类加权主成分分析在企业物流绩效评价中的应用

以企业物流绩效为研究对象,建立了一种较全面的二层次指标体系,综合考虑了财务、客户、市场、业务以及学习等各方面的绩效指标,并将上层指标看作是下层指标的指标类.利用层次分析法确定全体指标和每个指标类的权重系数,并对指标进行主成分分析,求得主成分;对标准化指标进行加权处理,同时考虑指标类的权重,建立了类加权主成分评价模型.通过一个示例对企业物流绩效进行评价,并将评价结果分别与主成分分析和加权主成分分析进行了比较.该模型将层次分析法的主观分析与主成分分析法的客观分析相结合,不仅考虑了全体指标的重要程度,而且考虑了物流绩效指标类的重要度差异,更加符合客观实际.     

Principal component analysis applied to filtered signals for maintenance management

This paper presents an approach for detecting and identifying faults in railway infrastructure components. The method is based on pattern recognition and data analysis algorithms. Principal component analysis (PCA) is employed to reduce the complexity of the data to two and three dimensions. PCA involves a mathematical procedure that transforms a number of variables, which may be correlated, into a smaller set of uncorrelated variables called ‘principal components’. In order to improve the results obtained, the signal was filtered. The filtering was carried out employing a state–space system model, estimated by maximum likelihood with the help of the well‐known recursive algorithms such as Kalman filter and fixed interval smoothing. The models explored in this paper to analyse system data lie within the so‐called unobserved components class of models. Copyright © 2009 John Wiley & Sons, Ltd.     

通信运营商定制移动终端物流服务商选择研究

以C公司为例,通过其定制移动终端业务的特殊物流需求,构建物流服务商的评价指标体系。并根据评价指标的特点,采用基于AHP、主成分分析法和DEA的“组合评价”方法作为通信运营商定制移动终端物流服务商的评价模型。通过实证分析发现,该方法有利于为通信运营商定制移动终端业务选出优秀、合适的物流服务商,从而达到双赢的预期。     

Source apportionment of PM2.5 in urban area of Hong Kong

A monitoring program for PM(2.5) had been performed at two urban monitoring stations in Hong Kong from November 2000 to February 2001 and June 2001 to August 2001. PM(2.5) samples were collected once every 6 days at PolyU and KT stations with the sampling duration of 24-h. A sum of 25 chemical species in PM(2.5) were determined and selected for receptor models. Enrichment factors relative to earth crust abundances were evaluated and it was noted that most crustal elements including Al, Ti, Mg, Ca and K have small enrichment factors. Correlation and multivariate analysis technique, such as principal components analysis (PCA)/absolute principal components analysis (APCA) and cluster analysis (CA) are used for source apportionment to identify the possible sources of PM(2.5) and to determine their contribution. Six factors at each site were isolated by using PCA/APCA and cluster analysis. Similar sources (crustal matter, automobile emission, diesel emission, secondary aerosols, tire wear, and non-ferrous smelter) are identified by the PCA/APCA and cluster analysis.     

基于1DDCNN和PCA信息融合的滚动轴承FLHI智能提取方法

滚动轴承故障预测方法的核心在于健康指数(HI)的构建,绝大部分已经提出的HI都是基于专家经验人工构造的,且往往只能适用于部件某一特定退化阶段的趋势分析。为解决上述问题,结合振动信号的一维特性,提出一种基于一维深度卷积神经网络(1DDCNN)结合主成分分析(PCA)的滚动轴承全寿命健康指数(FLHI)智能提取法;利用1DDCNN对原始时域信号自适应提取特征,深度挖掘能够表征研究对象健康状态的退化特征矩阵,而后利用PCA法对提取的特征矩阵进行融合,从而实现研究对象的FLHI智能提取。滚动轴承试验振动信号实测结果表明,相较于传统健康指数,FLHI在趋势性、鲁棒性和单调性方面更具有优势。     

