的离散小波变换(DWT)相比,提升小波变换将现有的小波滤波器分解成基本的构造模块,分步骤完成变换,结构简单,运算速度快。在矿物油荧光光谱去噪过程中具有运算量低、原位运算和便于实现的特点,有效解决了传统小波变换在这方面的不足。提升算法的小波变换、传统离散小波变换和经验模态变换(EMD)分别运用到0#柴油、97#汽油、煤油三种矿物油的荧光光谱去噪中,评价去噪效果指标的信噪比(SNR)、重构均方根误差(RMSE)和波形相似度(NCC)证明了提升方法小波变换用于矿物油荧光光谱去噪的有效性。同时,提升算法变换能提高构造的灵活性和运算的简单性使消噪时间降低62%,证明了提升算法的小波变换运用到矿物油荧光光谱去噪中的快速性,适于矿物油实时消噪处理系统。
[Abstract]:The use of mineral oil is an important cause of air pollution such as smog. The efficiency and rapidity of spectral denoising in the fluorescence spectrum detection system of mineral oil is a hot issue in on-line real-time monitoring system. The lifting algorithm wavelet transform (LWT) is applied to de-noising the fluorescence spectrum of mineral oil. Compared with the traditional discrete wavelet transform (DWT), the lifting wavelet transform decomposes the existing wavelet filter into the basic construction module, completes the transformation step by step, the structure is simple, the operation speed is fast. In the process of fluorescence spectrum denoising of mineral oil, it has the characteristics of low computation, in situ operation and easy to realize, which effectively solves the shortcoming of traditional wavelet transform in this respect. The lifting algorithm of wavelet transform, traditional discrete wavelet transform and empirical mode transform (EMD) are applied to the fluorescence spectrum denoising of three kinds of mineral oils, namely, 0# diesel oil, 97# gasoline and kerosene, respectively. The signal-to-noise ratio (SNR) of the denoising effect index, the reconstruction of root mean square error (RMSE) and waveform similarity (NCC) proved the effectiveness of lifting wavelet transform in the denoising of mineral oil fluorescence spectrum. At the same time, the lifting algorithm transform can improve the flexibility of structure and the simplicity of operation, so the denoising time can be reduced by 62%. It is proved that the wavelet transform of lifting algorithm can be applied to the de-noising of mineral oil fluorescence spectrum quickly, and it is suitable for the real-time de-noising processing system of mineral oil.
【作者单位】：燕山大学河北省仪器科学与技术重点实验室;
【基金】：国家自然科学基金项目(61471312) 河北省自然科学基金项目(F2015203240,F2015203072)资助
【分类号】：O657.3

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