Isoperimetric graph partitioning for image segmentation

Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability.     

Random walks for image segmentation

A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a high-quality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs     

Recursive segmentation and recognition templates for image parsing

In this paper, we propose a Hierarchical Image Model (HIM) which parses images to perform segmentation and object recognition. The HIM represents the image recursively by segmentation and recognition templates at multiple levels of the hierarchy. This has advantages for representation, inference, and learning. First, the HIM has a coarse-to-fine representation which is capable of capturing long-range dependency and exploiting different levels of contextual information (similar to how natural language models represent sentence structure in terms of hierarchical representations such as verb and noun phrases). Second, the structure of the HIM allows us to design a rapid inference algorithm, based on dynamic programming, which yields the first polynomial time algorithm for image labeling. Third, we learn the HIM efficiently using machine learning methods from a labeled data set. We demonstrate that the HIM is comparable with the state-of-the-art methods by evaluation on the challenging public MSRC and PASCAL VOC 2007 image data sets.     

粘结剂处理的铁基混合粉的温压特性

研究的目标是结合粉末粘结技术和温压技术以制备高质量、高性能的粉末冶金材料。预粘结粉末的一大优点是能有效地降低成分偏析，因此在混粉过程中应尽量避免偏聚。本文采用两种混粉工艺制备预粘结铁基混合粉，通过不同的压制压力、不同的粘结剂含量以常温压制和温压压制制备出铁基粉末冶金材料，测量其压坯密度并进行比较分析。研究结果表明，不同预粘结工艺、压制温度和粘结剂含量对压坯密度影响很大。在一定的粘结剂含量范围内，生坯密度随粘结剂含量增加而呈线性减少。不同的预粘结工艺对生坯密度的影响随着粘结剂含量的增加而增大。     

The share of items of highly productive sources as a function of the size of the system

Summary The research in this paper is based on the paper of D.W. Aksnes & G. Sivertsen: The effect of highly cited papers on national citation indicators, Scientometrics 59 (2) (2004), 213-224, where one states that “the few highly cited papers account for the highest share of the citations in the smallest fields”. This, at first sight, evident property is examined in the theoretical models that exist in the literature. We first define exactly what we mean by “size of a field” (i.e. when is a field “smaller” or “larger” than another one). We show that there are two, non-equivalent possible definitions. Next we define exactly the possible property under study. This leads us again to two possible, non-equivalent formulations. Hence, in total, there are four different formulations to consider. We show, by giving counterexamples, that none of these four formulations are true in general. We also express conditions (in Lotkaian and Zipfian informetrics), under which the property of Aksnes and Sivertsen is true. All these results are not only valid in the papers-citations relationships but in any informetric source-item relationship. In this connection we present formulae describing the share of items of highly productive sources as a function of the parameters of the system (e.g. the size of the system).     

Genetic algorithms combined with discriminant analysis for key variable identification

Many trouble-shooting problems in process industries are related to key variable identification for classifications. The contribution charts, based on principal component analysis (PCA), can be applied for this purpose. Genetic algorithms (GAs) have been proposed recently for many applications including variable selection for multivariate calibration, molecular modeling, regression analysis, model identification, curve fitting, and classification. In this paper, GAs are incorporated with Fisher discriminant analysis (FDA) for key variable identification. GAs are used as an optimization tool to determine variables that maximize the FDA classification success rate for two given data sets. GA/FDA is a proposed solution for the variable selection problem in discriminant analysis. The Tennessee Eastman process (TEP) simulator was used to generate the data sets to evaluate the correctness of the key variable selection using GA/FDA, and the T² and Q statistic contribution charts. GA/FDA correctly identifies the key variables for the TEP case studies that were tested. For one case study where the correlation changes in two data sets, the contribution charts incorrectly suggest that the operating conditions are similar. On the other hand, GA/FDA not only determines that the operating conditions are different, but also identifies the key variables for the change. For another case study where many key variables are responsible for the changes in the two data sets, the contribution charts only identifies a fraction of the key variables, while GA/FDA correctly identifies all of the key variables. GA/FDA is a promising technique for key variable identification, as is evidenced in successful applications at The Dow Chemical Company.     

Friction Stir Welding of Ceramic Particle Reinforced Aluminium Based Metal Matrix Composites

The mechanical and microstructural properties of 6061+20% Al₂O_3p and 7005+10% Al₂O_3p aluminium based metal matrix composites joined by friction stir welding were analyzed in the present study. The two materials were welded into the form of sheets of 7 mm thickness after T6 treatment and were tested in tension at room temperature. The microstructure of the joints was observed by optical microscopy and the fracture surfaces were analyzed by employing a scanning electron microscope equipped with field emission gun in order to study the micromechanisms involved during the deformation.