小波分析与蚁群神经网络5′-硝基水杨基荧光酮分光光度法同时测定钨钼锑

提出了钨、钼、锑-5′-硝基水杨基荧光酮-CTMAB的新显色体系,并用于钨、钼、锑三组分光度分析。为解析钨、钼和锑的重叠光谱,提高多组分同时测定的准确度,将蚁群神经网络(ACAN)与小波分析相结合,建立一种利用小波变换提取重叠光谱信息,再用蚁群神经网络解析的方法,此方法应用于合金钢标准样品中钨、钼、锑多组分同时测定,测定值与认定值相符,相对误差分别为0.14%、0.13%、0.013%。     

基于MATLAB的电化学滴定曲线导数变换的实现

利用MATLAB软件的命令行编程、一维离散小波的工具箱GUI功能和Simulink仿真三种方式,以电位滴定曲线为例,分别对电化学滴定曲线进行导数变换,以提高仪器检出信号和曲线突跃点确定的精度,为电化学定量检测提供一种有效的方法。此三种导数变换方式可以应用于一些基础电化学仪器的定量分析,尤其是对未配备相关计算软件的读数型电位滴定仪十分有效。本文所介绍的例程、例举可使电化学工作者在进行相关参数设置或导入、导出设定后直接应用到电化学分析实际工作。     

基于复数小波的X射线荧光光谱本底扣除法

在X射线荧光光谱(XRF)分析中,本底能否有效扣除对分析结果的精确性有很大影响。针对实数小波变换系数在奇点附近有正负振荡及缺乏平移不变性等问题,提出了一种使用近似解析的复数小波扣除本底的新方法。该复数小波变换通过两个实数小波来实现,所以方法兼有实数小波局部时频分析和多分辨率等优点。采用复数小波分解光谱后,利用低频逼近重构信号得到本底。为了检验其有效性,对一个模拟的光谱和实验测得的光谱进行了本底扣除,并通过分析比较了方法对特征峰净峰面积和峰位置的影响,实验结果显示该复数小波得到的本底比一般实数小波的本底更为精确,证明了方法可以有效扣除本底并能保持峰面积等有效信号不变。     

基于小波多分辨率分析和能量交叉法的小目标检测

小目标检测问题是目标跟踪领域的难点,将小波引入到小目标检测中,利用小波分析的多分辨率特性和时频局部窗特性能够克服传统方法的局限.本文结合小波分析和小目标的特性,在理论分析的基础上提出了基于小波变换和能量特征的检测小目标的方法.最后给出了比较满意的实验结果.     

基于小波变换的TEM信号处理中小波基函数的选择

瞬变电磁法(TEM)作为一种时间域电磁勘探方法,具有探测深度大,受地形影响小等优点。随着对数据处理精度要求越来越高,近些年出现了使用小波变换来对TEM信号去噪的方法。由于小波基的选择对于小波变换是一个重要问题,小波基函数对去噪的效果好坏影响也很大。通过试验MATLAB小波工具箱中Coif及Bior小波函数的去噪效果,从去噪结果上分析,Coif小波函数去噪效果更好。     

基于脊波变换的手指静脉图像增强研究

针对人体手指静脉图像的结构和特点,提出一种基于脊波变换的图像增强算法.该算法对手指静脉图像小波域各高频子带系数进行脊波变换,利用非线性新脊域系数确定法对脊域系数进行处理,然后对各高频子带进行脊波逆变换和小波图像重构.该算法对手指静脉二维曲线奇异处理、边缘增强等具有较好的效果,克服了小波变换在高维曲线奇异和方向选择上的不足.通过与传统的二维离散小波变换边缘增强法和自适应调整系数的脊波变换方法作比较,实验结果表明本算法具有更好的手指静脉图像效果.     

基于小波变换的爆破振动信号分解与重构

针对非平稳爆破振动信号采用Fourier变换只能在时域或频域变换的问题,提出将时间和频率结合起来分析非平稳信号的方法--小波分析法.利用小波分析法对同一爆破振动信号采用不同小波基进行分析,说明了差异性.并通过不同的小波基对实测的爆破振动信号的分解与重构,以重构误差的大小确定最优小波基.     

