Prediction of Residual Stresses Using Partial Least Squares Regression on Barkhausen Noise Signals

Residual stresses are quantitatively predicted based on the Barkhausen noise measurement with a partial least squares regression model. The measurements are taken from two sets of case-hardened samples. The benefits of using certain feature elimination strategies prior model identification are also studied. The elimination methods applied are correlation-based elimination, uninformative variable elimination and successive projections algorithm. The results show that the best predictions are usually obtained when the successive projections algorithm is applied. The prediction accuracy of the best models found shows that partial least squares models can be successfully used for prediction of material properties based on the Barkhausen noise measurement.     

购后行为意向的偏最小二乘建模与分析

简述了偏最小二乘用于多元回归建模的方法。对顾客购后行为意向及其成因指标的调查数据采用偏最小二乘法，根据变量投影重要性指标和因子载荷分析，从lO个成因指标中筛选出8个组成自变量指标集合，然后以重复购买、交叉购买、正面推荐等三种顾客购后行为意向指标作为因变量集合，建立了多元回归模型，取得了比较满意的拟合与预测效果。主要结论如下：对于电视机产品，(1)期望和愿望自身对于顾客购后行为意向没有显著影响；(2)期望和愿望的满足程度影响顾客的购后行为意向；(3)企业形象和发布信息的真实性影响顾客的购后行为意向；(4)质量因素比价格因素的影响强烈；(5)在三种顾客购后行为中，顾客满意度对于正面推荐的影响系数最大。     

Prediction intervals in Partial Least Squares regression via a new local linearization approach

Univariate Partial Least Squares is a biased regression procedure for calibration and prediction widely used in chemometrics. To the best of our knowledge the distributional properties of the PLS regression estimator still remain unpublished and this leads to difficulties in performing inference-based procedures such as obtaining prediction intervals for new samples. Prediction intervals require the variance of the regression estimator to be known in order to evaluate the variance of the prediction. Because the nonlinearity of the regression estimator on the response variable, local linear approximation through the δ-method for the PLS regression vector have been proposed in the literature. In this paper we present a different local linearization which is carried out around the vector of the covariances between response and predictors, and covariances between predictors. This approach improves the previous ones in terms of bias and precision. Moreover, the proposed algorithm speeds up the calculations of the Jacobian matrix and performs better than recent efficient implementations.     

非线性偏最小二乘法在混合炸药体系液相色谱中的应用

本文着重讨论了非线性偏最小二乘法(简称NLPLS)在混合炸药体系定量分析领域里的应用。以HMT、RDX、Tetryl、TNT等常用炸药为对象,对炸药的高效液相色谱分析中所遇到的复杂体系如谱峰严重重叠体系、待分析物含量相对悬殊体系等进行了研究。结果表明,本方法对混合炸药体系的液相色谱分析数据具有较强的处理能力,对完全重叠色谱的解析置信度取95％,误差小于5％。为解决这类炸药的液相色谱定量分析问题增添了另一条途径。     

Estimating Military Aircraft Cost Using Least Squares Support Vector Machines

A multi-layer adaptive optimizing parameters algorithm is developed for improving least squares support vector machines (LS-SVM), and a military aircraft life-cycle-cost (LCC) intelligent estimation model is proposed based on the improved LS-SVM. The intelligent cost estimation process is divided into three steps in the model. In the first step, a cost-drive-factor needs to be selected, which is significant for cost estimation. In the second step, military aircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM. Then the model can be used for new type aircraft cost estimation. Chinese military aircraft costs are estimated in the paper. The results show that the estimuted costs by the new model are closer to the true costs than that of the traditionally used methods.     

