Dynamic Affinity Cluster Allocation in a Shared Disks Cluster

A shared disks (SD) cluster couples multiple computing nodes for high performance transaction processing, and all nodes share a common database at the disk level. In the SD cluster, a front-end router selects a node for an incoming transaction to be executed. An affinity-based routing can increase the buffer hit ratio of each node by clustering transactions referencing similar data to be executed on the same node. However, the affinity-based routing is non-adaptive to the changes of the system load. This means that a specific node would be overloaded if corresponding transactions rush into the system. In this paper, we propose a new transaction routing algorithm, named Dynamic Affinity Cluster Allocation (DACA). DACA can make an optimal balance between the affinity-based routing and indiscriminate sharing of load in the SD cluster. As a result, DACA can increase the buffer hit ratio and reduce the frequency of inter-node buffer invalidations while achieving the dynamic load balancing.     

Fuzzy systems design: direct and indirect approaches

A systematic classification of the data-driven approaches for design of fuzzy systems is given in the paper. The possible ways to solve this modelling and identification problem are classified on the basis of the optimisation techniques used for this purpose. One algorithm for each of the two basic categories of design methods is presented and its advantages and disadvantages are discussed. Both types of algorithms are self-learning and do not require interaction during the process of fuzzy model design. They perform adaptation of both the fuzzy model structure (rule-base) and the parameters. The indirect approach exploits the dual nature of Takagi-Sugeno (TS) models and is based on recently introduced recursive clustering combined with Kalman filtering-based procedure for recursive estimation of the parameter of the local sub-models. Both algorithms result in finding compact and transparent fuzzy models. The direct approach solves the optimisation problem directly, while the indirect one decomposes the original problem into on-line clustering and recursive estimation problems and finds a sub-optimal solution in real-time. The later one is computationally very efficient and has a range of potential applications in real-time process control, moving images recognition, autonomous systems design etc. It is extended in this paper for the case of multi-input–multi-output (MIMO systems). Both approaches have been tested with real data from an engineering process.     

K-means method for rough classification of R&D employees' performance evaluation

This paper proposes an approach that can roughly cluster a data set with fuzzy linguistic entries as a prior data arrangement for performance evaluation of R&D employees. The extension principles of fuzzy linguistic numbers are used to modify the K‐means method for handling the linguistic data set. We define the absolute difference of fuzzy linguistic variables as their fuzzy distance. Based on this definition, the K‐means approach can be modified slightly for clustering purposes. The performance of employees engaged in designing and R&D‐oriented jobs is possibly related to some qualitative attributes and the evaluation of such attributes for each employee has a tendency toward semantic scales. In the proposed approach, the supervisor can evaluate the performance of each employee directly with a semantic scale. The modified K‐means approach can roughly cluster their performance into different classes in advance of applying some other sophisticated processes.     

Boundary-trimmed 3D triangular mesh segmentation based on iterative merging strategy

This paper presents a segmentation algorithm for 3D triangular mesh data. The proposed algorithm uses iterative merging of adjacent triangle pairs based on their orientations. The oversegmented regions are merged again in an iterative region merging process. Finally, the noisy boundaries of each region are refined. The boundaries of each region contain perceptually important geometric information of the entire mesh model. According to the purpose of the segmentation, the proposed mesh-segmentation algorithm supports various types of segmentation by controlling parameters.     

归一化型划分熵

讨论了基于样本最大分类信息的改进划分熵的若干性质 ,并对Bezdek提出的划分熵、基于样本最大分类信息的改进划分熵、划分熵的归一化形式的分类性能进行了比较实验。结果表明归一化形式的分类性能是最好的。     

模糊聚类方法ISODATA在自由锻辅助设计中的应用

基于派生成CAPP专家系统的GT分类特点，论述了ISODATA方法的基本概念，重要性，主要步骤及算法，重点介绍了聚类判据与准则，实验表明了该方法的有效性，为自由锻CAPP专家系统设计提供了依据。     

逻辑集群与数据缓存在网络数据库中的应用

在网络计算环境下，当大量客户同时存取数据库资源，数据库服务器常成为性能瓶颈。将集群技术应用于数据库系统结构，通过将具有相似数据存取模式的客户形成一个逻辑集群，由一中间层服务器负责集群范围内的目录服务和缓存管理，形成客户用／服务器／服务器三层结构。为获得更好的性能，客户机，特别是中间层服务器规定有合适的缓存功能。该结构可有效降低服务器负载，提高数据库事务响应性能，有效扩大系统支持的客户规模，具有一定的应用价值。     

一种广义预测模型的研究

传统的预测方法存在着难以全面考虑影响因素，其中包括算法复杂、精度较差、实时应用等缺点，针对供热、空调、电力、供水等系统的负荷既受到环境因素等模拟量的影响，又受到节假日、上下班等开关量影响的特点，将模拟人脑逻辑思维的模糊技术与模拟人脑自学习的神经网络技术相结合的模糊神经网络引入到负荷的预测中，提出了一种新型的基于模糊神经网络的预测模型，并对输入量的选择进行了详细的分析，既考虑了历史数据的影响，又考虑了突发事件的作用，获得了较快的预测速度和较高的预测精度。     

Fractal image compression based on fuzzy theory

Though progress has been made in fractal compression techniques,the long encoding times still remain the main drawback of this technique,which results from the need of performing a large number of range-domain matches,the total encoding time is the sum of the time required to perform each matech,In order to make this method more efficient in practical use,the Fuzzy theory based on feature extraction of the projection and normalized codebook method has een provided to optimize the encoding time,based on the co-means clustering approach.The results of the implementation of Rate Mean Square(RMS),Peak signal noise ratio(PSNR) and the encoding time of this propsed method have been compared to other methods like the Feature Extraction and Self-orgarnization methods to show its efficiency.     

基于模糊概念图的文档聚类及其在Web中的应用

随着World Wide Web上数据量的日益庞大,现有的搜索引擎已经不能满足用户日益增长的需求.利用数据挖掘技术,提高搜索效率,实现了查询的用户化.首先提出了模糊概念图的模型来描述词语间的关系,然后在聚类过程中引入概念知识,提出了基于模糊概念图的文档聚类算法,通过分析用户的浏览行为发现兴趣模式.在上述技术的基础上,给出了一种用户化的智能搜索系统的实现策略,通过分析概念间的关系和用户的兴趣模式,评价超链/文档和查询的相关程度,从而帮助用户得到更准确的信息.