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.     

PCA-DEA复合模型的分销商评价与改善

构建了一套合理的分销商评价指标体系,并利用主成分分析和数据包络分析这两种评价方法的原理,提出了二者结合的PCA-DEA复合评价模型.实例结果显示,PCA-DEA复合模型不但能对分销商进行合理有效的评价,而且可以找到提高分销商绩效的敏感因素,从而帮助企业制定出更好的分销策略.     

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。     

Multivariate Analyses of a Production Formulation Optimization Experiment

Three prominent multivariate statistical analyses, canonical correlation analysis (CCA), principal component analysis (PCA) and CAS-Regression analysis (CAS-R) are appropriately applied to the formulation optimization data associated with Product-T for determining a set of key excipient/process variables and a set of key response variables to be used in monitoring the future performance of the optimizated formula. CCA which considers both sets of variables simultaneously in a single analysis, successfully delineated two key parameters, one for each set. PCA which considers only the response variables concurred with the CCA results and CAS-R which considers each response variable separately also concurred. Even though CCA is a predominant technique, adjunct results of PCA and CAS-R could be supplemented for a comprehensive interpretation. It is recommended that all three analyses be carried out and interpreted appropriately.     

Road safety risk evaluation and target setting using data envelopment analysis and its extensions

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand one's own safety situation and to learn from those best-performing countries by indicating practical targets and formulating action programmes. In this respect, crash data such as the number of road fatalities and casualties are mostly investigated. However, the absolute numbers are not directly comparable between countries. Therefore, the concept of risk, which is defined as the ratio of road safety outcomes and some measure of exposure (e.g., the population size, the number of registered vehicles, or distance travelled), is often used in the context of benchmarking. Nevertheless, these risk indicators are not consistent in most cases. In other words, countries may have different evaluation results or ranking positions using different exposure information. In this study, data envelopment analysis (DEA) as a performance measurement technique is investigated to provide an overall perspective on a country's road safety situation, and further assess whether the road safety outcomes registered in a country correspond to the numbers that can be expected based on the level of exposure. In doing so, three model extensions are considered, which are the DEA based road safety model (DEA-RS), the cross-efficiency method, and the categorical DEA model. Using the measures of exposure to risk as the model's input and the number of road fatalities as output, an overall road safety efficiency score is computed for the 27 European Union (EU) countries based on the DEA-RS model, and the ranking of countries in accordance with their cross-efficiency scores is evaluated. Furthermore, after applying clustering analysis to group countries with inherent similarity in their practices, the categorical DEA-RS model is adopted to identify best-performing and underperforming countries in each cluster, as well as the reference sets or benchmarks for those underperforming ones. More importantly, the extent to which each reference set could be learned from is specified, and practical yet challenging targets are given for each underperforming country, which enables policymakers to recognize the gap with those best-performing countries and further develop their own road safety policy.     

广东高校科技资源投入产出效率研究

应用数据包络分析法（DEA）,根据广东省2000年～2009年的科技年鉴数据对广东省高等教育的科技资源投入和产出进行了评价和分析。结果表明：1999～2007年广东高校科技资源投入产出效率整体状况良好;但也存在DEA有效与非有效年份;不同产出指标的效率水平存在差异且变化趋势不同;各年度纯技术效率普遍高于其规模效率;在非有效年份增加科技资源投入有利于提高高等院校的科技创新效率。     

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DEA模型的广东省级工程中心技术效率研究

以广东21个城市为决策单元,选用数据包络分析（DEA）中的C2R和BC2模型,评价分析2008年广东省级工程中心技术效率情况。研究表明：全省省级工程中心技术效率总体水平偏低,但珠三角地区相对比较高;纯技术非有效问题比较严重,规模非有效问题非常突出;纯技术效率对技术效率有显著影响;16个无效城市的省级工程中心资源投入冗余比较大,产出不足问题比较突出,尤其是专利授权数。提出了制定省级工程中心发展规划、转变研发重点、实施标杆管理等提升省级工程中心技术效率的政策建议。     

Multivariate Analyses of a Production Formulation Optimization Experiment

Abstract

Three prominent multivariate statistical analyses, canonical correlation analysis (CCA), principal component analysis (PCA) and CAS-Regression analysis (CAS-R) are appropriately applied to the formulation optimization data associated with Product-T for determining a set of key excipient/process variables and a set of key response variables to be used in monitoring the future performance of the optimizated formula. CCA which considers both sets of variables simultaneously in a single analysis, successfully delineated two key parameters, one for each set. PCA which considers only the response variables concurred with the CCA results and CAS-R which considers each response variable separately also concurred. Even though CCA is a predominant technique, adjunct results of PCA and CAS-R could be supplemented for a comprehensive interpretation. It is recommended that all three analyses be carried out and interpreted appropriately.     