涡扇发动机风扇静态噪声预测

发动机风扇是涡扇发动机最主要的噪声源之一,其产生的机理是气体在高速旋转的叶片之间流动,引起强烈的宽频噪声和单音噪声。利用Matlab软件编程实现Heidmann大风扇修正噪声模型,对某型大涵道比涡扇发动机风扇静态噪声进行预测。建立该发动机风扇噪声数据库,通过基于1/3倍频程频谱的声压级和感觉噪声级来分析噪声预测结果。     

Computerized Derivative Spectrophotometric Assay of two Component Mixture Using Least Squares Method

Abstract

Two computerized spectrophotometry methods; the derivatve (D-) method and the derivative under least squares approach (D-LS) method; have been described for the assay of two component mixture. That mixture has been solved for chlorpheniramine maleate (CP) and phenylephrine hydrochloride (PE) existing in a ratio range of 1/0.8 to 1/2. Being more versatile and fast, the proposed methods supersede the Vierordt's method in dealing with the assay of two component mixture since the latter is subjective to many limitations.     

Computerized Derivative Spectrophotometric Assay of two Component Mixture Using Least Squares Method

Two computerized spectrophotometry methods; the derivatve (D-) method and the derivative under least squares approach (D-LS) method; have been described for the assay of two component mixture. That mixture has been solved for chlorpheniramine maleate (CP) and phenylephrine hydrochloride (PE) existing in a ratio range of 1/0.8 to 1/2. Being more versatile and fast, the proposed methods supersede the Vierordt's method in dealing with the assay of two component mixture since the latter is subjective to many limitations.     

用加权最小二乘法评价公共汽车预防维修效果

本文采用加权最小二乘法拟合可修系统失效点过程数据到幂律模型以评价预防维修的质量。权函数与正态密度函数成正比,它赋予靠近维修时间的失效数据大的权值,而赋予远离维修时间的失效数据小的权值。分析了一个实际案例,对比了加权与不加权的最小二乘法所得到的结果。结果表明,对于维修质量评价,加权的最小二乘法优于普通的最小二乘法。     

Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear Regression

I extend the concept of partial least squares (PLS) into the framework of generalized linear models. A spectroscopy example in a logistic regression framework illustrates the developments. These models form a sequence of rank 1 approximations useful for predicting the response variable when the explanatory information is severely ill-conditioned. Iteratively reweighted PLS algorithms are presented with various theoretical properties. Connections to principal-component and maximum likelihood estimation are made, as well as suggestions for rules to choose the proper rank of the final model.     

基于KPLS的滚动轴承装配质量预测

轴承的装配质量在机械运行过程中起到十分关键的作用。为了预测轴承的装配质量,建立了能够准确表征轴承特性与零件几何要素间关系的数学模型,对轴承产品装配质量进行预测。结果表明采用核偏最小二乘回归法(kernel partial least squares,KPLS)对轴承的装配质量进行预测,克服了实际生产中非线性因素对预测模型的不利影响,具有很好的预测精度,为轴承的精准制造提供了帮助,具有十分重要的意义。     

基于改进偏最小二乘的CMM动态误差混合建模分析

三坐标测量机(CMM)动态误差源错综复杂,并且相互影响,因此很难建立一个通过误差源分析误差的准确预测模型.本文以空间测量位置的三维坐标值和测量机测量时的计算机直接控制(DCC)参数,包括移动速度、逼近距离和触测速度作为CMM动态测量误差模型的原始自变量,并通过3B样条变换获得各原始自变量与动态测量误差的非线性关系函数,再利用正交投影法把解释矩阵中与因变量无关的成分扣除掉,得到新的解释矩阵后再用偏最小二乘(PLS)回归进行降维和参数估计,从而得到CMM动态测量误差模型,即基于3B样条-正交投影偏最小二乘(3BS-OPPLS)模型.这样既避免了分析错综复杂的误差源及其相互影响,又能够捕捉各自变量对动态测量误差的非线性影响,并能克服因解释变量过多而产生的多重共线性问题.实验结果表明建立的3BS-OPPLS模型的预测效果优于未经正交投影的3B样条-偏最小二乘(3BS-PLS)模型,模型的预测精度得到显著提高.     

