机群管理系统消息传输的负载平衡

提出了一种机群管理系统中管理消息的传递方式,使管理消息以树形方式在机群系统内部扩散传递,根据当前系统状态动态生成消息的剩余传播路径,维持机群系统内部的负载平衡。     

快速聚类算法在个性化服务中的应用

许多实际应用已经证明,k-means算法能够有效地得到好的聚类结果。但是,k-means直接算法的时间复杂度和模式复杂度对数据量的大小非常敏感,无法满足一些高性能的应用场合,如个性化服务中对用户数据进行的群组分析。对此,笔者提出了一种新颖的基于k-d树的聚类算法。这种算法采用空间数据结构—k-d树组织所有的样本数据,可以高效地搜索到离某个给定的聚类中心最近的全部模式。实验结果表明,该方案可以显著提高k-means直接算法的运算速度,在距离运算量和总的运算时间上,可把性能提高1～2个数量级。     

基于免疫进化算法的投影寻踪聚类在公司债券财务质量评级中的应用

针对公司债券财务质量评级问题,采用投影寻踪聚类(PPC)模型,将多维数据指标转换到低维子空间,并利用免疫进化算法(IEA)优化其投影方向,根据投影函数值的大小评价出样本的优劣,从而做出决策。应用表明,该方法能利用一个综合反映多因素之间复杂关系的特征指标给出客观而直接的评价结果,且根据计算结果还可以分析各个指标对综合评价结果的影响程度,为公司债券财务质量评级提供了一种计算过程简单、直观的新方法。     

面向修复的集群高可用技术分析

面向修复计算把硬件故障、软件错误和操作员失误当作不可避免的事实,研究的是面对事实的处理技术。它着眼于通过降低MTTR而提高系统的可用性,同时减少系统的总拥有成本。本文介绍了可修复计算采用的技术,详细论述了集群系统高可用性所采用的方法和技术。     

基于颜色对的色彩量化算法

提出了一种新颖的基于颜色对的色彩量化算法。综合考虑了色彩层次感和关键细节的要求,并且可以根据具体色彩量化任务的要求,对它们的偏重性做出调整,以得到最满意的量化结果。     

PBS中的任务调度以及一类资源分配失效的解决

首先对PBS强大的任务调度功能做了详细的介绍,然后实现了PBS和MPICH的绑定,从而解决了使用PBS和MPICH所带来的资源分配失效的问题。     

企业集群的网络化制造研究

为适应日益激烈的市场竞争压力,企业集群成为当今中小企业发展的重要方向。随着信息技术和计算机网络技术的发展,网络化制造的出现使企业集群的优势更加凸显,两者互相促进,形成良性循环。本文对企业集群及其成因、企业集群的网络化制造机理、企业集群的网络化制造运行模式及实施过程进行了一些探讨。     

Kinetic Monte Carlo simulation of low-pressure chemical vapor deposition of silicon nitride: Impact of gas flow rate and temperature on silicon cluster size and density

In the present study, the deposition process of SiN_x thin films obtained by a low-pressure chemical vapor deposition technique with a mixture of disilane (Si₂H₆) and ammonia (NH₃) was simulated by using the kinetic Monte Carlo method. A new pattern describing the distribution of ammonia molecules in the simulation matrix was proposed. The influences of the NH₃/Si₂H₆ gas flow ratio and the deposition temperature on the obtained films structure in terms of silicon cluster size and density were analyzed. The simulation results indicate that an increase in the gas flow ratio leads to the deposition of amorphous silicon clusters characterized by small sizes. Nevertheless, an increase in the temperature values of the process provokes an enhancement in the silicon cluster size along with a decrease in their density.     

Alarm management practices in natural gas processing plants

In industrial data sets, groups of variables often move together. Monitoring all these variables may result in many nuisance alarms. However, it is possible to take advantage of redundant information to design and reduce the size of alarm sets. The present work reports the application of an alarm management protocol based on alarm priorization to three large Natural Gas Processing Plants, during a three year period, and also investigates the use of different correlation analyses techniques as tools to assist in the further reduction of the number of alarms. The results show that the adopted practices enable the reduction of alarms.     

Hierarchical Correlation Clustering in Multiple 2D Scalar Fields

Sets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time‐dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2‐dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.