A comparison of methods for the quantification of assemblage zones

Several methods of quantitative biostratigraphy that are based on assemblage zones are examined utilizing three sets of faunal distribution data. Two of the data sets are structured simply and one is complex. Various types of cluster analysis and multidimensional scaling are applied to weighted and unweighted binary (presence/absence) data. For weighted data, the presences are multiplied by the relative biostratigraphic values (RBV) of the taxa. There are two options for calculating the RBV's. One method (RBV2) emphasizes timestratigraphic correlation, whereas the other is a compromise between time-stratigraphic correlation and biofacies correlation (RBV1). Results from lateral tracing also are examined.The case studies allow the formulation of a general strategy. Weighting is not appropriate if paleoecological groupings are sought. If biostratigraphic zonation is required, weighted data may produce clusters that are stratigraphically more homogeneous than those based on unweighted data. Also the RBV's can point out species that can be deleted from the analysis without losing significant biostratigraphic information. Range through data should be employed in most situations. Similarity matrices between samples can be calculated from various coefficients based on presences. Biostratigraphic zones are extracted from the similarity matrix by cluster analysis, multidimensional scaling and lateral tracing to produce an overall view of the data structure. Lines of correlation and fence diagrams can be constructed between the samples in adjacent stratigraphic sections using the same techniques.     

Wind turbine performance analysis based on multivariate higher order moments and Bayesian classifiers

A data-driven model based on Bayesian classifiers and multivariate analysis of the power curve (wind speed vs. power) for monitoring wind farms' performance is presented. A new outlier detection criterion and various control bounds on the skewness and kurtosis of the data for cluster separation and classification of turbines' faulty and normal state of operation are introduced. Further continuous monitoring is addressed with Hotelling's T² and Bayesian network approaches, and it is proven that under certain conditions, the outcomes of these two methods are equivalent. The Bayesian approach, however addresses the likelihood of classification, making supervised controls more flexible.     

基于储备池主成分分析的多元时间序列预测研究

提出一种基于回声状态网络储备池的非线性ＰＣＡ 方法,并将其应用于多元时间序列的预测中．由于多维输入变量间的相关性会影响建模效果,通过储备池将输入在原空间的非线性特征转化成高维空间的线性特征．在其中运用线性ＰＣＡ 技术寻找输入在储备池空间的最大方差方向,提取有效的多元变量综合信息．经储备池主成分分析处理后的输入与预测点呈动态线性映射,可使用线性方法建模．仿真结果表明了该方法的有效性．     

Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms

Multivariate Gaussian models are widely adopted in continuous estimation of distribution algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for multivariate Gaussian based EDAs (MGEDAs), named eigen decomposition EDA (ED-EDA). Unlike classical EDAs, ED-EDA focuses on eigen analysis of the covariance matrix, and it explicitly tunes the eigenvalues. All existing MGEDAs can be unified within our ED-EDA framework by applying three different eigenvalue tuning strategies. The effects of eigenvalue on influencing the evolution are investigated through combining maximum likelihood estimates of Gaussian model with each of the eigenvalue tuning strategies in ED-EDA. In our experiments, proper eigenvalue tunings show high efficiency in solving problems with small population sizes, which are difficult for classical MGEDA adopting maximum likelihood estimates alone. Previously developed covariance matrix repairing (CMR) methods focusing on repairing computational errors of covariance matrix can be seen as a special eigenvalue tuning strategy. By using the ED-EDA framework, the computational time of CMR methods can be reduced from cubic to linear. Two new efficient CMR methods are proposed. Through explicitly tuning eigenvalues, ED-EDA provides a new approach to develop more efficient Gaussian based EDAs.     

A language for the classification and shading of multivariate volume data

A novel, language-based interface to the specification of multivariate volume classification and shading algorithms has been implemented. The system facilitates experimentation by providing access to data relevant to volume classification and shading (scalars, gradients, and gradient magnitudes) in a C-like language environment. The user writes code to calculate opacity and colour on a per voxel basis. The code is interpreted and compiled in a transparent fashion and then executed on a volume data-set. The output is a volume primitive suitable for input to standard volume rendering algorithms.     

噪声对多变量分析方法影响的研究

多变量分析能充分利用现代分析仪器获取的多通道量测数据,解决多组分不经分离或掩蔽同时测定的问题。然而,噪声的存在往往影响多变量分析的准确度。本文用仿真数据研究了噪声在不同分离度下,不同半峰宽比时和不同信噪比下,对3种多变量分析方法——卡尔曼滤波法,多元线性回归法和主成分回归法的影响。实验表明,噪声影响多变量分析方法的准确度,且噪声对主成分回归法的分析结果准确度影响较大,而对卡尔曼滤波法和多元线性回归法影响较小。     

Stochastic approximation learning for mixtures of multivariate elliptical distributions

Most of the current approaches to mixture modeling consider mixture components from a few families of probability distributions, in particular from the Gaussian family. The reasons of these preferences can be traced to their training algorithms, typically versions of the Expectation-Maximization (EM) method. The re-estimation equations needed by this method become very complex as the mixture components depart from the simplest cases. Here we propose to use a stochastic approximation method for probabilistic mixture learning. Under this method it is straightforward to train mixtures composed by a wide range of mixture components from different families. Hence, it is a flexible alternative for mixture learning. Experimental results are presented to show the probability density and missing value estimation capabilities of our proposal.     