最小二乘法在热电偶热电势校准中的应用

在热电偶的实际应用过程中，分度点往往需要根据校准点用高阶多项式进行拟合求得。本文首先确定最优的多项式拟合阶次，同时鉴于目前国际上对校准结果表示的规范化要求，每一个校准结果都有一个不确定度与之相对应，本文对最优的多项式拟合结果的不确定度进行了评定。     

A Coarse Alignment Based on the Sliding Fixed-Interval Least Squares Denoising Method

The observation vectors in traditional coarse alignment contain random noise caused by the errors of inertial instruments, which will slow down the convergence rate. To solve the above problem, a real-time noise reduction method, sliding fixed-interval least squares (SFI-LS), is devised to depress the noise in the observation vectors. In this paper, the least square method, improved by a sliding fixed-interval approach, is applied for the real-time noise reduction. In order to achieve a better-performed coarse alignment, the proposed method is utilized to de-noise the random noise in observation vectors. First, the principles of proposed SFI-LS algorithm and coarse alignment are devised. A simulation test and turntable experiment were executed to demonstrate the availability of the designed method. It is indicated that, from the results of the simulation and turntable tests, the designed algorithm can effectively reduce the random noise in observation vectors. Therefore, the proposed method can enhance the performance of coarse alignment availably.     

A Least Squares Fit Method for Precision Evaluation of Waveguide-Resonator Measurements

The resonance frequencies of waveguide resonators, e.g., for the measurement of dielectric constants, are evaluated using a least squares fit method based on the analytic eigenfrequency equation. This yields a reliable estimate of the number of order of the resonances even for long resonators and averages random measurement errors.     

非线性系统载荷识别的最小二乘支持向量机法

为解除载荷识别问题对原系统先验知识的依赖,本文提出采用最小二乘支持向量机（Least squares support vector machine,LS-SVM）对非线性系统进行逆模型辨识,随后在该逆模型基础上利用工作状态的响应数据识别时域载荷。通过对某一非线性系统的稳态和非稳态激励的仿真计算,验证了该方法的有效性。仿真结果表明LS-SVM能够辨识出可靠的非线性系统的逆模型,进而反演出较精确的时域载荷。该方法不需要了解系统的数学模型及参数,只需少量训练样本即可,因此该方法能够应用于工程实践中。     

Applying the methodology of Design of Experiments to stability studies: a Partial Least Squares approach for evaluation of drug stability

The aim of the present research is to show that the methodology of Design of Experiments can be applied to stability data evaluation, as they can be seen as multi-factor and multi-level experimental designs. Linear regression analysis is usually an approach for analyzing stability data, but multivariate statistical methods could also be used to assess drug stability during the development phase. Data from a stability study for a pharmaceutical product with hydrochlorothiazide (HCTZ) as an unstable drug substance was used as a case example in this paper. The design space of the stability study was modeled using Umetrics MODDE 10.1 software. We showed that a Partial Least Squares model could be used for a multi-dimensional presentation of all data generated in a stability study and for determination of the relationship among factors that influence drug stability. It might also be used for stability predictions and potentially for the optimization of the extent of stability testing needed to determine shelf life and storage conditions, which would be time and cost-effective for the pharmaceutical industry.     

Transformation of Chord Length Distributions into Particle Size Distributions Using Least Squares Techniques

For the characterization of particulate systems, various measuring techniques exist. Many of these assume that the particles are spherical in order to compute a particle size distribution (PSD) from the measured data. However, in many applications the shape of the particles deviates from a sphere, and as a consequence the computed PSD will contain errors because of this violated assumption. Measuring techniques that do not require this a priori assumption are, for example, those that measure the chord lengths of the particles. A disadvantage of the latter techniques is that the interpretation of the chord length distribution (CLD) is less transparent than the interpretation of a shape-based PSD (the PSD given an assumed particle shape). To facilitate the interpretation of a CLD, an algorithm based on least squares optimization techniques is presented. This algorithm computes the shape-based PSD that best explains the measured CLD and can, for example, discriminate spheres from rods using information of the CLD only. Knowledge about the type of PSD (e.g., Gaussian or log-normal) is not required.