Random forest and rotation forest ensemble methods for classification of epileptic EEG signals based on improved 1D‐LBP feature extraction

In this study, an efficient method for extracting and selecting features of unrefined Electroencephalogram (EEG) signals according to the one‐dimensional local binary pattern (1D‐LBP) is presented. Considering that taking a correct decision on various issues particularly in the field of diagnosing diseases, such as epilepsy, is of paramount importance, a functional approach is designed to extract the optimal features of EEG signals. The proposed method is comprised of two main steps: First, extraction and selection of features is performed based on a novel improved 1D‐LBP model followed by data normalization through principal component analysis (PCA); as combining 1D‐LBP neighboring models and PCA (1D‐LBP^c2p) method. The second step includes classification using two of the best ensemble classification algorithms, that is, random forest and rotation forest. A comparative evaluation is performed between the proposed methods and 13 distinct reported approaches including uniform and non‐uniform 1D‐LBP. The results are demonstrating that the combining method presented in our approaches has superiority along with efficiency by providing higher accuracy compared to the other models and classifiers. The proposed method in this paper can be considered as a new method for feature extraction and selection of other kinds of EEG signals and data sets.     

随机海洋环境下基于互相关函数和主成分分析的平台结构损伤辨识

为了确保海洋平台安全作业,及时辨识损伤以及进行损伤定位,海洋平台结构健康监测技术已成为学者研究关注的重要问题。针对某在役导管架平台,对平台在不同随机波浪激励下的动力响应分别进行了健康状态和损伤状态的数值模拟。在损伤辨识过程中,对结构不同位置的动力响应进行互相关分析,提取损伤敏感特征;利用主成分分析(principal component analysis,PCA)方法从复杂的数据中提取主成分;定义损伤指标并进行损伤辨识。针对传统PCA方法对某些杆件的损伤辨识精度不高等问题,提出了一种新的主成分选取方式,并在此基础上对传统PCA方法进行了改进。结果表明,改进后的PCA方法有效提高了损伤辨识的精度,可以对随机波浪条件下的结构损伤进行准确辨识。     

基于多项眼动数据的拖拉机造型设计评选模型

目的提出了一种基于多项眼动数据的拖拉机造型设计的评选模型。方法使用Eye-link眼动仪采集了30名被试在对4款不同的拖拉机设计效果图的评价过程中的眼动数据,在眼动数据与主观评价值的多重共线性检验的基础上,分析BP神经网络,并建立了拖拉机造型设计的评估模型。结论模型均方误差MSE=0.040,平均相对波动AVR=0.188,预测值和主观值的配对样本t检验P值远大于0.05。使用目标用户在体验过程中的眼动数据来评估拖拉机的造型设计,为拖拉机的造型设计评估提供了新的思路和方法。     

Multivariate analysis of anionic, cationic and nonionic textile surfactant degradation with the H(2)O(2)/UV-C process by using the capabilities of response surface methodology

Anionic, cationic and nonionic surfactants being frequently employed in the textile preparation process were subjected to H(2)O(2)/UV-C treatment. As a consequence of the considerable number of parameters affecting the H(2)O(2)/UV-C process, an experimental design methodology was used to mathematically describe and optimize the single and combined influences of the critical process variables treatment time, initial H(2)O(2)concentration and chemical oxygen demand (COD) on parent pollutant (surfactant) as well as organic carbon (COD and total organic carbon (TOC)) removal efficiencies. Multivariate analysis was based on two different photochemical treatment targets; (i) full oxidation/complete treatment of the surfactants or, alternatively, (ii) partial oxidation/pretreatment of the surfactants to comply with the legislative discharge requirements. According to the established polynomial regression models, the process independent variables "treatment time" (exerting a positive effect) and "initial COD content" (exerting a negative effect) played more significant roles in surfactant photodegradation than the process variable "initial H(2)O(2) concentration" under the studied experimental conditions.     

