小波分析与漏磁检测信号处理

详细介绍了小波分析的基本理论,并将其引入到钢管漏磁探伤信号的分析中,可以去除噪声、压缩数据和检测奇异性,而且为缺陷的定性和定量分析提供了新的判定方法.     

基于小波变换的超声信号自适应消噪方法研究

超声检测中，特定的缺陷回波信号一般都有一定的相关性，因而可利用自适应噪声抵消来增强缺陷回波信号。针对最小均方（LMS）算法自适应噪声抵消的缺点，提出了基于小波变换的自适应噪声抵消方法，通过Matlab软件将该算法应用于超声缺陷信号的仿真处理。结果表明，该算法大大提高了缺陷回波信号的信噪比，且具有较高的缺陷定位精度和纵向分辨率。     

Characterization of pressure signals in a bubble column by wavelet packet transform

All experiments of pressure fluctuations were carried out in a bubble column with a moderately large column of 0.376 m ID. The recently developed technique of wavelet packet transform based on localized wavelet functions is applicable to analysis of the fluctuating signals. The time series of pressure fluctuation signals have been analyzed by means of wavelet packet transform components, decomposition through best basis algorithm and timefrequency representation. By resorting to this technique, the objects in bubbly flow regime have fine scales and frequencies than ones in churn-turbulent flow regime. Thus, this wavelet packet transform method enables us to obtain the frequency content of local complex flow behaviors in a bubble column.     

Interannual and intra-annual cycles of satellite-derived chlorophyll-a concentrations in the Caspian Sea

Variability of phytoplankton in the Caspian Sea (CS) is related to the complex orography, the variety of physical and chemical processes, and climatic indices. Here, interannual and intra-annual variability of satellite-derived chlorophyll-a concentrations (Chl-a) were studied using wavelet analysis during 2002–2019 in different regions of the CS. Self-Organizing Maps (SOM) analysis performed to classify the CS into the areas of similar variability of satellite-derived Chl-a. Wavelet spectral analysis showed that the Chl-a variability regulated by four primary periodic cycles: 0.5-year, 1-year, 2 to 2.5-year, and 3 to 5-year. The 0.5 and 1-year wavelet cycles mostly depicted the intensity of seasonality patterns. The 2–2.5-year and 3–5-year cycles of Chl-a showed non-stationary coherence with corresponding low-frequency cycles of NAO and ENSO. The 3–5-year wavelet amplitudes of Chl-a strongly correlated with NAO and ENSO in the southern CS. Weak correlations of 2–2.5-year cycle wavelet amplitudes of Chl-a with NAO and ENSO suggested that variations do not always directly translate to a biological response. A negative anomaly in the Chl-a autumn peaks observed during 2011–2016 in the middle and southern CS, when NAO phases were persistently positive. The interannual variations of summer peaks in the northern CS, and autumn peaks in the middle and southern CS were broadly related to the precipitation. SST and wind stress. Moreover, it was shown that the Volga discharge has a significant influence on Chl-a variability in the northern CS.     

Examination of scale-invariant characteristics of epileptic electroencephalograms using wavelet-based analysis

There is evidence that biological and physiological systems including the brain exhibit can exhibit fractal characteristics that can be used to identify the state of the system. In this study, wavelet-based fractal analysis is used to examine self-similar or scale-invariant characteristics of intracranial EEG data in terms of the spectral exponent. The intracranial EEG data were recorded from subjects with epilepsy during non-seizure period and during epileptic seizure activity. From the computational results, it is observed that the self-similar or scale-invariant characteristics of the intracranial EEG data obtained during these two periods are significantly different. The actual value of the estimated spectral exponent depends on the wavelet bases used for the computations.     

一种改进的小波域图像修复算法

针对全变分小波图像修复算法在平滑区易产生阶梯效应并且噪声抑制不充分的缺陷,提出一种新颖的小波域图像修复模型。通过优化一个全变分和图像梯度的二范数的能量泛函来实现小波域的图像修复,使得在去除噪声的同时较好地保留了图像的边缘,在平滑区域削弱了阶梯效应,并利用有限差分法对所建立的扩散方程进行数值求解。实验结果表明:所提方法对于不同丢失比例的图像以及噪声图像都取得了较好的修复效果,尤其当小波系数丢失率较高时更为明显。     

语音识别中小波分析的应用

语音信号是一种时变、非平稳的随机信号。现有语音识别系统的适应性比较差,尤其在高噪声环境下的语音识别非常困难,若将语音识别系统商品化,还有识别速度、拒识问题以及关键词／句检测技术等细节问题需要解决。如何把语言学、生理学、心理学方面的研究成果量化、建模并用于语音识别,还需深入研究。本文介绍了语音识别中小波分析的研究现状及几种常见的技术方法,并且分析探讨了语音识别中小波分析的应用和发展前景。     

基于小波分析的储粮害虫图像去噪研究

以储粮害虫为研究对象,阐述了小波去噪的基本原理和高频系数置零、硬阈值法、软阈值等几种方法,并提出了一种改进的小波阈值去噪方法,比较了这几种方法在粮虫图像去噪上的优缺点。实验结果表明,改进的新阈值方法能更好的去除噪声,收到更好的效果。     

Gasoline quality prediction using gas chromatography and FTIR spectroscopy: An artificial intelligence approach

This paper reports on analysis of 45 gasoline samples with different qualities, namely, octane number and chemical composition. Measurements of data from gas chromatography and IR (FTIR) spectroscopy are used to gasoline quality prediction and classification. The data were processed using principal component analysis (PCA) and fuzzy C means (FCM) algorithm. The data were then analyzed following the neural network paradigms, hybrid neural network and support vector machines (SVM) classifier. The IR spectra were compressed and de-noised by the discrete wavelet analysis. Using the hybrid neural network and multi linear regression method (MLRM), excellent correlation between chemical composition of the gasoline samples and predicted value of the octane number was obtained. About 100% correct classification for six different categories of the gasoline was achieved, each of which has different qualities.