發(fā)布時間：2018-06-25 14:43

  本文選題：提升小波 + 去噪　； 參考：《光譜學(xué)與光譜分析》2016年07期

【摘要】：礦物油的使用是造成霧霾等空氣污染問題的重要原因。礦物油熒光光譜檢測系統(tǒng)光譜消噪處理的有效性和快速性是在線實時監(jiān)測系統(tǒng)的熱點問題。研究應(yīng)用提升算法小波變換(LWT)礦物油熒光光譜去噪的方法。與傳統(tǒng)的離散小波變換(DWT)相比,提升小波變換將現(xiàn)有的小波濾波器分解成基本的構(gòu)造模塊,分步驟完成變換,結(jié)構(gòu)簡單,運算速度快。在礦物油熒光光譜去噪過程中具有運算量低、原位運算和便于實現(xiàn)的特點,有效解決了傳統(tǒng)小波變換在這方面的不足。提升算法的小波變換、傳統(tǒng)離散小波變換和經(jīng)驗?zāi)B(tài)變換(EMD)分別運用到0#柴油、97#汽油、煤油三種礦物油的熒光光譜去噪中,評價去噪效果指標的信噪比(SNR)、重構(gòu)均方根誤差(RMSE)和波形相似度(NCC)證明了提升方法小波變換用于礦物油熒光光譜去噪的有效性。同時,提升算法變換能提高構(gòu)造的靈活性和運算的簡單性使消噪時間降低62%,證明了提升算法的小波變換運用到礦物油熒光光譜去噪中的快速性,適于礦物油實時消噪處理系統(tǒng)。
[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.
【作者單位】： 燕山大學(xué)河北省儀器科學(xué)與技術(shù)重點實驗室;
【基金】：國家自然科學(xué)基金項目(61471312) 河北省自然科學(xué)基金項目(F2015203240,F2015203072)資助
【分類號】：O657.3

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本文編號：2066359