数据立方体上的虚拟视图机制

繁琐复杂的分析过程是OLAP面临的主要问题.当前的解决方法由于与具体的分析任务相关而存在不足.在数据立方体中引入虚拟视图机制解决了这个问题.一个虚拟视图是从原始实事表衍生出来的逻辑数据集合.给出了数据立方体上的虚拟视图的定义,并分3种情况讨论了虚拟视图的实现策略,以及对应的OLAP分析方法.实验证明了所述方法的可行性和有效性.     

全局优化径向基函数网络方法的设计及应用

针对径向基神经网络（RBFNN）中隐层单元中心及输出层权值向量难以有效确定的问题,论文提出了一种基于进化思想的解决方案.以进化算法中的模式定理为理论依据,运用分治策略思想,将隐层中心的最优化过程和输出层权值向量的最优化过程并行处理,提高了算法效率.最后将设计的整体进化径向基神经网络应用于数据的分类,以UCI数据库中的iris和wheat数据集为测试物料,采用该文提出的进化方案得到最优中心和权值向量,测试表明相对于RBFNN运用聚类办法确定中心和最小二乘确定权值的方法以及支持向量机其检出率能提高20％.     

基于Skinner 操作条件反射的抽样一致性算法

针对基础矩阵的估计问题,提出一种基于Skinner概率自动机的抽样一致性算法(Skinner-Ransac)。该算法对数据样本集合中的每个样本赋予权值,并根据当前的抽样结果对每一个样本的权值进行更新;同时,针对先验知识缺乏的情况提出了3种迭代终止条件。以一组模拟数据和一组真实图像作为实验对象,与4个现有算法进行对比的实验结果表明, Skinner-Ransac无论在迭代次数,还是在计算精度上均优于其他算法。     

渐进网格简化模型的改进算法

渐进网格模型可以用于层次细节的实现、三维网格的简化及重建等.改进并实现了一种渐进网格生成算法,针对渐进网格在简化和重建过程中各边权值计算中出现的误差,给出一种改进的权值公式,以便更精确地确定各边的优先级.同时针对简化过程中,各个块之间会出现裂缝,提出裂缝消除的方法.实验结果表明,该方法可以有效的简化模型,以满足用户的需求并提高效率.     

基于模糊熵的多传感器加权融合算法

针对目前多传感器数据融合时,各传感器的权值难以确定的问题,结合模糊理论,提出一种基于模糊熵的多传感器加权融合算法.该算法不需要任何的环境先验信息和传感器参数信息,根据各个传感器的当前有效量测数据的模糊程度不同,通过求取实时有效量测集合的模糊熵来确定该传感器在融合时的权值.仿真实验证明:该算法具有很好的环境适应能力,可以在一定程度上提高量测精度.     

基于局部线性加权的离群点检测方法

为了提高高维数据集合离群数据挖掘效率,在分析了传统的离群数据挖掘算法优点和缺点的基础上,提出了一种基于局部线性加权的离群点检测算法.该算法利用LLE算法的思想寻找样本数据的内在嵌入分布,并通过距离公式和离群点权值判别式进行权值数据判定,根据权值的大小标识出数据集中的离群点.仿真实验的结果表明了该方法能够有效地发现高维数据集中的离群点.与此同时,该算法具有参数估计简单、参数影响不大等优点.该算法为离群点检测问题的机器学习提供了一条新的途径.     

层次分析法在电网信息系统安全评估分析中的应用

信息系统安全评估中的指标分配是评估过程中一项重要活动。介绍了层次分析法的原理及其在电网信息系统安全评估指标分配中的应用过程,包括评估的层次接口模型的建立,评估系统中各层元素相对权值的确定,各评估模块相对于系统安全的组合权重的确定。最后结合工作经验,对开展电网信息系统安全评估给出了思考与建议。     

基于虚拟领队的不确定轮式移动机器人自适应编队控制

针对包含不确定动力学因素的轮式移动机器人的编队控制问题,运用虚拟领队的概念,提出一种基于径向基神经网络的自适应编队控制方法.首先通过虚拟领队的引入,将高维编队控制问题转化为单个移动机器人的跟随问题;然后,利用权值自适应变化的径向基神经网络在线估计跟随机器人不确定部分的上界值,大大简化了控制器的设计过程;最后,应用Lyapunov稳定性理论和Barbalat定理证明了闭环系统的稳定性.仿真结果表明了所提出控制策略的有效性.     

