分布式卫星的环绕对InSAR测高精度的影响

分布式卫星SAR系统是最近提出的一种新的SAR系统,该文结合干涉SAR(InSAR)技术,研究了该系统中InSAR成像所面临的问题,分析和推到了系统环绕引起的干涉相位、基线等参数的变化,以及对成像高度精度的影响,给出了相应的数学模型及关系曲线。分析结果表明随着环绕角度的增加,测高误差增大,必须对相位和基线进行合理修正。     

集群计算并行I/O中动态文件分配策略

针对集群计算系统中并行I／O文件分配问题进行了研究,提出一种新的动态文件分配算法一启发式负载与服务时间变化综合平衡策略。该策略将所需要的访问服务时间相似的文件分配到集群系统的同一结点磁盘上,并保证每个磁盘上的负载值不超过负载平衡的极限值,从而在所有结点磁盘间既基本实现负载均衡,又使每个磁盘上文件间的服务时间变化最小化。实验结果表明了该策略在性能提高方面的有效性。     

高可用性集群中多个节点的心跳模型研究

提出了一种应用于多个节点集群失效检测的心跳模型。该模型包括信任度模型、投票模型和邻居链模型3个子模型。测试表明,利用这3个子模型设计实现的心跳系统能迅速、准确地定位小规模集群中失效的节点,从而能为接下来的热切换等动作做好准备。     

基于移动代理的负载平衡策略

在异构计算环境中负载平衡是一个重要问题。移动代理是一种新的分布计算模式,具有许多优势,比如移动代理能够从一台机器移动到另一台机器执行任务。该文提出了一个基于移动代理的并行计算框架,利用一个二段负载平衡策略使程序能够适应不断变化的异构计算环境。实验结果显示移动代理不仅能够用于并行计算,而且能够有效地改善负载平衡。     

Particulate clusters and permeability in porous media

The permeability of particulate colloidal titanium dioxide, P25, was investigated during sedimentation, permeation and filtration when suspended in water at a consistent ionic strength similar to tap water. Happel's cell model of permeability was used to determine the apparent particle size during these processes, and compared with the size of particle clusters measured using laser diffraction under identical ionic conditions and varying degree of shear. The primary particle size of the P25 was determined to be 28 nm, from consideration of the surface area and density of the particles, and the cluster size during permeation and filtration was close to 100 nm. During sedimentation the cluster size was determined to be close to 10 μm, which is the same size obtained by laser diffraction when measuring under conditions of low shear. Using the above two sizes (28 nm and 10 μm) as limits in Happel's permeability model it was possible to determine an ‘operating envelope’ of permeability that matched the experimentally measured values for the sedimentation, permeation and filtration processes.     

On voting-based consensus of cluster ensembles

Voting-based consensus clustering refers to a distinct class of consensus methods in which the cluster label mismatch problem is explicitly addressed. The voting problem is defined as the problem of finding the optimal relabeling of a given partition with respect to a reference partition. It is commonly formulated as a weighted bipartite matching problem. In this paper, we present a more general formulation of the voting problem as a regression problem with multiple-response and multiple-input variables. We show that a recently introduced cumulative voting scheme is a special case corresponding to a linear regression method. We use a randomized ensemble generation technique, where an overproduced number of clusters is randomly selected for each ensemble partition. We apply an information theoretic algorithm for extracting the consensus clustering from the aggregated ensemble representation and for estimating the number of clusters. We apply it in conjunction with bipartite matching and cumulative voting. We present empirical evidence showing substantial improvements in clustering accuracy, stability, and estimation of the true number of clusters based on cumulative voting. The improvements are achieved in comparison to consensus algorithms based on bipartite matching, which perform very poorly with the chosen ensemble generation technique, and also to other recent consensus algorithms.     

A linguistic approach to classification of bacterial genomes

In the present paper, 188 prokaryote genomes are classified by separately calculating the compositional spectra for the coding and the non-coding parts of the genomes. For each subsequence, the compositional spectrum is transformed into the corresponding point in a vector space. This enables the categorization of genomes into meaningful groups by a formal method. Repeated clustering performed for the coding and the non-coding genome parts makes it possible to estimate the true number of the genome clusters. The method we propose is based on a new application of external cluster validation indexes and on the misclassified quantities obtained in the process of repeated clustering. Besides, we have constructed additional data embedding into the appropriate Euclidean space only on the basis of the distances between compositional spectra. Biological evaluation of the results obtained for the 4-letter and the 2-letter alphabets substantiates the appropriateness of the resulting cluster-based classification.     

Solving the minimum sum-of-squares clustering problem by hyperbolic smoothing and partition into boundary and gravitational regions

This article considers the minimum sum-of-squares clustering (MSSC) problem. The mathematical modeling of this problem leads to a min-sum-min formulation which, in addition to its intrinsic bi-level nature, has the significant characteristic of being strongly nondifferentiable. To overcome these difficulties, the proposed resolution method, called hyperbolic smoothing, adopts a smoothing strategy using a special C^∞ differentiable class function. The final solution is obtained by solving a sequence of low dimension differentiable unconstrained optimization subproblems which gradually approach the original problem. This paper introduces the method of partition of the set of observations into two nonoverlapping groups: “data in frontier” and “data in gravitational regions”. The resulting combination of the two methodologies for the MSSC problem has interesting properties, which drastically simplify the computational tasks.