主成分分析的入侵检测方法研究

直接将入侵检测算法应用在粗糙数据上,其入侵检测分析的效率非常低.为解决该问题,提出了一种基于主成分分析的入侵检测方法.该方法通过提取网络连接中的相关信息,对它进行解码,并将解码的网络连接记录与已知的网络连接记录数据进行比较,发现记录中的变化和连接记录分布的主成分,最后将机器学习方法和主成分分析方法结合实现入侵检测.实验结果表明该方法应用到各种不同KDD99入侵检测数据集中可以有效减少学习时间、降低各种数据集的表示空间,提高入侵检测效率.     

基于滑动中值滤波的多尺度主元分析方法

提出了一种基于滑动中值滤波的多尺度主元分析(MSPCA)方法,该方法利用中值滤波对主元分析(PCA)前的原始数据进行预处理,以去除异常点,并用多尺度主元分析方法把小波变换和主元分析有机结合起来,通过对过程数据的多尺度建模,来消除系统中的次要主元和小的小波系数,这样既提高了对数据中细微、重要变化的检测灵敏度,又解决了在测量数据中含有异常点的情况下,现有多尺度主元分析难以去除因异常点的存在而产生的虚警问题.仿真验证了该方法的有效性和可行性.     

Robust principal component analysis using facial reduction

Optimization and Engineering - We introduce a novel approach for robust principal component analysis (RPCA) for a partially observed data matrix. The aim is to recover the data matrix as a sum of a...     

Multivariate process capability using principal component analysis

Wang and Chen (Qual. Eng. 1998; 11:21–27) have defined process capability indices (PCIs) for multivariate normal processes data using principal component analysis (PCA). Veevers (Statistical Process Monitoring and Optimization. Marcel Dekker: New York, NY, 1999; 241–256) has suggested a multivariate capability index based on the first principal component (PC). In this paper we demonstrate the problem in the definition of PCIs given by Wang and Chen (Qual. Eng. 1998; 11:21–27) and the non‐suitability of PCI given by Veevers (Statistical Process Monitoring and Optimization. Marcel Dekker: New York, NY, 1999; 241–256) through some examples. We also suggest an alternative method for assessing multivariate process capability based on the empirical probability distribution of PCs. This method has been performed on industrial and simulated data. Copyright © 2008 John Wiley & Sons, Ltd.     

Robust principal component analysis for functional data

A method for exploring the structure of populations of complex objects, such as images, is considered. The objects are summarized by feature vectors. The statistical backbone is Principal Component Analysis in the space of feature vectors. Visual insights come from representing the results in the original data space. In an ophthalmological example, endemic outliers motivate the development of a bounded influence approach to PCA.     

时间步长对爆破模拟结果的影响研究

为提高LS-DYNA软件模拟工程爆破时的精度,从理论上改进了确定极限时间步长Δte的方法,结合已有的数据对本方法的合理性进行了验证,并通过实例探讨了时间步长Δt对爆破模拟结果的影响。研究表明:Δte仅与炸药的爆速、炸药最小单元的最小边长以及计算时间步长比例因子有关,在网格划分时,炸药单元不能取过小或过于狭长;Δt对土体中爆炸空腔半径形成的影响不大,而对压力峰值的影响显著,且具有一定的规律性;随着比例距离Z的增加,压力峰值误差呈现由小到大,再由大到小的趋势。当计算区域范围在Z<0.5m·kg^(-1/3)时,Δt可取为1(-1/3)时,Δt可取为1³倍Δte;当Z≥0.5m·kg3倍Δte;当Z≥0.5m·kg^(-1/3)时,时间步长Δt可适当放宽,取为3(-1/3)时,时间步长Δt可适当放宽,取为3⁵倍Δte。对于一般的爆破模拟,考虑到经济性,Δt取为15倍Δte。对于一般的爆破模拟,考虑到经济性,Δt取为1³倍Δte即可满足大多数工程计算精度的要求。     

时间步长对爆破模拟结果的影响研究

为提高LS-DYNA软件模拟工程爆破时的精度,从理论上改进了确定极限时间步长Δte的方法,结合已有的数据对本方法的合理性进行了验证,并通过实例探讨了时间步长Δt对爆破模拟结果的影响。研究表明:Δte仅与炸药的爆速、炸药最小单元的最小边长以及计算时间步长比例因子有关,在网格划分时,炸药单元不能取过小或过于狭长;Δt对土体中爆炸空腔半径形成的影响不大,而对压力峰值的影响显著,且具有一定的规律性;随着比例距离Z的增加,压力峰值误差呈现由小到大,再由大到小的趋势。当计算区域范围在Z0.5m·kg~(-1/3)时,Δt可取为1~3倍Δte;当Z≥0.5m·kg~(-1/3)时,时间步长Δt可适当放宽,取为3~5倍Δte。对于一般的爆破模拟,考虑到经济性,Δt取为1~3倍Δte即可满足大多数工程计算精度的要求。     