基于小波变换的闪烁背光LCD响应时间估算

为了更准确、方便地估算闪烁背景光下的LCD液晶响应时间,利用小波变换法对测量得到的LCD亮度响应曲线进行滤波.通过实际测量连续背景光下五级灰度之间的液晶响应以及闪烁背景光的背景光响应,构造了闪烁背景光下的LCD亮度响应,利用小波变换滤波方法,通过小波分解、阈值处理、逆变换重构,还原了液晶响应信号,将估算得到的响应时间与真实值以及移动窗口积分方法的估算值进行比较.根据计算,移动窗口积分法的平均估算误差为7.535 5%,而小波变换滤波法的平均估算误差仅为1.966 5%.结果表明,小波变换滤波法对闪烁背景光技术下LCD液晶响应时间的估计比移动窗口积分法更加准确,而且应用简单,适应面广.     

小波变换在铁矿石中非铁矿类物质分析的应用

利用小波变换处理图像数据已经在多领域有广泛的应用,因此该技术同样可应用于铁矿石矿相图的分析。为了分辨铁矿石中的夹杂物、非铁矿类物质或冶金废料,一些检验机构采用了矿相分析手段。介绍了基于Matlab的小波变换,对不同铁矿石矿相结构的特征信息进行提取,从而完成矿相的近似体积比定量分析,并通过分析、分类特征信息,对不同矿相图...     

基于小波包分解和支持向量机的石油套管缺陷智能识别

 针对石油套管缺陷超声无损检测(NDT)中缺陷回波的特点,提出了一种基于小波包分解和支持向量机(SVM)的缺陷智能识别新方法。分析了Gabor、小波和小波包3种信号时频变换分解方法的特点,并进行了基于3种方法生成的特征数据可分性比较,确定了小波包分解方法效果最好。根据SVM解决分类问题的原理,采用SVM法对3种时频分解提取的缺陷信号特征数据进行识别。试验表明,基于小波包分解局部熵的特征提取结合SVM模式智能识别的组合方法,可应用于石油套管上的4种典型缺陷的识别。     

Roll Eccentricity Compensation Based on AntiAliasing Wavelet Analysis Method

 Roll eccentricity is an important factor causing thickness variations during hot strip rolling and might define the limit of strip thickness control accuracy. An improved multi resolution wavelet transform algorithm was proposed to compensate for the roll eccentricity. The wavelet transform method had good localization characteristics in both the time and frequency domains for signal analysis; however, the wavelet method had a frequency aliasing problem owing to the less than ideal cut off frequency characteristics of wavelets. This made its component reconstruction of an inaccurate signal. To eliminate inherent frequency aliases in the wavelet transform, fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) were combined with the Mallat algorithm. This synthesis was described in detail. Then, the roll eccentricity component was extracted from rolling force signal. An automatic gauge control (AGC) system added with a multi resolution wavelet analyzer was designed. Experimental results showed that the anti aliasing method could greatly restrain the inverse effect of eccentricity and the thickness control accuracy was improved from ±40 μm to ±15 μm.     

Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms

We are developing a computer-aided diagnosis (CAD) scheme for detection of clustered microcalcifications in digital mammograms. The use of an empirically chosen wavelet and scale combination for detection of microcalcifications as an initial step of the CAD scheme has been reported by us previously. In this study, we developed a technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of our CAD scheme based on the supervised learning method. In the learning process, an error function was formulated to represent the difference between a desired output and the reconstructed image obtained from weighted wavelet coefficients for a given mammogram. The error function was then minimized by modifying the weights for wavelet coefficients by means of a conjugate gradient algorithm. The Least Asymmetric Daubechies' wavelets were optimized with 297 regions of interest (ROIs) as a training set by a jackknife method. The performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis by use of the above set of ROIs. The analysis yielded an average area under the ROC curve of 0.92, which outperforms the difference-image technique used in our existing CAD scheme, as well as the partial reconstruction method used in our previous study.     

Prominent catalytic activity of mesoporous molecular sieves in the vapor phase dehydration of cyclohexanol to cyclohexene

Cerium incorporated KIT-6 mesoporous materials were synthesized through direct hydrothermal method and characterized by using X-ray diffraction (XRD), nitrogen sorption isotherm (BET), Fourier transform infrared spectroscopy (FT-IR), inductively coupled plasma -atomic emission spectroscopy (ICP-AES), diffuse reflectance ultraviolet visible spectroscopy (DRS-UV-Vis), thermogravimetric analysis (TGA), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) methods. It appeared that stable cerium ions were inserted into the silica framework of KIT-6, thus generating acid properties in their host materials. The catalytic activity of Ce-KIT-6 materials was evaluated in the vapor phase dehydration of cyclohexanol to cyclohexene and dicyclohexyl ether at different temperatures with various Si/Ce molar ratios. Ce-KIT-6 (25) showed higher activity with 54% cyclohexanol conversion and 64% selectivity to cyclohexene. The catalytic results indicated that Ce-KIT-6 mesoporous materials could be used as versatile and stable acid catalysts.     

