Image Segmentation by Pairwise Nearest Neighbor Using Mumford-Shah Model

Mumford-Shah model has been used for image segmentation by considering both homogeneity and the shape of the segments jointly. It has been previously optimized by complex mathematical optimization methods like Douglas-Rachford, and a faster but sub-optimal k-means. However, they both suffer from fragmentation caused by non-convex segments. In this paper, we present hierarchical algorithm called Pairwise nearest neighbor (PNN) to optimize the Mumford-Shah model. The merge-based strategy utilizing the connectivity of the pixels prevents isolated fragments to be formed, and in this way, reaches better quality in case of images containing complex shapes.

MODELING THE USE OF OUTSOURCING IN THE CONSTRUCTION OF CLUSTER FORMATIONS

Цель статьи – построение комплексной методологии оценки оптимальной структуры формирования информационных кластеров и механизмов их эффективного функционирования. На основе преимуществ аутсорсинга в информационной экономике предложена использована новая форма сетевого взаимодействия – информационный кластер, разработан агломеративный иерархический алгоритм оценки максимальной эффективной сложности и однородности субъектов кластера по различным критериям (информационноемкость, интеллектуальный потенциал, социометрические характеристики и др.), построены модели распределения целей среди наиболее близких в институциональном плане субъектов кластера, которые позволяют решить большую область задач распределения проектов в партнерской сети информационных кластеров, организовывать сетевую распределенную работу и экономить ресурсы времени. The purpose of the article is to build a comprehensive methodology for assessing the optimal structure of the formation of information clusters and the mechanisms for their effective functioning. Based on the advantages of outsourcing in the information economy, a new form of network interaction is proposed - an information cluster, an agglomerative hierarchical algorithm has been developed to assess the maximum effective complexity and homogeneity of cluster subjects according to various criteria (information capacity, intellectual potential, sociometric characteristics, etc.), models for the distribution of goals among the closest in institutional terms of cluster subjects are built, which allow solving a large area of project distribution tasks in a partner network of information clusters, organizing network distributed work and saving time resources.

A High Precision Modeling Technology of Material Surface Microtopography and Its Influence on Interface Mechanical Properties

In order to accurately and effectively obtain the contact performance of the mating surface under the material surface topography characteristics, a numerical simulation method of rough surface based on the real topography characteristics and a multi-scale hierarchical algorithm of contact performance is studied in this paper. Firstly, the surface topography information of materials processed by different methods was obtained and characterized by a measuring equipment; Secondly, a non-Gaussian model considering kurtosis and skewness was established by Johnson transform based on Gaussian theory, and a rough surface digital simulation method based on real surface topography was formed; Thirdly, a multi-scale hierarchical algorithm is given to calculate the contact performance of different mating surfaces; Finally, taking the aeroengine rotor as the object, the non-Gaussian simulation method was used to simulate the mating surfaces with different topographies, and the multi-scale hierarchical algorithm was used to calculate the contact performance of different mating surfaces. Analysis results showed that the normal contact stiffness and elastic–plastic contact area between the mating surfaces of assembly 1 and assembly 2 are quite different, which further verifies the feasibility of the method. The contents of this paper allow to perform the fast and effective calculation of the mechanical properties of the mating surface, and provide a certain analysis basis for improving the surface microtopography characteristics of materials and the product performance.

A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is that this is an innovative hybrid genetic algorithm with the original, hierarchical architecture. In particular, the genetic algorithm is combined with the so-called hierarchical (self-similar) iterated tabu search algorithm, which serves as a powerful local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm. The results of the conducted computational experiments demonstrate the promising performance and competitiveness of the proposed algorithm.

A Review on Data Discrepancy Factor Performance for Industrial Applications using Clustering Algorithms

DDF is the most significant measure among different bunch execution procedures to assess the immaculateness of any group component. Ordinarily, best groups are assessing by processing the quantity of information focuses inside a bunch. At the point when this tally is comparable to the quantity of required information focuses then this group is viewed as great. The greatness of the bunch system is fundamental not exclusively to discover the information check inside a group yet in addition to inspect it by totalling the information focuses these are (I) present inside a group where it ought not be and the other way around and (ii) not grouped for example anomalies (OL). The principle usefulness of DDF is that all bunch focuses can be gathered in comparative groups without exceptions, the current paper features on how contrasted with DDF more effective Clusters can be shaped through the Modern DDF. Further, we assess the exhibition of some grouping calculations, K-Means. As of late we, fostered the Modified K-Means Algorithm and Hierarchical Algorithm by utilizing the Data Discrepancy Factor (DDF).