基于主成分分析的护听器优选方法研究

为了探究不同护听器对抽水蓄能电站内不同工作场所的降噪效果及适用情况,以便于工作人员根据不同需要选择合适的护听器,根据112种护听器的插入损失测试结果,应用主成分分析(Principal Component Analysis, PCA)对数据进行分析。结合某抽水蓄能电站10个工作场所的现场测试结果,得出文中所测试的112种护听器大部分适用于该蓄水电站中1#发电机隔声罩内、1#水车室外、1#水车室内、2#尾水锥管室外、2#尾水锥管检修门、3#尾水锥管室外和3#尾水锥管检修门7个工作场所,其他场所需要有针对性地选择适合的护听器。该文同时可以为其他不同工作场所情况下护听器的选择提供借鉴。     

基于电容测量和PCA法的两相流相浓度检测方法

介绍利用电容层析成像系统阵列传感器结构和采样特点,引入主成分分析法(PCA)求取两相流相浓度的新方法.对大量测量值样本进行统计分析后,求出用测量值第一主成分求取相浓度的经验公式,仿真及静态实验表明:两者之间有着良好的对应关系,其测量结果不受两相流流型的影响,是一种有较好应用前景的测量方法.     

Identifying product shape relationships using principal component analysis

Product forms with multiple features, like automobiles, have traditionally accepted feature definitions and relationships between those features. These relationships drive how the product is created by focusing on expected, and accepted, feature development to push the form outside the traditional bounds. This paper uses principal component analysis to determine the fundamental characteristics within vehicle classes. The results of this analysis can then be considered by product designers to create new designs based upon the derived shape relationships. These new designs will be novel due to the non-traditional grouping of characteristics.     

Dimensionality reduction and visualization in principal component analysis

Many modern applications of analytical chemistry involve the collection of large megavariate data sets and subsequent processing with multivariate analysis techniques (MVA), two of the more common goals being data analysis (also known as data mining and exploratory data analysis) and classification. Classification attempts to determine variables that can distinguish known classes allowing unknown samples to be correctly assigned, whereas data analysis seeks to uncover and understand or confirm relationships between the samples and the variables. An important part of analysis is visualization which allows analysts to apply their expertise and knowledge and is often easier for the samples than the variables since there are frequently far more of the latter. Here we describe principal component variable grouping (PCVG), an unsupervised, intuitive method that assigns a large number of variables to a smaller number of groups that can be more readily visualized and understood. Knowledge of the source or nature of the variables in a group allows them all to be appropriately treated, for example, removed if they result from uninteresting effects or replaced by a single representative for further processing.     

Three-way principal component analysis applied to noodles sensory data analysis

The results presented in this paper are issued from the study and the interpretation of a 3-way data matrix constituted from the sensory analysis of wheat noodles. The aim of this work was to provide a complementary understanding of internal relationships between the chemical composition of the noodles and sensory attributes such as color, surface smoothness, elasticity or chewiness. The application of the Tucker3 algorithm involving the noodles composition, the sensory attributes and the assessors as the three modes, facilitates the interpretation of the differences among the types of noodles and also the estimation of the effect of different sources of variability on the sensory evaluation. A joint interpretation of the first and of the second mode (noodles and sensory attributes) allows to link appearance and texture attributes with the composition of the noodles.     

Connections between multiple co-inertia analysis and consensus principal component analysis

Consensus Principal Component Analysis is a multiblock method which is designed to reveal covariant patterns between and within several multivariate data sets. The computation of the parameters of this method namely, block scores, block loadings, global loadings and global scores are based on an iterative procedure. However, very few properties are known regarding the convergence of this iterative procedure. The paper discloses a monotony property of CPCA and exhibits an optimisation criterion for which CPCA algorithm provides a monotonic convergent solution. This makes it possible to highlight new properties of this method of analysis and pinpoint its connection to existing methods such as Generalized Canonical Correlation Analysis and Multiple Co-inertia Analysis.     

