稳定自组织网络结构中单点突变对网络稳定性影响

从战场恶劣环境通信设备无法正常通信的条件下,介绍了一种自组织网络稳定度的模型。通过比较自组织网络结构中微结构和巨结构的优缺点,动态地描述了结构间的聚合和裂解。基于结构分析,考虑单点突变对网络稳定性的影响,从时间因素策略方面,提出了一个基于预测最稳定链路生存时间的一种分布式成群策略。通过合理建模以及优化路由算法,尽可能地维护网络结构的稳定。     

Revisiting inspection game and inspector leadership through reaction networks

The inspection game is a two-player noncooperative game that models a situation where an inspector verifies whether the inspectee complies with the rules (on the assumption that the inspectee has the tendency to violate at least one of the rules). The usual approach in the analysis of this game seeks to find an optimal strategic inspection scheme for each of the two players yielding favorable payoffs. Recently, there have been some developments in the study of such games that use a mathematical structure known as reaction network involving a set of molecular species and the existing reactions among these species. In this paper, we use a reaction network to analyze the inspection game giving an alternative way of modeling the social situation. The molecular species play the role of the players' decision moves and their resulting gain or loss, while the reactions are the encounters of the decisions of the players which, as expected, yield payoffs. We reexamine the dynamics of the inspection game through the lens of reaction network theory and consider various situations that call for more detailed analyses such as equal or unequal reaction rates and inspection leadership. Conditions concerning reaction rates, initial population of decision species, benefits, and costs are determined in order to identify strategies that yield better payoffs both for the inspector and inspectee. These results illustrate practical insights rooted from the formulated simple game models.     

A misclassification cost‐minimizing evolutionary–neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.     

基于LabVIEW的齿轮箱故障诊断

着重介绍了齿轮箱故障诊断系统的实现过程和设计思想,及通过虚拟仪器开发软件LabVIEW、小波变换和神经网络技术,来实现齿轮箱故障诊断系统的核心内容,最后通过实例验证了此设计的可行性。     

Synchronization in hub terminals with the circular arrangement problem

Hub terminals are important entities in modern distribution networks and exist for any transportation device, that is, cross docks and parcel distribution centers for trucks, container ports for ships, railway yards for trains, and hub airports for aircraft. In any of these hubs, the mid‐term planning task of synchronizing the transshipment of goods and passengers when servicing the transportation devices has to be solved, for which many different solution approaches specifically tailored to the respective application exist. We, however, take a unified view on synchronization in hubs and aim at a general building block. As a point of origin, a basic vertex ordering problem, the circular arrangement problem (CAP), is identified. We explain the relation between the CAP and hub processes, develop suited algorithms for solving the CAP, and extend the basic CAP by multiple additions, for example, arrival times, limited storage space, and multiple service points, make the problem adaptable to a wide range of hub terminals. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 454–469, 2015     

航空自组网中面向容错的中继节点速度控制

由于飞机节点的通信距离有限,航空自组网的网络拓扑高动态的变化会导致频繁的网络分割并严重影响网络上层应用的正常运行。为了保证飞机节点之间端到端的连通性不受影响,航空自组网必须具备容错性,即任意一个节点或链路失效后网络仍然连通。通常情况下飞机节点的运动不可控,因此可在网络中加入一定数量的中继节点,通过控制中继节点的运动速度来实现并维持航空自组网的容错性。提出了一种在线中继节点速度控制方法,该方法根据网络当前状态计算出中继节点的最佳运动方式,在保证网络容错的前提下使得中继节点在网络运行时间内所运动的总路程最短。仿真结果表明该中继节点速度控制方法在航空自组网的容错控制方面具有潜在的应用前景。     

基于竞争式MAC接入的移动Adhoc网络建模与仿真

总结了具有代表性的竞争式MAC接入协议:冲突避免多址接入MACA、IEEE 802.11 DCF和信道获得多址接入FAMA传输介质访问控制接入的一般性规律,建立了一个竞争式MAC接入数学模型,研究哪些因素影响网络通信。通过NS2网络模拟实验,验证了这些影响的存在,并初步了解如何减少这些负面影响。与无线网络物理层或高层网络比起来,MAC接入对于无线网络高效可靠通信的负面影响容易被忽略,实际却存在,该研究对无线网络可靠通信具有一定参考价值。     

基于禁忌搜索算法的指挥控制系统网络组织优化方法

基于复杂网络的拓扑结构优化理论与禁忌搜索算法,结合指挥控制系统的特点,建立了优化最优指挥与控制系统网络结构（Command and Control Architecture Network,C2AN）模型,并以复杂网络的网络效率和网络成本分别描述C2AN中的信息传播效率及构建C2AN的资源消耗,建立优化的目标函数,通过禁忌搜索算法获得了最优的C2AN。最后通过仿真实例,分析了最优C2AN生成过程及其基本统计特征。     

基于加权网络模型的指挥节点重要度评估方法

指挥节点重要度评估是研究指挥网络可靠性和抗毁性的重要内容。通过定义加权网络节点重要度贡献矩阵,提出了一种利用节点重要度评价矩阵确定加权网络关键节点的相对重要度的方法,该方法能充分考虑节点的位置信息、邻接节点的重要度贡献关系;结合作战任务对社区的依赖度,设计了节点全局重要度评估算法,并利用该算法对典型网络节点重要度进行了分析,结果表明算法有效、可行。     

分布式态势感知一致性分析与度量方法研究

一致的态势感知,是现代武器、火力系统以及部队紧耦合协同作战的基础。本文以分布式态势感知理论为依据,从系统的角度,构建了团队达成一致态势感知过程的动力学模型;以系统内部一致性、外部一致性和一致所需最短时间为指标,给出了团队态势感知一致性的度量方法。仿真结果表明,适当地提高团队内沟通的广泛度和相互作用强度,选择合适的沟通网络,对提高一致性能力有积极的作用。