星载高速无线数据网络协议的设计

星载无线数据网络可以解除航天器内错综复杂的线缆网的束缚,是航天器轻小型化的重要技术储备。旨在替代航天器内现有的点对点三线制的低电压差分信号线缆,提出了一种基于脉冲超宽带(IRUWB)技术的星载高速无线数据网络的设计方案,并重点介绍了高速无线网络协议的设计和实现。网络协议设计参考了美国军用数据总线标准MIL-STD-1553B协议,采用时分制指令响应机制,按物理层、链路层、网络层和应用层进行了详细说明,以适应星载高速率无线数据传输的要求,具有灵活性高、可靠性高、扩展性高的特点。网络协议的IP核经过地面演示系统进行验证,实验测得应用层数据传输的误码率小于10-9。     

On the uncapacitated K‐commodity network design problem with zero flow‐costs

Extending Sastry's result on the uncapacitated two‐commodity network design problem, we completely characterize the optimal solution of the uncapacitated K‐commodity network design problem with zero flow costs for the case when K = 3. By solving a set of shortest‐path problems on related graphs, we show that the optimal solutions can be found in O(n³) time when K = 3, where n is the number of nodes in the network. The algorithm depends on identifying a list of “basic patterns”; the number of basic patterns grows exponentially with K. We also show that the uncapacitated K‐commodity network design problem can be solved in O(n³) time for general K if K is fixed; otherwise, the time for solving the problem is exponential. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

改进的前向信息修补算法及其应用

前向信息修补算法可以对离散动态贝叶斯网络的缺失数据进行预测,该算法只适用于所有观测节点是相互独立的网络,却不能处理观测节点有依赖关系网络的缺失数据。针对该算法的这一缺陷,提出了改进的前向信息修补算法,在分析离散动态贝叶斯网络的缺失数据具有二种基本形式的基础上,推导出了每种形式的相应预测公式。继而构建了用于识别威胁源离散动态贝叶斯网络的模型。仿真实验验证了改进的前向信息修补算法的有效性。     

地面防空武器系统的效能评估模型

作战系统服务能力是衡量其作战效能的重要指标,结合现代作战系统的特点,运用排队论原理,建立了一种地面防空武器系统效能评估模型,确定了武器系统作为随机服务系统的主要性能指标,包括目标突防概率、目标杀伤概率、占用射击通道的平均数、射击通道占用概率和武器系统效率等。示例计算分析显示,该模型与外部条件和地面防空武器系统的综合特性密切相关,对辅助指挥员决策有一定的参考价值。     

水面舰艇对海作战可靠性的新型评价方法

针对水面舰艇对海作战网络的特点,基于复杂网络"反社区"性质,将对海作战网络描述成"反社区"有向赋权图。结合传统可靠性分析方法,提出了一种新的可靠性评价指标,进一步扩展了传统可靠性指标的4个特性,并通过实例仿真,证明了该指标的有效性。该方法将传统可靠性理论与复杂网络理论有效地结合起来,将进一步扩大复杂网络的适用范围。     

多波束卫星网络的物理层安全性能指标研究

安全性是卫星通信网络充分发挥其优势的必要保证。将物理层安全传输技术引入多波束卫星通信网络,可望有效提高卫星通信系统的抗截获和抗侦测能力。分析了卫星通信的物理层安全应用需求,设计了一套波束成形和功率控制相结合的多波束卫星下行链路物理层安全传输策略,建立了信道模型和信号模型,构建了一套多波束卫星网络下行链路的物理层安全性能评价指标,并对指标进行了定性和定量的分析。     

The building evacuation problem with shared information

In this article, the Building Evacuation Problem with Shared Information (BEPSI) is formulated as a mixed integer linear program, where the objective is to determine the set of routes along which to send evacuees (supply) from multiple locations throughout a building (sources) to the exits (sinks) such that the total time until all evacuees reach the exits is minimized. The formulation explicitly incorporates the constraints of shared information in providing online instructions to evacuees, ensuring that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. Arc travel time and capacity, as well as supply at the nodes, are permitted to vary with time and capacity is assumed to be recaptured over time. The BEPSI is shown to be NP‐hard. An exact technique based on Benders decomposition is proposed for its solution. Computational results from numerical experiments on a real‐world network representing a four‐story building are given. Results of experiments employing Benders cuts generated in solving a given problem instance as initial cuts in addressing an updated problem instance are also provided. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

基于排队论的反舰导弹突防概率研究

根据水面舰艇对海导弹攻击中作战指挥的需要,提出了基于排队论中随机服务理论的反舰导弹突防概率解算模型.综合研究了目标舰艇编队采用各种手段进行抗击帮助条件下,反舰导弹突防概率的计算问题,并且结合想定装备参数进行了算例分析.为指挥员实施导弹攻击提供辅助决策和量化支持.     

基于神经网络和功率谱的水中目标信号检测方法

将功率谱和神经网络相结合,应用于高海况、低信噪比条件下,水中目标信号的特征提取中.文中首先对信号进行功率谱估计,利用目标信号功率主要集中在低频部分的特点,提取低频信号的能量作为特征,然后利用人工神经网络对目标信号进行检测.利用不同浪级情况下海洋水压场的仿真信号数据,对某型目标舰船的水压信号进行了检测计算,验证了该方法的有效性,尤其是达到了在高海况、低信噪比条件下,对目标信号检测率比较高、虚警率比较低的效果.     

Non‐greedy heuristics and augmented neural networks for the open‐shop scheduling problem

In this paper we propose some non‐greedy heuristics and develop an Augmented‐Neural‐Network (AugNN) formulation for solving the classical open‐shop scheduling problem (OSSP). AugNN is a neural network based meta‐heuristic approach that allows integration of domain‐specific knowledge. The OSSP is framed as a neural network with multiple layers of jobs and machines. Input, output and activation functions are designed to enforce the problem constraints and embed known heuristics to generate a good feasible solution fast. Suitable learning strategies are applied to obtain better neighborhood solutions iteratively. The new heuristics and the AugNN formulation are tested on several benchmark problem instances in the literature and on some new problem instances generated in this study. The results are very competitive with other meta‐heuristic approaches, both in terms of solution quality and computational times. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.