基于激波信号的超音速弹丸识别技术探索

为了探索低空超音速目标的探测技术,提出了一种基于激波信号的超音速飞行弹丸的目标分类识别方法.通过5.56 mm,7.62 mm和12.7 mm三种枪弹实测分析,提取信号的时域特征.通过主成分分析方法,对原始信息进行了处理,用支持向量机方法进行学习训练,设计了两级SVM分类器,获得了很好的分类效果.研究表明,基于超音速飞行体产生的激波信息识别目标是可行的.     

Integration of PCA and DEA for identifying and improving the impact of Six Sigma implementation on job characteristics in an automotive industry

This study presents an integrated approach, based on data envelopment analysis (DEA) and principal component analysis (PCA) methods, to evaluate the influence of Six Sigma deployment on key job characteristics in an automotive industry. The job characteristics are defined as satisfaction, stress, and security. A standard questionnaire is designed and distributed among the employees at the company's production site, who were affected by the implementation of Six Sigma. DEA and PCA methods are applied to measure the performance of the sub-groups of employees in the company. Consequently, the most efficient and inefficient sub-groups are determined. According to the findings of this investigation, it was perceived that the implementation of Six Sigma has had the greatest impact on job satisfaction. Additionally, a design of experiment was carried out to recognize the most effective job factor, which was identified to be the overall working conditions for the related case study. This is the first study that integrates DEA and PCA toward identifying and optimizing job characteristics in terms of Six Sigma implementation. The approach, employed in this study, can be easily used in the other manufacturing systems, in order to assist them to identify and improve their key job characteristics.     

基于混沌理论和支持向量机的人脸识别方法

针对如何选定主成分分析(PCA)特征维数和如何选定支持向量机(SVM)的参数来进一步提高人脸识别系统性能的问题,提出了一种基于混沌理论和支持向量机的人脸识别方法.首先,在统一的目标函数下,在采用PCA方法对人脸图像进行降维和将得到的特征送入SVM中进行训练期间,使用具有可操作性的改进混沌优化算法同时对PCA图像特征维数和分类器参数进行优化选择,然后用得到的优化人脸特征和最佳参数的分类器对未知图像进行识别.基于该方法,对ORL和Yale人脸库进行实验,其识别率都高达99%以上,仿真结果表明,该方法极大地提高了人脸识别能力.     

Advance warning for loss of separation in an industrial distillation column

This paper describes the development of a data‐driven advance warning system for the onset of loss of separation in an industrial distillation column. The system would enable preventive actions to avoid several hours of bad operation and subsequent recovery of the process. Data of more than 2 years of process operation were used to identify and validate various monitoring systems based on both static principal component analysis (PCA) and dynamic PCA. Despite the presence of autocorrelation in the data, only minor differences in advance warning were observed between PCA and dynamic PCA. The developed system provides warnings for 35% to 45% of the observed periods of bad column operation, with respective advance warning times of 16 and 6 minutes. It proves a valuable additional tool to monitor the operation of the distillation column and avoid losses of product, with the potential of reducing bad operation (and the associated costs) by up to 45% and substantially improving overall process reliability.     

基于PCA与决策树的转子故障诊断

将数据挖掘方法引入旋转机械故障诊断领域，提出一种基于主元分析（PCA）与决策树相结合的转子故障诊断方法。该方法首先利用PCA进行特征约简，降低特征空间的维数，然后采用C4．5决策树进行训练学习以及诊断决策。通过对转子类常见故障的诊断分析，证明该方法具有比BP神经网络训练时间更短、诊断准确率稍高的特点。     

基于主成分分析的函数型产品质量特性的优化方法研究

针对质量特性为轮廓（Profile）的输出响应的优化问题展开研究,提出一种基于主成分分析的双响应曲面法和满意度函数相结合的函数响应优化方法。将Profile的每个观测点看成一个独立响应,将Profile问题转化为多响应问题。求得多个观测点的均值和方差的满意度函数值,通过主成分分析法,将多个观测点的均值和方差的满意度函数值转化为主成分综合得分,并将这两者的加权和作为最终的优化指标。本文所提方法可以有效解决观测点之间存在的相关性的问题,并且优化过程同时考虑到每个观测点响应的均值和方差影响。实例证明,该方法简单易行,优化结果满意。     