基于数据手套的虚拟手建模与驱动

针对传统虚拟操作的不足,结合WSTG love5数据手套等硬件设备,本文提出一种新的虚拟操作方法。采用面向对象的方法实现数据手套数据的获取,采用层次建模方法建立虚拟手的几何模型,实现对虚拟手的控制以及虚拟手对物体的虚拟操作,并给出具体的应用实例。     

基于核的PP主成分分析及其在离群聚类中的应用

为了提高高维数据集合离群数据挖掘效率,在分析了传统的离群数据挖掘算法优点和缺点的基础上,提出了一种离群聚类算法,该算法将核方法与PP主成分变换结合于离群聚类算法中,采用基于核的PP主成分变换进行数据维数消减。通过该数据变换矩阵得到相应的非线性向量,并为每个向量分配一个动态权值,在优化经典的FCM模糊聚类的目标优化迭代函数基础上,最终得到各个数据的权值,根据权值的大小标识出数据集中的离群点,理论上证明了该算法的收敛性,仿真实验的结果表明了该方法能够有效地发现高维数据集中的离群点。     

Handling interaction in fuzzy production rule reasoning

When fuzzy production rules are used to approximate reasoning, interaction exists among rules that have the same consequent. Due to this interaction, the weighted average model frequently used in approximate reasoning does not work well in many real-world problems. In order to model and handle this interaction, this paper proposes to use a nonadditive nonnegative set function to replace the weights assigned to rules having the same consequent, and to draw the reasoning conclusion based on an integral with respect to the nonadditive nonnegative set function, rather than on the weighted average model. Handling interaction in fuzzy production rule reasoning in this way can lead to a good understanding of the rules base and an improvement of reasoning accuracy. This paper also investigates how to determine from data the nonadditive set function that cannot be specified by a domain expert.     

云数据中心中一种物理资源利用阈值边界管理策略

提出了云数据中心的一种物理资源利用阈值边界管理策略RUT-MS(physical resource utilization thresholds management strategy)。RUT-MS把虚拟机迁移过程进一步划分为超负载主机检测、虚拟机选择、虚拟机放置第1阶段、低负载主机检测和虚拟机放置第2阶段。使用一种迭代权重线性回归方法来预测物理资源的阈值上限,避免超负载的物理主机数量的增加;采用最小能量消耗策略完成虚拟机选择过程。使用多维物理资源的均方根来确定其资源使用阈值下限,减少低负载主机数量。实验结果表明： RUT-MS物理资源利用阈值边界管理策略使云数据中心的能量消耗和虚拟机迁移次数明显减少,SLA违规率和SLA及能量消耗联合指标只有少量的增加。     

虚拟机迁移中一种新的物理主机异常状态检测算法

提出了一种新的物理主机异常状态检测算法PHSDA(Physical host status anomalous detection algorithm)。PHSDA算法包括两个阶段;在超负载检测中,采用一种迭代权重线性回归方法来预测物理资源的使用效率情况;在低负载检测中,利用多维物理资源的均方根来确定其资源使用阈值下限,避免异常状态的物理主机数量的增加; PHSDA检测算法配合迁移过程中后续的虚拟机选择策略和虚拟机放置策略,就可以形成一个全新的虚拟机迁移模型PHSDA-MMT-BFD。以CloudSim模拟器作为PHSDA的仿真环境。经PHSDA策略优化过后的新虚拟机迁移实验表明：比近几年的BenchMark迁移模型比较起来,可以很好的降低云数据中心的能量消耗,虚拟机迁移次数减少,云服务质量明显提高。     

Modeling uncertain variables of the weighted average operation by fuzzy vectors

The paper deals with the fuzzy extension of the weighted average operation. First, we study the convenient ways how uncertain weights and weighted values can be modeled by fuzzy vectors. We show that, in comparison to a tuple of fuzzy numbers that have been used for modeling uncertain values of particular weights and weighted values up to now, fuzzy vectors extend the possibilities of utilizing the vague expert information concerning the weights and weighted values. Next, we focus on computation of a fuzzy weighted average of a fuzzy vector of weighted values with a fuzzy vector of weights. We derive a general formula and we study its special forms. The advantage of the approach presented in the paper is that the resulting fuzzy weighted average is not overly imprecise since every available information about its variables is involved in computation. This fact is illustrated by several examples. Finally, we briefly discuss the problem of defuzzification of the resulting fuzzy weighted average.     

