汽车企业集群备件管理及信息系统设计

分析汽车企业集群中备件管理的重要性和备件管理中存在的问题,对汽车企业集群的备件进行分类,并提出相应的管理策略。在此基础上设计信息系统方案,最后给出该管理策略及信息系统方案在一个汽车企业集群中的初步应用情况。     

基于遗传算法的胸部CT图像肺组织分割

肺组织分割是肺结节检测、肺功能定量分析、三维重建与可视化计算等胸部CT图像分析处理的基础。该文采用了一种基于遗传算法的边缘检测方法直接分割原始胸部CT图像的肺组织,利用遗传算法的全局寻优能力,以最大类间方差为适应度函数自动搜索最佳边缘检测阈值,并结合形态学处理提取肺组织边缘以实现肺组织分割。实验结果表明,该方法能简化分割处理,且分割效果较好,有不错的应用前景。     

多核并行技术在Image Quilting纹理合成算法中的应用

针对Image Quilting纹理合成算法的新旧纹理块拼接特点,研究了一种创建多任务实现纹理块切割拼接的并行编程模式.设计了重叠区域缝合路径计算的多任务模块函数iq(),利用MATLAB多核集群中创建的调度器将子任务分配到各个节点上并行执行计算.实验结果表明,该算法获得了较好的加速比,提高了多核CPU的使用效率,有效地提升了多核计算机资源的利用率.     

Cluster-assembled metallic glasses

A bottom-up approach to nanofabricate metallic glasses from metal clusters as building blocks is presented. Considering metallic glasses as a subclass of cluster-assembled materials, the relation between the two lively fields of metal clusters and metallic glasses is pointed out. Deposition of selected clusters or collections of them, generated by state-of-the-art cluster beam sources, could lead to the production of a well-defined amorphous material. In contrast to rapidly quenched glasses where only the composition of the glass can be controlled, in cluster-assembled glasses, one can precisely control the structural building blocks. Comparing properties of glasses with similar compositions but differing in building blocks and therefore different in structure will facilitate the study of structure–property correlation in metallic glasses. This bottom-up method provides a novel alternative path to the synthesis of glassy alloys and will contribute to improving fundamental understanding in the field of metallic glasses. It may even permit the production of glassy materials for alloys that cannot be quenched rapidly enough to circumvent crystallization. Additionally, gaining deeper insight into the parameters governing the structure–property relation in metallic glasses can have a great impact on understanding and design of other cluster-assembled materials.     

基于灰色关联度的聚类分析机械故障诊断方法

本文以灰色系统理论中的关联分析方法为基础．建立了评价待分析体系的聚类方法，并将该方法应用于机械故障诊断模式识别。实际诊断表明，该方法是一种行之有效的简便方法．     

Inferential structure of the NEO Five-Factor Inventory: Construct validity of the Big Four personality clusters.

Four studies investigated the dimensionality of the NEO Five-Factor Inventory (NEO-FFI; P. T. Costa & R. R. McCrae, 1992). In Study 1, four inferential dimensions and four clusters represented the NEO-FFI when 114 undergraduates freely sorted items into categories. Construct validity for four item-clusters derived from the inferential space was obtained in Study 2 based on self-report with 304 undergraduates. Study 3 validated these inferential clusters using self and peer reports for 420 undergraduates. Study 4 validated the cluster scales for predicting quality of life and significant social and cultural behaviours for 110 undergraduates. Implications for implicit personality theory, the number of dimensions issue in personality, and test construction are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An Enhanced Parallel Loop Self-Scheduling Scheme for Cluster Environments

Approaches for dealing with scheduling and load-balancing in PC-based cluster systems are famous and well known. In such environments, Self-Scheduling Schemes are suitable for parallel loops with independent iterations. However, while schemes such as FSS, GSS, and TSS fit most computer systems, they cannot provide good load-balancing. Chao-Tung Yang and Shun-Chi Chang proposed a parallel loop scheduling scheme for heterogeneous PC cluster systems in Yang and Chang [13]. Though the proposed scheme allows users to choose parameters before execution initialization, weaknesses in it motivated us to develop further improvements. For instance, using fixed and monotonous parameters can easily lead to invalid scheduling due to use of previously input information. Thus, in this paper we propose a new scheme that fits most widely available computer systems and allows the scheduling parameter to be adjusted dynamically in order to provide higher overall performance.     

The Effect of Process Topology and Load Balancing on Parallel Programming Models for SMP Clusters and Iterative Algorithms

This article focuses on the effect of both process topology and load balancing on various programming models for SMP clusters and iterative algorithms. More specifically, we consider nested loop algorithms with constant flow dependencies, that can be parallelized on SMP clusters with the aid of the tiling transformation. We investigate three parallel programming models, namely a popular message passing monolithic parallel implementation, as well as two hybrid ones, that employ both message passing and multi-threading. We conclude that the selection of an appropriate mapping topology for the mesh of processes has a significant effect on the overall performance, and provide an algorithm for the specification of such an efficient topology according to the iteration space and data dependencies of the algorithm. We also propose static load balancing techniques for the computation distribution between threads, that diminish the disadvantage of the master thread assuming all inter-process communication due to limitations often imposed by the message passing library. Both improvements are implemented as compile-time optimizations and are further experimentally evaluated. An overall comparison of the above parallel programming styles on SMP clusters based on micro-kernel experimental evaluation is further provided, as well.     

Optimal server allocations for streaming multimedia applications on the Internet

In this paper, we address the server selection problem for streaming applications on the Internet. The architecture we consider is similar to the content distribution networks consisting of geographically dispersed servers and user populations over an interconnected set of metropolitan areas. Server selection issues for Web-based applications in such an environment have been widely addressed; the selection is mostly based on proximity measured using packet delay. Such a greedy or heuristic approach to server selection will not address the capacity planning problem evident in multimedia applications. For such applications, admission control becomes an essential part of their design to maintain Quality of Service (QoS). Our objective in providing a solution to the server selection problem is threefold: first, to direct clients to the nearest server; second, to provide multiple sources to diffuse network load; third, to match server capacity to user demand so that optimal blocking performance can be expected. We accomplish all three objectives by using a special type of Linear Programming (LP) formulation called the Transportation Problem (TP). The objective function in the TP is to minimize the cost of serving a video request from user population x using server y as measured by network distance. The optimal allocation between servers and user populations from TP results in server clusters, the aggregated capacity of each cluster designed to meet the demands of its designated user population. Within a server cluster, we propose streaming protocols for request handling that will result in a balanced load. We implement threshold-based admission control in individual servers within a cluster to enforce the fair share of the server resource to its designated user population. The blocking performance is used as a trigger to find new optimal allocations when blocking rates become unacceptable due to change in user demands. We substantiate the analytical model with an extensive simulation for analyzing the performance of the proposed clustered architecture and the protocols. The simulation results show significant difference in overall blocking performance between optimal and suboptimal allocations in as much as 15% at moderate to high workloads. We also show that the proposed cluster protocols result in lower packet loss and latencies by forcing path diversity from multiple sources for request delivery.