A formulation development strategy for multivariate kinetic responses

The purpose of this paper was to evaluate a multivariate strategy for handling time-dependent kinetic data during formulation development. Dissolution profiles were evaluated by the Weibull equation, multiple linear regression (MLR), principal component analysis (PCA), alone and in combination. In addition a soft independent modeling of class analogy (SIMCA) was performed. Employing a typical kinetic model for solid formulations (here Weibull) showed difficulties with the model adaptation, resulting in increased model standard deviation and thereby failure in identifying significant variables. In general, the selection of a kinetic model is crucial for finding the significant formulation variables. Describing the dissolution profile based on MLR models of individual time points described the dissolution rates as a function of formulation variables with good precision. Establishing prediction models made it easy to evaluate effects on the entire dissolution profile. The use of PCA/MLR (PCR) reduced the influence of noise from single measurements in a kinetic profile, since they develop statistical parameters representing the profile without being dependent on a physicochemically-modeled profile. The use of PCA reduced the eight time-point variables to two latent variables (principal components), simplifying the classification of formulations and new samples as well as avoiding unwanted effects of model non-linearities between the factors and responses (model error). The group membership of new samples was demonstrated by SIMCA.     

Condition‐based Maintenance Optimization Using Neural Network‐based Health Condition Prediction

Artificial neural network (ANN)‐based methods have been extensively investigated for equipment health condition prediction. However, effective condition‐based maintenance (CBM) optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (i) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (ii) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available, which is critical for performing CBM optimization. In this paper, we propose a CBM optimization approach based on ANN remaining life prediction information, in which the above‐mentioned key challenges are addressed. The CBM policy is defined by a failure probability threshold value. The remaining life prediction uncertainty is estimated based on ANN lifetime prediction errors on the test set during the ANN training and testing processes. A numerical method is developed to evaluate the cost of the proposed CBM policy more accurately and efficiently. Optimization can be performed to find the optimal failure probability threshold value corresponding to the lowest maintenance cost. The effectiveness of the proposed CBM approach is demonstrated using two simulated degradation data sets and a real‐world condition monitoring data set collected from pump bearings. The proposed approach is also compared with benchmark maintenance policies and is found to outperform the benchmark policies. The proposed CBM approach can also be adapted to utilize information obtained using other prognostics methods. Copyright © 2012 John Wiley & Sons, Ltd.     

Comprehensive control charting applied to chromatography

Multivariate statistical process control (MSPC) based for example on principal component analysis (PCA) can make use of the information contained in multiple measured signals simultaneously. This can be much more powerful in detecting variations due to special causes than conventional single variable statistical process control (SPC). Furthermore, the PCA based SPC simplifies monitoring as it limits the number of control charts to typically two charts rather than one for each signal. However, the derived MSPC statistics may suffer from lack of sensitivity if only one or a few variables deviate in a given situation. In this paper we develop a new comprehensive control (COCO) chart procedure that considers both univariate statistics and multivariate statistics derived from PCA in a single plot that allows easy visualization of the combined data from a univariate and multivariate point of view. The method is exemplified using twenty analytical chromatographic peak areas obtained for purity analysis of a biopharmaceutical drug substance. The new control chart procedure detected two different types of faulty events in this study.     

Forecasting systems reliability based on support vector regression with genetic algorithms

This study applies a novel neural-network technique, support vector regression (SVR), to forecast reliability in engine systems. The aim of this study is to examine the feasibility of SVR in systems reliability prediction by comparing it with the existing neural-network approaches and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. A real reliability data for 40 suits of turbochargers were employed as the data set. The experimental results demonstrate that SVR outperforms the existing neural-network approaches and the traditional ARIMA models based on the normalized root mean square error and mean absolute percentage error.