Analyzing supplier quality management practices in the construction industry

Supplier quality management (SQM) is an important function in the construction industry. Many construction organizations place high importance on using quantitative analyses to select effective SQM practices that ensure that materials, assemblies, and fabricated equipment for the construction project are within quality specifications. However, traditional quantitative analysis methods may be limited because the process of acquiring enough data to conduct the analyses is time consuming and costly. This article discusses the use of principal component analysis (PCA) to analyze a number of SQM practices from construction organizations known for their effective SQM. PCA is useful in this study because the data available for analysis are small in size and multivariate. SQM practices are discussed extensively and validated with subject matter experts (SMEs) using the analytical hierarchy process (AHP). We show that supplier's work observation, supplier performance rating, inspection effort tracking, and inspection and testing plans are important practices for SQM. We propose a quantitative methodology that can be used by quality engineers to analyze small sample size data. The research also describes how AHP, an analysis method based on expert judgment, can be used to validate and support the conclusions drawn from small sample size analyses. Identification of important SQM practices can benefit construction professionals with limited resources.     

Comparative study of face recognition techniques that use joint transform correlation and principal component analysis

Face recognition based on principal component analysis (PCA) that uses eigenfaces is popular in face recognition markets. We present a comparison between various optoelectronic face recognition techniques and a PCA-based technique for face recognition. Computer simulations are used to study the effectiveness of the PCA-based technique, especially for facial images with a high level of distortion. Results are then compared with various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical-correlator-based neural networks, and pose-estimation-based correlation.     

Comparative analyses of anthropometry associated with overweight and obesity: PCA and ICA approaches

This study undertakes to explore the co-varying structure in anthropometric variables that might be affected by the recent surge of overweight and obesity. The increase of overweight and obesity makes the distribution of body dimensions asymmetric by the long tail in distribution (skewness, kurtosis). Principal component analysis (PCA) has been well applied to understand the co-varying body dimensions. However, because PCA decomposes covariance/correlation matrix, the effects of overweight and obesity may not be well captured. Independent component analysis (ICA) is a variant of PCA with the additional assumptions of components being non-Gaussian and independent, in which kurtosis is decomposed. PCA and ICA are applied on the anthropometric data from the North American portion of the Civilian American and European Surface Anthropometry Resource (CAESAR) project. ICA yields more interpretable results by visual inspection than corresponding PCA results. The first independent component (IC 1) is associated with hip/thigh circumferences and chest/waist circumferences and has the largest correlation coefficients with body mass index (BMI). Only the second IC shows the overall size factor that reveals gender difference while principal components 1, 2 and 3 show gender difference. The ICs 3 (torso length) and 4 (arm and leg lengths) are associated with individual differences in body dimensions. The ranges of 38 body dimensions are identified in order to satisfactorily meet the anthropometric variations for both males and females. The ICA gives promise of becoming a valuable tool in the field of ergonomics.     

海上风电导管架陆地建造过程中的应力分析

海上风电基础承载装备的结构越来越复杂且越来越重,建造工艺与建造路径等不同会导致其残余应力不同,从而影响其安全性和可靠性.为此,基于有限元分析方法,分别采用梁单元与壳单元建立了不同的海上风电导管架计算模型,并运用单元生死技术模拟海上风电导管架的立式与卧式陆地建造过程.由模拟结果可知:使用壳单元能够更加准确地模拟海上风电导...     

