Neural network-based event-triggered cluster quasi-consensus for unknown multiagent systems with directed topology

This paper studies cluster quasi-consensus problem for a class of unknown nonlinear multiagent systems (MASs) with directed communication topology. First, a distributed continuous neural network (NN)-based adaptive protocol is presented for solving this problem by introducing reference model to each agent. Then, taking limited communication resource and energy consumption into account, a distributed event-triggered cluster quasi-consensus protocol is proposed. Different from the existing results, two event-triggered mechanisms are constructed in the proposed event-triggered protocol to reduce communication load and control update frequency as possible. The sufficient conditions that guarantee cluster quasi-consensus under the both proposed protocols are obtained, respectively. Zeno behavior is proved to be excluded. Finally, simulation results verify the effectiveness of the proposed protocols.     

Hierarchical and Hybrid Cell Load Balancing in 5G Heterogeneous Mobile Networks

In Fifth Generation (5G) Heterogeneous Mobile Networks (HetNets), deploying dense small cell networks makes user association more challenging. The process of collecting cell load information from the User Equipments (UEs) and broadcasting the feedback message involves significant overhead and time complexity. Moreover, the UEs may not know the optimum cell to reselect, satisfying its data rate requirements. In order to overcome these drawbacks, in this paper, we propose to design an Hierarchical and Hybrid Cell Load Balancing (HHCLB) technique using Selective Handoff. In this technique, the UEs of each cell are grouped into clusters depending on their proximity distance. Each cluster contains a cluster controller (CC) which is in charge of determining the intra-cell load and redirecting the cell-reselection request of a UE. If the data rate of any UE in a cluster becomes less than its required rate, then the cell reselection process is performed. By simulation results, it is shown that load balancing can be done proactively (implicitly) by the CCs when the load is unbalanced or can be done on demand (explicitly) when a UE send a request for cell reselection. In the case of Macro cells, HHCLB attains 71% higher throughput for low load scenario and 59% higher throughput for high load scenario. Similarly, in the case of Femto cells, HHCLB attains 19% higher throughput for low load scenario and 27% higher throughput for high load scenario.     

数据结构与算法核心课程建设

针对数据结构与算法课程理论联系实际的特点,文章提出在课程建设过程中应不断完善课程体系,加强团队建设,深入教学改革以及丰富教学资源,从培养学生的兴趣入手,注重学生思维能力、动手能力和创新能力的培养,最终达到提高教学质量的目的。     

高性能机群计算本科教学研究

针对目前高性能机群人才需求旺盛而高校对高性能机群计算知识普及不够的现状,以培养工程实践性机群系统管理人员为目标,阐述面向高校计算机专业本科生开设高性能机群计算课程的教学目标;并进行课程设计,包括教学内容及教材选取、课时安排以及教学方法设计。北京工业大学的教学实践表明,上述课程设计取得了较好的教学成效。     

A graph-theoretical clustering method based on two rounds of minimum spanning trees

Many clustering approaches have been proposed in the literature, but most of them are vulnerable to the different cluster sizes, shapes and densities. In this paper, we present a graph-theoretical clustering method which is robust to the difference. Based on the graph composed of two rounds of minimum spanning trees (MST), the proposed method (2-MSTClus) classifies cluster problems into two groups, i.e. separated cluster problems and touching cluster problems, and identifies the two groups of cluster problems automatically. It contains two clustering algorithms which deal with separated clusters and touching clusters in two phases, respectively. In the first phase, two round minimum spanning trees are employed to construct a graph and detect separated clusters which cover distance separated and density separated clusters. In the second phase, touching clusters, which are subgroups produced in the first phase, can be partitioned by comparing cuts, respectively, on the two round minimum spanning trees. The proposed method is robust to the varied cluster sizes, shapes and densities, and can discover the number of clusters. Experimental results on synthetic and real datasets demonstrate the performance of the proposed method.     

军事无线传感器网络路由协议的研究

针对军事无线传感器网络节点部署区域的特殊性及节点易遭破坏的特点,提出了基于LEACH协议的军事无线传感器网络簇头多跳算法(LEACH-HOP)。模拟仿真证明,LEACH-HOP与LEACH相比具有更好的性能,可以有效延长网络生存时间。     

InfoSigs:一种面向Web对象的细粒度聚类算法

面向Web对象的细粒度聚类已经成为学术界研究的热点.然而现有大多数聚类模型只关注如何对文本内容或文章主题进行聚类,聚类结果粒度较粗,无法满足大规模网络信息检索的质量要求.针对上述挑战,充分挖掘Web文档中词汇间的树状概率层次关系,提出一种以词汇信息分布作为特征标志的聚类算法InfoSigs,实现对Web对象的细粒度聚类.算法构建一个信息传递有向无环图,根据词汇在图中信息分布的集中度赋予其合理的权重,产生更具代表性的特征向量;同时算法提出了一个自适应的记录合并模型,有效提高记录簇中记录间的相似度,减少噪音对合并过程的影响.实验结果表明,InfoSigs算法比传统聚类算法—I-Match和Shingling—在F-Measure值上平均约有21.3%的提高,可以有效地运用到多领域Web对象的聚类问题.     

MANET网中基于簇结构的认证服务方案

针对移动Ad Hoc网络无中心的特性及一般分布式认证中单跳认证节点少于门限值、CA节点不可靠等问题,采用基于多层簇的体系结构和 Feldman可验证秘密共享技术,提出一种基于簇结构的认证服务方案,结果证明该方案可实现各级簇头和簇成员的分布式认证,解决单跳内认证节点少于门限值问题,防止对系统私钥的重构等外部攻击及内部被攻破节点对分布式认证的干扰。     

基于中心距序降维的聚类算法

为提高金融业务数据集上的聚类质量和聚类效率,提出簇的直径、簇间的相似度这2个概念。利用距离尺度降维的中心距序降维法,将多维数据降至一维,在一维上利用自适应排序聚类算法ASC聚类。该算法和传统的Cobweb算法、K-means算法做对比,实验表明该方法能提高簇间相似度,最大提高200%。