基于小波包重构的汽车发动机故障诊断

利用小波包对信号进行分解，分解的层数越多，频域分辨率越高，但时域分辨率越低，对故障信号的精确诊断不利，为提高时域分辨率，本文采用小波包重构算法，对发动机气缸振动信号的特定频段进行了小波包重构，并由此判断汽车发动机气缸燃烧振动的具体状态。     

多分辨率小波极限学习机

针对一类具有空间不均匀性的辨识和回归问题,提出了基于小波分析的极限学习机方法.从多分辨率分析的思想出发,构造一簇紧支撑正交小波作为隐层激活函数,并利用改进的误差最小化极限学习机训练输出层权重,避免了新加入高分辨率子网络后的重新训练.同时,由一维多分辨分析的张量积构造了二维多分辨小波极限学习机.进而通过脊波变换将小波学习机扩展到高维空间,对脊波函数的伸缩、方向和位置参数进行优化计算.对具有奇异性的函数仿真结果证明,与标准极限学习机相比,小波极限学习机由于其聚微性能在极短的训练时间内更好地逼近目标.一些实际基准回归问题上的测试验证了脊波极限学习机在其中大部分问题上达到更高的训练和泛化精度.      

基于主成分分析与遗传算法-支持向量机的喷溅预测方法

针对冶炼过程喷溅特征提取及喷溅预测困难的问题,提出基于小波包变换与主成分分析的优化参数模型的支持向量机喷溅预测方法。该方法经小波包变换将冶炼喷溅的噪声和氧枪振动信号分解为不同频带的信号。由于不同频带的信号出现相互干扰和堆叠,因此通过主成分分析将频带能量降维分离成不同频带,进而将这些处理后的信号作为喷溅特征向量。对支持向量机模型参数(C、g)进行遗传算法优化,通过支持向量机对喷溅的分类及预测,验证了该方法的有效性。实验结果表明：经小波包变换和主成分分析获得的特征信号能够准确地反应喷溅特征,提出的支持向量机方法具有较好的分类性能,喷溅预测准确率较高。     

Medication side effects in anxious patients: negative placebo responses?

Heart sounds produce an incessant noise during lung sounds recordings. This noise severely contaminates the breath sounds signal and interferes in the analysis of lung sounds. In this paper, the use of a wavelet transform domain filtering technique as an adaptive de-noising tool, implemented in lung sounds analysis, is presented. The multiresolution representations of the signal, produced by wavelet transform, are used for signal structure extraction. In addition, the use of hard thresholding in the wavelet transform domain results in a separation of the nonstationary part of the input signal (heart sounds) from the stationary one (lung sounds). Thus, the location of the heart sound noise (1st and 2nd heart sound peaks) is automatically detected, without requiring any noise reference signal. Experimental results have shown that the implementation of this wavelet-based filter in lung sound analysis results in an efficient reduction of the superimposed heart sound noise, producing an almost noise-free output signal. Due to its simplicity and its fast implementation the method can easily be used in clinical medicine.     

Wavelet Transform Analysis of Open Channel Wake Flows

Wavelet transform analysis offers a new approach to signal processing through its ability to decompose signals in both time and frequency. As such, it is more suited to nonstationary and intermittent signals than traditional Fourier analysis. The first part of this paper provides an introduction to the theory and signal processing properties of both continuous and discrete wavelet transform analysis. An account is then given of the application of wavelet transform analysis to a variety of experimental open channel wake flows. Feature location is undertaken using a continuous wavelet transform, and both turbulent statistical analysis and thresholding of the turbulent signal components are undertaken using a discrete wavelet transform.     

基于小波神经网络的管道腐蚀缺陷定量识别研究

漏磁检测是目前广泛采用的油气管道检测方法,通过漏磁检测仪获得腐蚀缺陷漏磁场切向分量的漏磁场信号,提取与腐蚀缺陷外形长、宽、深有关的漏磁场信号波形特征量,结合小波分析和神经网络的优势形成小波神经网络,分析设计了小波神经网络的结卡句,给出了网络训练算法,利用网络的非线性逼近性能,对腐蚀缺陷外形进行定量评价,给出预测评价曲线,试验验证方法有效可行。