基于核主分量分析的经验模式分解及其工程应用

若信号的信噪比较小,经验模式分解不能正确分解出基本模式分量,分量中含有伪分量。根据此种情况,提出一种核主分量分析与经验模式分解相结合的方法。该方法首先建立信号相空间,利用核主分量分析方法提取相空间的核主分量,然后利用投影逆过程将得到的核主分量逆向投影回原相空间,从而重建信号相空间。最后对重建的相空间所对应的信号作经验模式分解。此方法可以有效消除噪声和冗余对经验模式分解的影响,提高经验模式分解的适应能力保证分解的有效性,确保其能够分解出正确的基本模式分量。通过工程实例进一步验证了该方法的可行性。     

Spectral analysis of the rotational component of seismic ground motion

An algorithm for the spectrum of the rotational component of surface ground motion during earthquake is derived. To obtain the rotation the total motion is decomposed into the wave components. Then the rotational motion is obtained in terms of the horizontal and vertical components treated as non-stationary random processes. The evolutionary spectrum of the rotational acceleration is a function of respective translational spectra, their co-spectrum and respective wave parameters. The analysis shows a shift of the higher frequency components in the rotational spectrum. The rotation is a function of the time derivative of translational components.     

Introduction and application of secured principal component regression for analysis of uncalibrated spectral features in optical spectroscopy and chemical sensing

In this study, a novel chemometric algorithm for improved evaluation of analytical data is presented and applied to three spectroscopic data sets obtained by different analytical methods. This so-called secured principal component regression (sPCR) was developed for detecting and correcting uncalibrated spectral features newly emerging in spectra after finalizing the PCR calibration, which may result in major concentration errors. Hence, detection and correction of uncalibrated features is essential. Furthermore, detected uncalibrated features provide qualitative information for sensing and process monitoring applications indicating problems in the process flow. After conventional PCR calibration, sPCR analyzes measurement data in two steps: The first step investigates whether the obtained data set is consistent with the calibration model or not. If spectroscopic features are found that cannot be modeled by the principal components, they are extracted from the measurement spectrum. This corrected spectrum is then evaluated by conventional PCR. In the Experimental Section, sPCR was successfully applied to three data sets obtained by different spectroscopic measurements in order to corroborate general applicability of the proposed concept. For each data set, one of several substances was excluded from the calibration acting in the sPCR assessment as uncalibrated absorber. The test sets consisted of disturbed and undisturbed samples. A total of 109 out of 110 test samples were correctly classified as disturbed or undisturbed by an uncalibrated absorber. It was confirmed that the extracted disturbance spectra are in accordance with the spectra of the uncalibrated analytes. The concentration results obtained with sPCR were found to be equivalent to conventional PCR results in the case of undisturbed samples and more precise for disturbed samples.     

Improving automotive dimensional quality by using principal component analysis

Dimensional quality is a measure of conformance of the actual geometry of products with the designed geometry. In the automotive body assembly process, maintaining good dimensional quality is very difficult and critical to the product. In this paper, a dimensional quality analysis and diagnostic tool is developed based on principal component analysis (PCA). In quality analysis, the quality loss due to dimensional variation can be partitioned into a mean deviation and piece-to-piece variation. By using PCA, the piece-to-piece variation can be further decomposed into a set of independent geometrical variation modes. The features of these major variation modes help in identifying the underlying causes of dimensional variation in order to reduce the variation. The variation mode chart developed in this paper provides the explicit and exact geometrical interpretation of variation modes, making PCA easily understood. A case study using an automotive body assembly dimensional quality analysis will illustrate the value and power of this methodology in solving actual engineering problems in a practical manner.     

Clustering and feature selection using sparse principal component analysis

In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief introduction and motivation on sparse PCA and detail our implementation of the algorithm in d’Aspremont et al. (SIAM Rev. 49(3):434–448, 2007). We then apply these results to some classic clustering and feature selection problems arising in biology.     

Nonnegative color spectrum analysis filters from principal component analysis characteristic spectra

Nonnegative color analysis filters are obtained by using an invertible linear transformation of characteristic spectra, which are orthogonal vectors from a principal component analysis (PCA) of a representative ensemble of color spectra. These filters maintain the optimal compression properties of the PCA scheme. Linearly constrained nonlinear programming is used to find a transformation that minimizes the noise sensitivity of the filter set. The method is illustrated by computing analysis and synthesis filters for an ensemble of measured Munsell color spectra.     

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.