基于经验模态分解方法的拉曼光谱信号处理研究

经验模态分解(EMD)方法是一个以信号内在物理频率成分为对象的自适应时频分析方法,而常见的非平稳信号分析方法,比如小波分析,它需要选择小波基,不同小波基的分析结果不同;拉曼光谱信号是典型的非线性和非平稳信号,EMD方法充分地保留了信号本身的非线性和非平稳的特征,适应于拉曼光谱信号的分析。实验在自行研制的拉曼光谱测试平台上获得了原始的拉曼光谱信号,并通过经验模态分解将信号分解成不同频率的10个本征模式分量,信号能量集中在750cm^-1和1500cm^-1左右,最后进行了频率成分分析和去噪处理,并和小波分析方法进行了对比,验证了EMD方法的有效性和实用性,该方法在拉曼光谱信号分析中有较好的应用前景。     

基于算法优化的便携式X射线荧光光谱仪土壤重金属定量检测模型的建立

将小波变换去噪和迭代多项式拟合去除本底结合,对便携式X射线荧光光谱仪(PXRF)谱图进行去除噪声和本底扣除处理,然后根据土壤重金属含量标准值和处理后测定的计数建立各重金属的标准曲线。相比较未做去噪和本底扣除,经过sym4小波去噪和9次迭代多项式拟合处理后的仪器标准曲线决定系数R²范围提升至0.965 2~0.998 5,有效提高了仪器检测的正确度和精密度。采用算法优化(小波变换去噪+迭代多项式拟合)前后的便携式X射线荧光光谱仪分别对土壤样品中的铜、砷、铬、锌、铅和镍等6种重金属元素含量进行测定,分别从检出限、正确度和精密度等方面进行了对比分析。结果表明:在正确度方面,未处理前的相对误差范围为0.2%~4.8%,而仪器算法处理后测定样品的相对误差范围为0.4%~4.6%;在精密度方面,仪器算法处理后相对标准偏差范围为0.50%~5.2%,未处理前相对标准偏差范围为0.80%~15%;在检出限方面,仪器算法处理后分析6种重金属检出限范围低至3.5~22.0 μg/g。最后通过与实验室检测结果对比,数据显示部分元素均能接近实验室分析标准,实验结果无显著性差异,具有可比性,可用于大面积土壤环境修复和环境监测。     

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。     

基于小波分析的光谱数据处理

原子发射光谱作为多元素同步分析技术具有巨大的在线分析潜力。由于光谱数据量大,干扰信息与有效信息并存,不利于对光谱数据进行定性定量分析。小波分析具有分时分频精细表达和多尺度多分辨率分析的独特优势,本文介绍了小波变换去噪技术原理,通过对一组光谱数据的去噪处理,说明利用小波分析法可以有效的消减光谱中的干扰信息。     

板带轧机HAGC系统基于小波变换的传感器故障诊断

 提出了一种基于小波分析的HAGC系统传感器故障诊断方法。通过建立系统的MA模型，建立残差信号，利用离散小波变换对残差信号进行多分辨率分析。通过残差信号的故障高低频特性，提取故障敏感特征，实现了传感器故障时间、故障原因、故障程度的诊断。仿真研究表明，该方法凸现故障特征，时域定位准确，不但适用于突变故障，对渐变故障同样有效。     

小波遗传神经网络-阳极溶出伏安法同时测定钢中铜和铅

研究了二硫代二安替吡啉甲烷作修饰剂的修饰玻碳电极,并以此为工作电极,建立了测定铜和铅的阳极溶出伏安法。应用小波变换去噪、数据压缩和背景扣除功能对峰电流信号进行处理,用遗传算法自适应概率搜索神经网络的最优网络结构和参数,解决神经网络过拟合问题,将小波神经网络(WT-BP-ANN)、小波遗传神经网络(WT-GA-BP-ANN)的分析结果进行比较,Cu2+,Pb2+测定结果的RSD分别为3.49%,2.33%;3.77%,1.89%,表明小波遗传神经网络优于小波神经网络。     

