Graphical methods for evaluating covering arrays

Covering arrays relax the condition of orthogonal arrays by only requiring that all combination of levels be covered but not requiring that the appearance of all combination of levels be balanced. This allows for a much larger number of factors to be simultaneously considered but at the cost of poorer estimation of the factor effects. To better understand patterns between sets of columns and evaluate the degree of coverage to compare and select between alternative arrays, we suggest several new graphical methods that show some of the patterns of coverage for different designs. These graphical methods for evaluating covering arrays are illustrated with some examples. Copyright © 2015 John Wiley & Sons, Ltd.     

SPSS在数据处理中的应用

面对大量零乱的数据,如何从中找出内在规律性,这是一个让人感到非常棘手的问题,也是许多人感到困惑的地方.这一问题在数学建模中尤为突出.探讨如何利用软件SPSS处理实际生活中的数据问题,对一般的数据分析与处理具有较强的实用性和参考价值.     

文人陶瓷山水画的时空观念

虽然陶瓷山水画中的散点透视法可阐释其在表现手法上的许多重要特征,但尚需从其他角度进行比较与关联,才有助于对其美学本质的深刻把握。这里从文人陶瓷山水画的时空观念、玄学对其表现技法的影响以及与西方风景画的比较这三个角度入手,对文人陶瓷山水画的时空观念与技法进行较深入的阐释。     

Selecting Coherent and Relevant Plots in Large Scatterplot Matrices

The scatterplot matrix (SPLOM) is a well‐established technique to visually explore high‐dimensional data sets. It is characterized by the number of scatterplots (plots) of which it consists of. Unfortunately, this number quadratically grows with the number of the data set’s dimensions. Thus, an SPLOM scales very poorly. Consequently, the usefulness of SPLOMs is restricted to a small number of dimensions. For this, several approaches already exist to explore such ‘small’ SPLOMs. Those approaches address the scalability problem just indirectly and without solving it. Therefore, we introduce a new greedy approach to manage ‘large’ SPLOMs with more than 100 dimensions. We establish a combined visualization and interaction scheme that produces intuitively interpretable SPLOMs by combining known quality measures, a pre‐process reordering and a perception‐based abstraction. With this scheme, the user can interactively find large amounts of relevant plots in large SPLOMs.     

ScatterJ: An ImageJ plugin for the evaluation of analytical microscopy datasets

We present ScatterJ, an ImageJ plugin that allows for extracting qualitative as well as quantitative information from analytical microscopy datasets. A large variety of analytical microscopy methods are used to obtain spatially resolved chemical information. The resulting datasets are often large and complex, and can contain information that is not obvious or directly accessible. ScatterJ extends and complements existing methods to extract information on correlation and colocalization from pairs of species‐specific or element‐specific maps. We demonstrate the possibilities to extract information using example datasets from biogeochemical studies, although the plugin is not restricted to this type of research. The information that we could extract from our existing data helped to further our understanding of biogeochemical processes such as mineral formation or heavy metal sorption. ScatterJ can be used for a variety of different two‐dimensional (2D) and three‐dimensional (3D) datasets such as energy‐dispersive X‐ray spectroscopy maps, 3D confocal laser scanning microscopy maps, and 2D scanning transmission X‐ray microscopy maps.     

Fast MR-imaging of porous media

The study of fluid displacements in porous media is of considerable interest for the oil industry as it provides a better understanding of the oil-recovery process. Consequently, fast MR imaging techniques for routine applications of sequencial analysis of large samples would be valuable. The performances of a variant of the Fast Low Angle Shot (FLASH) imaging technique called SNAP with a very short acquisition time of 144 ms per slice were evaluated on water and/or oil-filled core samples such as chalk and limestone whose linewidths were smaller than 800 Hz. The SNAP signal intensity was theoretically reviewed and inversion-recovery preweighted SNAP was used for fastT ₁ measurements in porous media. The images so acquired were processed using a pixel clusterization technique to segment images and roughly estimate oil and water content. This imaging method opens a new field of investigation such as the study of rapid fluid ingress or displacements.Address for correspondence: Catholic University of Louvain, Bâtiment Lavoisier (Bte 3A), Place Louis Pasteur 1, B-1348 Louvain-la-Neuve, Belgium. Additional reprints of this chapter may be obtained from the Reprints Department, Chapman & Hall, One Penn Plaza, New York, NY10119.     

基于维密度和聚类的散点图

散点图矩阵由于其简单有效的优点而成为开发大规模数据集的一种流行和广泛使用的方法.然而,这种技术存在着一些缺陷,在处理大规模数据时,可能会因为数据点的交叉重叠产生视图混乱现象.另外,这种技术很难表现除二维分布之外的其他信息.为了解决上述问题,对当前的散点图技术进行了改进和扩展:a) 利用overview+detail 技术同时展现全局信息和局部信息;b) 利用聚类算法对散点图中的数据进行分组,避免视图混乱.c) 用棒状轴代替直线轴表达各维的数据分布密度,表现更多信息特性.d) 用直方图作为另一种方法表现各维密度信息.e) 开发了一些交互技术来调整视图.最后,设计了一组实验来说明该方法的正确性和有效性.该方法适用于工业,金融业等领域的大规模多维数据集的展示和分析.