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。     

小波神经网络的数据压缩技术在超声自动探伤系统中的应用

为了实现对大型回转体零件内部缺陷的检测与识别,研制了超声波自动检测系统.系统主要完成超声信号的采集和处理、数据的实时存储、缺陷的在线分析与识别等功能.要实现缺陷的在线检测与识别,必然需要大量的原始数据,为了减少数据的存储量,通过小波神经网络提取相应的权重因子,构成小波基的尺度参数和与之对应的平移参数,实现缺陷有用信息的压缩;在缺陷数据重构中,利用上述特性参数并结合信号的特征值,对信号进行拟合.解决了缺陷检测现场大量数据的保存问题,为缺陷的进一步识别提供了基础.     

A Modified Nonparametric Prewhitened Covariance Estimator

Abstract. This paper proposes a fully modified version of the spectral matrix estimator (and the long‐run variance estimator as a special case) proposed originally by Xiao and Linton [Journal of Time Series Analysis (2002) Vol. 23, pp. 215–250], and derives its asymptotic results. A striking feature of the modified spectral matrix estimator is to achieve the convergence rate of O(T ^?8/9) in the mean squared error (MSE), which is usually achieved under the fourth‐order spectral window. However, this estimator does not sacrifice the positive definiteness of the resulting estimate for the rate improvement; it is Hermitian and positive definite in finite samples by construction. The faster convergence rate is established by a multiplicative bias correction of the crude spectral estimator under the second‐order spectral window. The approximations to some sensible definitions of the MSE of the estimator and the bandwidths that minimize the asymptotic MSEs are also derived. Monte Carlo results indicate that for a wide variety of processes the modified spectral matrix estimator reduces the bias without inflating the variance and thus improves the MSE, compared with the crude, bias‐uncorrected estimator.     

基于TW-SVM预测模型的某堆石坝变形预测分析

为提高监测资料有缺失的大坝变形预测模型精度,采用支持向量机方法建立一种具有小样本、高维、非线性的预测模型,并结合对其重要组成部分核函数的分析应用,提出一种根据结构风险最小化的TW-SVM预测模型。以某堆石坝为例进行研究,利用坝坡垂直位移和水平位移的监测数据,分别采用TW-SVM方法和BP神经网络(NET)方法建立相应预测模型进行比较分析。结果表明:采用TW-SVM方法和NET方法预测的垂直位移最大绝对误差分别为0.58 mm和6.18 mm,最大相对误差分别为270.00%和1 286.22%;采用TW-SVM方法和NET方法预测的水平位移最大绝对误差分别为0.25 mm和14.91 mm,最大相对误差分别为31.25%和1 189.85%;TW-SVM预测模型比NET预测模型更适合于影响因素为时间、水位的小样本预测分析。研究结果为堆石坝变形预测与分析提供参考。     

Comparison of Fourier,principal component and wavelet analyses for high speed flame measurements

The continuing advancement of high speed, combustion diagnostics calls for mathematical techniques that can extract key information from large datasets. This paper therefore describes a case study to compare the characterization of combustion dynamics behind a V-gutter flame holder using three mathematical methods: Fourier analysis, principal component analysis, (PCA), and wavelet analysis (WA). The comparison focuses on the analysis of the characteristic frequencies of flow–flame interactions, with a particular emphasis on the analysis of transient and unsteady combustion procedures, such as lean blow off. Experimental data obtained under a range of conditions were analyzed using all three methods, and several observations were made. When applied to the analysis of stable combustion processes, all three methods reported frequency characteristics that were similar both quantitatively and qualitatively. Under unstable and transient combustion conditions, the WA method is capable of revealing the dynamics of the frequency components in the measurements, while traditional Fourier and PCA methods encounter application restrictions. Lastly, these applications also demonstrated WA’s suitability for practical combustion measurements beyond chemiluminescence, such as its applicability to discrete signals, insensitivity to the choice of wavelet basis, and insensitivity to the target signal extracted from the raw measurements.     

Nonparametric kernel density estimation near the boundary

Standard fixed symmetric kernel-type density estimators are known to encounter problems for positive random variables with a large probability mass close to zero. It is shown that, in such settings, alternatives of asymmetric gamma kernel estimators are superior, but also differ in asymptotic and finite sample performance conditionally on the shape of the density near zero and the exact form of the chosen kernel. Therefore, a refined version of the gamma kernel with an additional tuning parameter adjusted according to the shape of the density close to the boundary is suggested. A data-driven method for the appropriate choice of the modified gamma kernel estimator is also provided. An extensive simulation study compares the performance of this refined estimator to those of standard gamma kernel estimates and standard boundary corrected and adjusted fixed kernels. It is found that the finite sample performance of the proposed new estimator is superior in all settings. Two empirical applications based on high-frequency stock trading volumes and realized volatility forecasts demonstrate the usefulness of the proposed methodology in practice.     

基于FDA-KDE间歇过程在线监控

针对基于传统的多向主元分析(Multiway Principal Component Analysis,MPCA)方法用于间歇过程在线监控时需要对新批次未反应完的数据进行预估,从而易导致误诊断,且统计量控制限的确定是以主元得分呈正态分布为假设前提的缺陷,结合Fisher判别分析(Fisher Discriminant Analysis,FDA)在数据分类及非参数统计方法核密度估计(Kernel Density Estimation,KDE)在计算概率密度函数方面的优势,提出了一种FDA-KDE的间歇过程监控方法。该方法首先利用FDA求取正常工况数据和故障数据的Fisher特征向量和判别向量,获得Fisher特征向量的相似度:然后在提出偏平均集成平方误差(Biased Mean Integrated Squared Error,BMISE)交叉验证法确定KDE的带宽从而获得相似度统计量控制限的基础上,利用已获得的数据测量值对过程进行监控,避免了基于MPCA方法对未来测量值的预估;最后采用基于Fisher判别向量权重的贡献图方法来进行故障诊断。通过对青霉素发酵间歇过程应用表明,所提出的方法比传统的MPCA方法能更及时地监测出过程异常情况,更准确地判断异常发生的原因。     

基于模糊梯度卷积核的在线棒材断面中心识别

针对传动带上严重堆叠、形状尺寸不规则的棒材,提出基于模糊梯度卷积核的在线棒材断面中心识别方法.以标准圆边缘为中心环,结合棒材断面边缘点偏离中心环的程度定义一个模糊梯度卷积核;通过模糊梯度卷积核与实时棒材断面梯度图像的卷积操作,实现棒材断面中心的增强;依次执行候选点选取、局部峰值抑制、距离检测步骤确定中心位置.实验结果表明,该方法能有效抑制棒材中心增强过程中易产生的峰值扩散现象,减少误识别和漏识别;平均计算时间为8.3 ms,满足系统实时性要求.     

选矿过程精矿品位自适应在线支持向量预测方法

本文提出了一种基于支持向量回归的选矿过程精矿品位自适应在线预测方法,通过使用新的混合核函数和参数在线更新机制提高了精矿品位的预测精度.在分析经典核函数特性后,构造了一种混合核函数以兼顾模型的学习能力与泛化能力,同时为了提高预测方法对选矿生产动态过程的适应性,模型依据新工况样本对现有样本集统计特性的影响,引入了模型参数自适应调整机制,并采用在线迭代学习机制更新模型,提高了模型的计算速度.使用某选矿厂生产实际数据进行实验分析,结果表明本文方法比现有方法在计算时间和预测精度上都有明显优势,适合应用于动态变化的选矿生产过程.