构造极小广播网络的新方法

广播是指从网络中某一成员开始 ,将消息传递给网络中其余成员的过程 .极小广播网络就是指能在最短时间内广播一条消息的通讯网络 .本文主要讨论一种构造极小广播网络的新方法 ,这种方法在大多数情况下改进了极小广播网络的最少边数的上界.     

Fixed-Orderl1-Control for Systems with Time-Delay

1. INTRODUCTIONPhysical plaaes are inevitably affected by exogenous disturbance, hence rejecting disturbanceis important. Ffor example, HOO-control systems can reject the LZ disturbance. But in factthere are maily species of disturbances: some are LZ-attenuation; some are stochastic; andsome are Loo(loo) [6]. If the exogenous disturbance is modeled as the energy-bounded signal, and the performance is the output energyt that is Hoo problem. If the performance isoutput energy, that is p…     

公路军事交通运输勤务综合演习项目GERT网络模型研究与分析

运用随机网络理论 ,构建了在现代高技术战争条件下 ,“红、兰”军双方激烈对抗时 ,公路军事交通运输勤务系统运行过程的 GERT网络模型 ,并举例求出了其解析解 .在此基础上 ,提出了演习 (或战争 )时 ,公路军事交通运输保障的“失败”概率与“转移”(战术、战役或战略转移 )概率的概念 ,并对其因素构成及其相互关系进行了剖析 .     

基于前视声呐信息的AUV避碰规划研究

在复杂海洋环境中,利用前视声呐获取的障碍物信息指导自治水下机器人（AUV）进行局部避碰。主要采用强化学习的方法对AUV进行控制和决策,综合Q学习算法、BP神经网络和人工势场法对AUV进行避碰规划。强化学习的方法强调AUV在环境的影响中学习,通过环境对不同行为的评价性反馈信号来改变行为选择策略。并且在环境发生变化时,AUV通过学习来实现对新环境的适应,不断改进其自治能力,进而实现在不确定环境下的避障任务。开发了AUV运动规划的虚拟仿真软件系统,仿真实验证明了算法的合理性与可行性。     

结合非线性CS算法的UWB-SAR运动补偿

载机飞行不稳对SAR成像质量影响很大,对超宽带SAR系统尤其如此,提出了一种结合非线性CS算法的超宽带SAR运动补偿方法。通过采用引入高次相位补偿和非正交旁瓣抑制的改进非线性CS算法,减小了超宽带SAR成像中的相位误差。通过成像前的常规一阶运动补偿和距离徙动校正后的分块二阶运动补偿,有效补偿了超宽带SAR中的方位空变运动误差,提高了飞行不稳时的成像质量。该方法在运动补偿时考虑了方位空变运动误差随距离的变化,针对载机运动误差变化较快的情况也给出了处理办法。仿真和实测数据的处理结果验证了该方法的有效性。     

贝叶斯方法在代码转换器可靠性验证试验中的应用

针对高可靠性产品的可靠性验证试验时间长、费用高等特点,将贝叶斯方法引用到可靠性验证试验中。推导出服从指数分布的贝叶斯可靠性序贯试验方法。将贝叶斯方法与经典序贯试验方法进行比较,贝叶斯方法较大的减少了试验量和试验时间。在某型飞机代码转换器的国产化可靠性验证试验中应用该方法,实际应用结果验证了该方法的有效性。     

一种基于星型子网的片上网络结构研究

提出了一种基于星型子网的片上网络结构SSBM NoC（Star-Subnet-Based-MeshNoC）,它在已有网格状结构的基础上引入了子网的概念,并在子网内使用星型拓扑结构来连接一个交换机与四个网络节点。OPNET仿真结果表明,在同等网络规模下SSBM结构较之单纯的二维网格状结构具有更少的端到端通信延时,并仅使用其1/4数目的交换机,而交换机的负载则不到其两倍。     

属性添加情况下的概念格重构算法

研究在给定的形式背景下,通过逐步添加属性建造概念格的方法.一般地,初始概念格可由部分属性生成.当添加属性时,得到了新节点与它在原概念格中的直接父节点是一一对应的结论.证明了所有新节点在原概念格中的直接父节点,只可能在某个节点和它的子节点中.基于上述性质,给出了添加属性情况下的重建概念格算法.此算法的优点是生成新节点的范围小,构建概念格的速度快.     

Ad hoc distributed mutual exclusion algorithm based on token-asking

The solution of distributed mutual exclusion is difficult in Ad hoc networks owing to dynamic topologies and mobility. Based on the analysis of the properties of Ad hoc networks and the disadvantages of the traditional algorithms, an improved Ad hoc system model was given and a novel algorithm was presented as AHDME (Ad Hoc Distributed Mutual Exclusion); it was based on the token-asking algorithms. It utilized broadcast to search for the token and to decrease the message complexity of multi-hop Ad hoc networks. Lamport's timestamp was improved to ensure the time sequence and to prevent nodes from starvation. When compared to traditional algorithms, AHDME does not require the fixed size of request queues and the global system information, which adapts itself to the frequent arrival/departures and the limited computing capability of nodes in Ad hoc networks. Performance analysis and simulation results show that the AHDME algorithm has low message complexity, small space complexity, and short response delay.     

The self-organizing worm algorithm

A new multi-modal optimization algorithm called the self-organizing worm algorithm (SOWA) is presented for optimization of multi-modal functions.The main idea of this algorithm can be described as followsdisperse some worms equably in the domain;the worms exchange the information each other and creep toward the nearest high point;at last they will stop on the nearest high point.All peaks of multi-modal function can be found rapidly through studying and chasing among the worms.In contrast with the classical multi-modal optimization algorithms,SOWA is provided with a simple calculation,strong convergence,high precision,and does not need any prior knowledge.Several simulation experiments for SOWA are performed,and the complexity of SOWA is analyzed amply.The results show that SOWA is very effective in optimization of multi-modal functions.