The weighted sum method for multi-objective optimization: new insights

As a common concept in multi-objective optimization, minimizing a weighted sum constitutes an independent method as well as a component of other methods. Consequently, insight into characteristics of the weighted sum method has far reaching implications. However, despite the many published applications for this method and the literature addressing its pitfalls with respect to depicting the Pareto optimal set, there is little comprehensive discussion concerning the conceptual significance of the weights and techniques for maximizing the effectiveness of the method with respect to a priori articulation of preferences. Thus, in this paper, we investigate the fundamental significance of the weights in terms of preferences, the Pareto optimal set, and objective-function values. We determine the factors that dictate which solution point results from a particular set of weights. Fundamental deficiencies are identified in terms of a priori articulation of preferences, and guidelines are provided to help avoid blind use of the method.     

赋权控制分配策略的空间优化设计

为提高静态赋权控制分配策略虚拟指令可达集的使用率,基于改进粒子群算法提出了一种离线确定控制指令最优化权值的空间优化设计方案.分别建立了加权伪逆法和混合优化法的数学模型,推导出统一的控制律形式,并给出了构造可达集空间的算法:引入量子和遗传因了,通过粒子的交叉操作增强粒子群的多样性,以快速求解控制指令权值的全局最优点.仿真结果表明,设计的方案可以实现赋权控制分配策略可达集空间的最大化.     

Intuitionistic Fuzzy Aggregation Operators

An intuitionistic fuzzy set, characterized by a membership function and a non-membership function, is a generalization of fuzzy set. In this paper, based on score function and accuracy function, we introduce a method for the comparison between two intuitionistic fuzzy values and then develop some aggregation operators, such as the intuitionistic fuzzy weighted averaging operator, intuitionistic fuzzy ordered weighted averaging operator, and intuitionistic fuzzy hybrid aggregation operator, for aggregating intuitionistic fuzzy values and establish various properties of these operators.     

Distributed computing methodology for training neural networks in an image-guided diagnostic application

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.     

Combining fuzzy AHP with MDS in identifying the preference similarity of alternatives

Multidimensional scaling (MDS) analysis is a dimension-reduction technique that is used to estimate the coordinates of a set of objects. However, not every criterion used in multidimensional scaling is equally and precisely weighted in the real world. To address this issue, we use fuzzy analytic hierarchy process (FAHP) to determine the weighting of subjective/perceptive judgments for each criterion and to derive fuzzy synthetic utility values of alternatives in a fuzzy multi-criteria decision-making (FMCDM) environment. Furthermore, we combine FAHP with MDS to identify the similarities and preferences of alternatives in terms of the axes of the space, which represent the perceived attributes and characteristics of those alternatives. By doing so, the visual dimensionality and configuration or pattern of alternatives whose weighted distance structure best fits the input data can be obtained and explained easily. A real case of expatriate assignment decision-making was used to demonstrate that the combination of FAHP and MDS results in a meaningful visual map.     

基于WSSRC单样本人脸识别及样本扩充方法研究

由于传统的SRC方法的实时性不强、单样本条件下算法性能低等缺点,提出了融合全局和局部特征的加权超级稀疏表示人脸识别方法(WSSRC),同时采用一种三层级联的虚拟样本产生方法获取冗余样本,将生成的多种表情和多种姿态的新样本当成训练样本,运用WSSRC算法进行人脸识别分类。在单样本的情况下,实验证实在ORL人脸库上该方法比传统的SRC方法提高了15.53%的识别率,使用在FERET 人脸库上则提高7.67%。这样的方法与RSRC 、SSRC、DMMA、DCT-based DMMA、I-DMMA相比,一样具备较好的识别性能。