基于感知信息共享的C2网络特征分析*

指挥控制(C2,Command and Control)关系网络连接数的增加会增强指控节点之间的信息共享,但也会增加节点信息处理和交换负荷,如果达到一定程度,则会造成节点本身的"信息过载"从而影响C2网络性能.对此,通过研究C2网络在两种不同处理方式下的共享感知信息平均提交时间,用网络节点响应时间的均方差表征一个C2网络的共享态势感知时间的一致性,最后对两种不同结构C2网络特征参数的计算,说明了网络连接增加会导致信息提交时间的延长,但一致性会增强.     

一种基于AMDF和ACF的基音周期提取算法研究

文中提出一种基于短时平均幅度差分函数(AMDF)和短时自相关函数(ACF)的基音周期提取算法。首先,将每帧语音信号通过AMDF算法进行计算,然后把得到的AMDF数值再通过ACF算法进行计算,得出每帧信号的基音周期,最后运用动态平滑技术计算出最终语音信号的基音周期。通过仿真实现该算法,并同基于MAMDF和ACF的基音周期提取算法进行比较。     

Two‐machine flow shop scheduling with common due window to minimize weighted number of early and tardy jobs

This article studies two due window scheduling problems to minimize the weighted number of early and tardy jobs in a two‐machine flow shop, where the window size is externally determined. These new scheduling models have many practical applications in real life. However, results on these problems have rarely appeared in the literature because of a lack of structural and optimality properties for solving them. In this article, we derive several dominance properties and theorems, including elimination rules and sequencing rules based on Johnsos order, lower bounds on the penalty, and upper bounds on the window location, which help to significantly trim the search space for the problems. We further show that the problems are NP‐hard in the ordinary sense only. We finally develop efficient pseudopolynomial dynamic programming algorithms for solving the problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

Approximation algorithms for general one‐warehouse multi‐retailer systems

Logistical planning problems are complicated in practice because planners have to deal with the challenges of demand planning and supply replenishment, while taking into account the issues of (i) inventory perishability and storage charges, (ii) management of backlog and/or lost sales, and (iii) cost saving opportunities due to economies of scale in order replenishment and transportation. It is therefore not surprising that many logistical planning problems are computationally difficult, and finding a good solution to these problems necessitates the development of many ad hoc algorithmic procedures to address various features of the planning problems. In this article, we identify simple conditions and structural properties associated with these logistical planning problems in which the warehouse is managed as a cross‐docking facility. Despite the nonlinear cost structures in the problems, we show that a solution that is within ε‐optimality can be obtained by solving a related piece‐wise linear concave cost multi‐commodity network flow problem. An immediate consequence of this result is that certain classes of logistical planning problems can be approximated by a factor of (1 + ε) in polynomial time. This significantly improves upon the results found in literature for these classes of problems. We also show that the piece‐wise linear concave cost network flow problem can be approximated to within a logarithmic factor via a large scale linear programming relaxation. We use polymatroidal constraints to capture the piece‐wise concavity feature of the cost functions. This gives rise to a unified and generic LP‐based approach for a large class of complicated logistical planning problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

基于多Agent的网络化舰空导弹系统协同机制研究

针对舰艇编队反导防御作战的复杂性,采用网络中心战的作战概念,分析了网络化的舰空导弹系统作战资源共享的优势.以集成火控系统(Integrated Fire Control System)的构建原理为例,描述了网络化舰空导弹系统的网格结构和特点,基于多Agent协同原理,提出了网络化的舰空导弹系统两层"混合式"协同体系结构,并对该结构的协同机制进行了研究.     

基于分布式网络结构的无人机地面控制站设计

针对现有集中式体系结构地面控制站存在可靠性低,功能单一等问题,利用计算机网络技术灵活的网络拓扑结构、并发性、资源共享和功能易于扩展的特性,提出了以冗余双星型网拓扑结构为体系结构的地面控制站设计思路,给出了系统组成方案以及软件设计方法,飞行试验表明系统具有双冗余、高可靠性、功能扩展性强等特点.     

装备采购风险的BP神经网络评价模型

装备采购具有高风险的特点,科学、准确的风险评价对装备采购至关重要.传统的评价方法主观因素太强,而BP神经网络模型为装备采购风险评价开辟了新途径.首先介绍了人工神经网络模型的原理和BP算法,然后结合实例给出了用于装备采购风险评价的BP神经网络模型,采用该模型获得结果令人满意.     

移动代理的分布式系统监控模型研究与实现

提出将一种移动Agent与专家系统技术相结合的分布式网络故障诊断系统模型,该模型能够提高故障诊断效率,缩短系统响应时间,降低诊断时网络流量.提高诊断的智能性与容错性,克服了传统集中式故障诊断的诸多缺点.     

基于效果的网络中心战模型研究

基于效果的网络中心战是利用物理域内分布式传感器网络、指挥控制网络和武器平台网络.通过网络内部以及网络之间信息域中高质量的信息共享,实现军事人员认知域态势感知共享和部队自同步,是信息时代的一种新兴的作战样式,体现了"兵力分散,效果集中"的信息化战争的作战思想.通过分析基于效果的网络中心战,建立了基于效果的网络中心战体系结构模型和未来军事行动中实施该理论的任务进程模型,对我军未来信息化作战条件下联合作战的实施具有一定的指导意义.