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石油类污染物是造成雾霾等空气污染问题的重要原因.去噪处理的有效性是石油类污染物荧光光谱检测中的热点问题.提出一种基于经验模态分解-提升小波变换(EMD-LWT)相结合的低浓度石油类污染物荧光光谱去噪方法.经验模态分解法(EMD)可自适应地滤除微弱荧光信号中的噪声,但去噪过程中第一个本征模态函数(IMF)包含的频率范围过宽,影响了去噪准确性和有效性.引入提升小波变换(LWT)对IMF1实现更精细的分解,有效分离出IMF1的有用信息,改善信噪分离效果.将EMD-LWT联用方法和传统的EMD或LWT去噪法分别运用于煤油荧光光谱检测中,仿真结果表明,与只用EMD或LWT相比,EMD-LWT相结合的光谱去噪法得到的信噪比和均方根误差均显著提高,验证了该方法的有效性和可行性.
Petroleum pollutants are the main causes of air pollution such as haze and haze.The effectiveness of denoising is a hot issue in the detection of petroleum pollutants.This paper presents a new method based on empirical mode decomposition-lifting wavelet transform (EMD- LWT), the empirical mode decomposition (EMD) can adaptively filter out the noise in the weak fluorescence signal, but the first eigenmode The frequency range covered by the function (IMF) is too wide, which affects the accuracy and effectiveness of denoising. The LWT is introduced to finer IMF1 decomposition, effectively separating the useful information of IMF1 and improving the effect of signal-noise separation. The EMD-LWT combination method and the traditional EMD or LWT denoising method are respectively applied to the kerosene fluorescence spectrometry. The simulation results show that compared with EMD or LWT only, the EMD-LWT combination of spectral denoising method Signal to noise ratio and root mean square error were significantly improved, validated the effectiveness and feasibility of the method.