基于模糊聚类分析的采空区自燃“三带”性质研究

针对采空区内不同区域的煤自燃倾向性存在差异性的问题,采用基于模糊聚类分析的采空区自燃"三带"划分方法对不同测点数据进行了分类,分析了不同λ截距阵下各分类的差异性。分析结果表明:不同的λ值可以将数据划分为不同的类别;在一定范围内,随着λ值的增大,原有的类别可以被划分为更小的子类;当λ值增大时,采空区内距工作面较近区域的测点数据更容易被划分为更小的子类,而距工作面较远区域的测点数据分类较为稳定,则可判定距煤壁较远的采空区区域环境性质较为稳定,而距煤壁较近的采空区区域环境性质差异较大;运用F-统计量计算最优的λ,相应分类与采空区"三带"划分相吻合。     

基于数据流分析的SQL注入漏洞发现技术研究

提出了一种新的SQL注入漏洞发现方法。在前人污染值传播概念的基础上,通过构建Web应用程序的控制流图,对不同控制流图结点指派合适的转换函数,跟踪污染值在程序中的传播过程,从而发现并定位源代码中存在的SQL注入漏洞。方法对用不同脚本语言编写的Web应用程序中SQL注入漏洞的发现具有通用性和有效性。     

基于数据分析的化工过程监测

为了改善主元分析对带噪声过程的监测性能,本文结合小波包分析消噪性能与主元分析提取变量间相关性能的特点,提出了一种小波包主元分析方法。给出了基于小波包主元分析的过程监测的算法实现。并在此基础上,对TE过程进行了监测性能仿真。结果表明小波包主元分析方法有较好的监测性能。     

Statistical analysis and graphical display of multivariate data on the Macintosh

Two Macintosh programs written for multivariate data analysis and multivariate data graphical display are presented. MacMul includes principal component analysis (PCA), correspondence analysis (CA) and multiple correspondence analysis (MCA), with a complete, original and unified set of numerical aids to interpretation. GraphMu is designed for drawing collections of elementary graphics (curves, maps, graphical models) thus allowing comparisons between variables, individuals, and principal axes planes of multivariate methods. Both programs are self-documented applications and make full use of the user-oriented graphical interface of the Macintosh to simplify the process of analysing data sets. An example is described to show the results obtained on a small ecological data set.     

Mixture analysis of multivariate categorical data with covariates and missing entries

Longitudinal or otherwise correlated categorical variables are typically related to some covariates and exhibit nonignorable correlations of the observed variables. A further complication often consists in missing entries. For analyzing such data, it is proposed to create an extra missing category and to employ latent class analysis which, regarding missing data, can be shown to belong to the family of nonmissing at random models. By treating the complete and the incomplete cases jointly, it becomes possible to estimate the parameters of interest along with additional parameters characterizing the missing mechanism. Data from the Muscatine Coronary Risk Factor Study, where each child was classified obese or not obese at three occasions, serve as an illustrative example. Previous analyses resulted in significant interaction of age and sex for the complete data (N=460), and in a linear increase in the logit of the rate of obesity over time for the incomplete data, with no effect of the covariate sex (N=1014). Reanalyses employing latent class models do not support these findings. The finally accepted two-classes model for the complete data assumes a linear effect of age which is the same for boys and girls. The incomplete data were considered three-categorical (not obese, obese, missing) and resulted in a more complex model only in part supporting the linear age hypothesis.     

Style synthesis and analysis of car designs for style quantification based on product appearance similarities

Understanding how similar design appears is a key element to understanding companies’ design strategies. However, it is difficult to evaluate companies’ design strategies with conventional style measurement methods since they only taxonomically measure whether a specific characteristic is included in a specific style. This study numerically measured car design similarities to synthesize and analyze car brand styles, thereupon discovering the design trends among car brands for strategic design positioning. This paper aims to find methods for quantifying style differences and identifying unique design elements of car designs among 23 automobile manufacturers based on design similarities of a large quantity of car designs (N = 119). To achieve this goal, a hybrid style quantification methodology – a mixture of Fourier decomposition, eye tracker, and shape grammar – was created to evaluate similarities, visual significance, and combinations of 19 car design elements. Fourier decomposition was incorporated to find the quantifiable values of design similarities of car design elements. Visual significance analysis was also conducted for each car design element through eye tracker to measure the importance of certain design elements for weighting factors. Then, each combination of design elements was compared with car design elements of other cars for similarity calculations. Finally, car design alternatives were synthesized, and transitions of design positioning were analyzed based on the similarity values weighed by the visual significance results. Using the suggested methods, alternate designs can be synthesized while preserving brands’ design styles, and design trends can be analyzed for strategic evaluation.     