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。     

迭代广义短时Fourier变换在行星齿轮箱故障诊断中的应用

短时Fourier变换(STFT)在分析非平稳信号的过程中,受调制系数的影响,时频分布图中的能量扩散至主导频率的周围,降低了时频分布的可读性.运用STFT分析瞬时频率缓变或恒定的信号时,调制系数的影响较小甚至可以忽略不计,而得到能量聚集程度很高的时频分布.根据这一特点,提出了迭代广义短时Fourier变换(IG-STFT),该方法有效改善了时频图的可读性.首先运用迭代广义解调分离出频率恒定的单分量成分,然后运用STFT分析信号的时频分布,最后依据STFT的分析结果和相位函数得到原信号的时频分布.通过行星齿轮箱仿真信号和实验信号分析,验证了该方法在分析非平稳信号中的有效性,准确诊断了齿轮故障.      

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.     

基于形态非抽样小波和S变换的轧机振动信号分析

为了确定板带连轧中多台轧机振动的起振顺序,首先在形态非抽样小波一般框架的基础上,构造了一种基于多尺度平均滤波器的形态非抽样小波.然后通过仿真实验,得到此形态非抽样小波尺度与频率之间的关系.最终利用基于多尺度平均滤波器的形态非抽样和S变换的方法对轧机振动信号进行分析,成功提取了故障频率的起振时间,确定了轧机的起振顺序.      

小波变换和神经网络的X射线衍射波谱滤噪技术比较

多晶X射线衍射扫描所形成的波谱会附带一些背景和噪声,而噪声是影响衍射谱峰分辨率的主要因素。本文通过多晶X射线衍射仪扫描某铁矿石矿物组成所形成的图谱,导入到MATLAB,分别采用神经网络、系统仿真和小波分析三种手段,对该图谱进行滤噪。最终对这三种滤噪方式的滤噪效果进行比较,得出小波分析的方法滤噪效果最佳。     

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.     

Roll Eccentricity Compensation Based on Anti-Alias-sing 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 im-proved from ±40 μm to ±15 μm.     

小波分析在激电数据处理中的应用

激电测量数据中一般包含有噪声和异常叠加效应,如何有效分离出有用异常信息,提高激电异常室内处理的效率和解释水平,成为激电异常解释的重要环节。在数据处理方法上,小波变换是20世纪80年代中期发展起来的一门新兴的数学理论和方法,在空间变换域和频率域同时具有良好的局部分析性质,可以将信号分解成各种不同频率或尺度成分,利用小波变换的上述特点,对激电异常进行划分,便可得到各种尺度意义下的异常。应用小波分析方法对河南省某矿区激电异常进行数据处理,区分出噪声和地表浅小地质体的干扰异常,深源或规模较大地质体的背景异常,突出了矿体的异常信息,取得了较好的效果。     

小波变换在铁矿热重矿物分析曲线处理的应用

小波变换是化学计量学中的一门新兴技术,目前在仪器分析的波谱解析中已有诸多应用。而热重分析是一种广泛应用于材料领域的分析方法,只要物质受热发生质量变化,都可采用热重分析。因此可应用于材料热稳定性、矿物分析等,但有时热重分析的曲线会因某种未知因素干扰而难以分辨。利用MATLAB的小波工具箱解析铁矿石热重曲线,采用Daubechies小波的5阶2层分解,对热重曲线进行噪声滤除,并采用小波导数变换分离重叠峰,最终达到提高热重分辨率的目的。     

Denoising of the fetal heart rate signal with non-linear filtering of the wavelet transform maxima

The fetal heart rate (FHR) signal provides valuable information for fetal development and well-being. However, the FHR traces derived from present-day ultrasound cardiotocographs are not of the desired quality. The paper applies the wavelet transform (WT) in order to denoise effectively the FHR signal. The denoising procedure analyses the evolution of the WT maxima across scales. The singularities of the signal create wavelet maxima with different properties from those of the induced noise. Since it is difficult to formulate precise rules that distinguish between the wavelet maxima of the FHR signal from those of the noise we have trained a neural network for this classification task. The neural network draws out successfully the noise induced wavelet maxima. An improved FHR signal can be obtained from the coarser wavelet approximation signal component and the filtered wavelet maxima by means of the inverse dyadic wavelet transform. Also, feature extraction and processing algorithms can be defined on the denoised wavelet coefficients (instead of on the original signal).     

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.      

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.