基于主成分分析马氏距离的支腿控制阀健康评估

支腿控制阀的性能是影响起重机支腿系统伸缩性能的重要因素之一。为了准确的评估支腿控制阀的健康性能,提出一种支腿系统的性能衰退与健康状态的评估方法,该方法基于PCA降维与马氏距离相结合的分析模型,建立不同状态下传感器信号与关键零部件的映射关系,从而达到对起重机支腿系统性能衰退量化评估的目的。该方法应用于起重机支腿控制阀的压力信号,通过对传感器信号内蕴关系及起重机在各年份提取的特征在特征空间的相关性分析,以得到量化性能评估结果。与常见的其他方法相比,该模型能够准确地反映起重机支腿系统历年来的性能衰退趋势,具有更好的鲁棒性与泛化性。     

Uncorrelated linear discriminant analysis (ULDA): A powerful tool for exploration of metabolomics data

The theory together with an algorithm for uncorrelated linear discriminant analysis (ULDA) is introduced and applied to explore metabolomics data. ULDA is a supervised method for feature extraction (FE), discriminant analysis (DA) and biomarker screening based on the Fisher criterion function. While principal component analysis (PCA) searches for directions of maximum variance in the data, ULDA seeks linearly combined variables called uncorrelated discriminant vectors (UDVs). The UDVs maximize the separation among different classes in terms of the Fisher criterion. The performance of ULDA is evaluated and compared with PCA, partial least squares discriminant analysis (PLS-DA) and target projection discriminant analysis (TP-DA) for two datasets, one simulated and one real from a metabolomic study. ULDA showed better discriminatory ability than PCA, PLS-DA and TP-DA. The shortcomings of PCA, PLS-DA and TP-DA are attributed to interference from linear correlations in data. PLS-DA and TP-DA performed successfully for the simulated data, but PLS-DA was slightly inferior to ULDA for the real data. ULDA successfully extracted optimal features for discriminant analysis and revealed potential biomarkers. Furthermore, by means of cross-validation, the classification model obtained by ULDA showed better predictive ability than PCA, PLS-DA and TP-DA. In conclusion, ULDA is a powerful tool for revealing discriminatory information in metabolomics data.     

Comprehensive analysis of LPG‐PCA algorithms in denoising and deblurring of medical images

This article presents the detailed analysis of the local pixel grouping–principle component analysis (LPG‐PCA) algorithm in denoising and deblurring of medical images. Inefficient diagnosis of the medical images containing lot of information is often affected by the noise and artifacts. In order to remove these noises and artifacts, a statistical decorrelation technique, LPG‐PCA is used which is found to be one of the efficient methods, which could be used in improving the performance of medical images. For better preservation of local structures of the image, a pixel and its nearest neighbors are modeled as a vector variable, which leads to the selection of similar intensity characteristics. Denoising method used in this article is done in two stages for improving the denoising performance. The smoothening caused by the denoising process is removed by using LPG‐PCA along with adaptive sparse domain representations in the deblurring process. This involves clustering of data and finding the subdictionary of each cluster using LPG‐PCA. Experimental results show that an average improvement of 2.9 and 5.1 dB is found in the computed tomography and magnetic resonance imaging images using denoising and deblurring process. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 157–170, 2013     

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。     

基于约束独立成分分析的滚动轴承故障诊断

用新的理论和方法丰富和提高机械故障诊断技术是机械故障诊断学的重要内容之一。本文用约束独立成分分析方法把研究对象的先验信息作为独立成分分析算法的约束条件,使新的算法仅仅收敛于感兴趣的故障信号。这样可以提高故障诊断的针对性和效果。该方法可以从传感器信号中快速诊断设备中是否发生了某种故障,并以滚动轴承的仿真和实验验证了该方法的适用性和有效性。     

基于PCA的多变量控制系统的故障监测与诊断

 主元分析(PCA)是一种能够对过程生产进行监测和质量控制的有效方法,在保证数据信息丢失最少的情况下,大大降低了原始数据空间的维数．利用PCA分析建模可以消除变量间的非线性关联,降低噪声影响．通过对某食品厂蒸煮设备控制流程进行大量试验表明,PCA 故障诊断模型能够有效地对设备生产进行监测,并能较准确及时地诊断设备运行中发生的故障．     

基于DEA视窗分析的渤海湾地区港口内部运营效率

既往对港口效率的研究大多仅考虑空间维度,忽略时间的作用。为了弥补这一不足,运用DEA视窗分析模型,从空间和时间两个维度出发,利用面板数据,对渤海湾地区三大主要港口的内部运营效率进行研究,并利用在DEA相对有效面上的投影分析,明确港口偏离有效的原因。此外,运用灰色关联模型对影响港口效率的因素进行了测算。研究发现,青岛港内部运营效率较高,其次为天津港,而大连港则一直处于非DEA有效状态;投入拥挤、产出不足以及资源配置不合理是造成非DEA有效的主要成因;各投入要素对三大港口内部运营效率的影响程度存在较大差异。