基于抽象域和数值熵协同的数值程序分析

在软件日益丰富的信息时代,程序的正确性验证问题需要深入地研究。提出了基于抽象解释和数值熵协同的数值程序正确性分析方法。利用抽象解释理论框架对数值程序进行抽象解释分析,提取不变量的抽象域区间;在抽象域区间上进行数值熵运算;运行程序获取数值变量的实际取值,计算数值熵;将抽象域区间数值熵和实际数值熵信息进行对比分析,准确地判断程序的正确性等性质。单纯的抽象解释分析只可以近似得到数值变量的取值范围,而引入数值熵算法,在取值范围的基础上对程序静态分析的准确性进一步检验,同时也做到了对程序的正确性验证。通过C语言程序实例,对抽象解释基础上的熵值分析方法进行了验证,证明了该分析方法的可行性和正确性。     

Crowd behaviors analysis and abnormal detection based on surveillance data

Crowd analysis and abnormal trajectories detection are hot topics in computer vision and pattern recognition. As more and more video monitoring equipments are installed in public places for public security and management, researches become urgent to learn the crowd behavior patterns through the trajectories obtained by the intelligent video surveillance technology. In this paper, the FCM (Fuzzy c-means) algorithm is adopted to cluster the source points and sink points of trajectories that are deemed as critical points into several groups, and then the trajectory clusters can be acquired. The feature information statistical histogram for each trajectory cluster which contains the motion information will be built after refining them with Hausdorff distances. Eventually, the local motion coherence between test trajectories and refined trajectory clusters will be used to judge whether they are abnormal.     

基于大数据的数据库安全保障体系浅析

信息化时代的最显著特征就是数据化趋向明显,也可以说信息化时代就是大数据时代,数据潜移默化的出现在我们的生活中,构成多样的数据集合.而数据在某种情况下,又是一种无形的资产,由此带来的安全性思考也逐渐引起现代人的重视.我国基于数据安全而形成的数据库安全防御体系也逐渐完善,在数据安全方面发挥着重要的作用.本文主要针对大数据背景下,数据库安全保障体系的建立进行分析,探究我国数据库安全保障体系的建设工作,从而促进我国数据信息的安全与保密.     

Hybrid electronic tongue for the characterization and quantification of grape variety in red wines

A multisensor system combined with multivariate analysis was applied to the characterization of red wines and to the quantification of the grape variety percentage. The proposed system, known as hybrid electronic tongue, consists of a colorimetric optofluidic system and an array of electrochemical sensors. Three monovarietal red wines were studied: Pinot Noir, Merlot and Cabernet Sauvignon. Homemade mixtures were elaborated from these wines according to a Simplex experimental design with 60 samples. The data obtained were treated using advanced chemometric tools like Principal Component Analysis (PCA) and Soft Independent Modeling Class Analogy (SIMCA) for the classification of the wine mixtures and Partial Least Squares (PLS) regression for the quantification of the grape variety composition. The results have shown a good classification of the grape varieties and the identification of the mixtures with Pinot Noir up to 75%. Besides, using the PLS regression, the system has demonstrated a high potential for quantifying the percentage of each grape variety.     

综合运用模式识别与回归分析的数据融合算法

在混合式系统融合框架下,提出综合运用模式识别和回归分析的数据融合算法．在融合中心,首先处理的是各目标航迹起始段的本地航迹,其次是经粗处理的原始测量．基于模式相似性测度分析形成起始段的融合航迹,并利用融合航迹信息对原始测量数据进行回归分析,估计融合点和置信区间,跟踪滤波形成航迹．通过仿真实验,验证了算法的有效性和实用性．     

MULCOX2: a general computer program for the Cox regression analysis of multivariate failure time data

Multivariate failure time data is commonly encountered in biomedicine, because each study subject may experience multiple events or because there exists clustering of subjects such that failure times within the same cluster are correlated. MULCOX2 implements a general statistical methodology for analyzing such data. This approach formulates the marginal distributions of multivariate failure times by Cox proportional hazards models without specifying the nature of dependence among related failure times. The baseline hazard functions for the marginal models may be identical or different. A variety of statistical inference can be made regarding the effects of (possibly time-dependent) covariates on the failure rates. Although designed primarily for the marginal approach, MULCOX2 is general enough to implement several alternative methods. The program runs on any computer with a FORTRAN compiler. The running time is minimal. Two illustrative examples are provided.