衰减记忆自适应滤波在惯导系统传递对准中的应用

针对滤波稳定性问题,提出了一种改进的衰减记忆自适应滤波算法。通过引入衰减记忆滤波矩阵,根据残差序列输出的互不相关性,在线自适应地调整衰减因子,从而使衰减记忆滤波工作在最佳状态。将该算法应用于惯导系统的传递对准过程,仿真结果表明在模型和噪声统计特性的先验信息不准确时,该算法优于传统的卡尔曼滤波。     

装甲战车训练模拟器综合显示终端仿真系统设计与实现

采用模拟器进行数字化武器装备的操作训练是当今信息化装备发展的需要,综合显示终端仿真系统是装甲战车训练模拟器中重要的组成部分.基于实装通过在PC机上VAPS平台和VC 开发环境相结合来构建仿真系统,可以降低设计成本,缩短设计周期.介绍了仿真系统的结构、设计思想和实现方法,给出了系统组成框图和开发流程,重点研究了仿真界面显示和外部驱动程序等关键问题,实现了某型装甲战车训练模拟器综合显示终端仿真系统,满足了装甲车辆综合显示终端信息化训练的需要,具有较高的军事和经济价值.     

冗余系统费用模型及优化

根据冗余设计的不同形式 ,在Tillman并联系统费用模型的基础上引入了系统功能指标 ,并将其推广至其它形式的冗余系统 ,从而得到了一个较为完整的冗余系统费用模型。在此基础上 ,建立了用于优化并串联冗余系统设计的非线性混合整数规划模型 ,分别使用了模式搜索与启发式算法的组合算法以及遗传算法对其进行求解 ,着重讨论了两种方法的实现关键及各自的优劣。最后 ,经计算比较 ,提出了遗传算法与模式搜索、启发式算法相结合的求解方法 ,并说明了该方法的有效性。     

群体决策中不精确结论综合模型的研究

采用关系化简、等价类划分和排序选择等步骤对群体决策中的不精确结论进行综合,可以有效地处理群体决策中的不精确结论综合问题,提高群体决策系统的性能.     

Spectral Properties and Positivity of Flows in Infinite Networks with Dynamic Boundary Control Nodes

In this paper we study transport processes on infinite networks with dynamic boundary control nodes. These flows can be modeled by operator semigroups on a suitable Banach space. Using functional analytical and graph theoretical methods, we investigate its spectral properties of the system and positivity of the semigroup under appropriate assumptions on the network.     

基于观测器的飞机除冰液温度鲁棒控制方法研究

对飞机除冰液温升模型的时变和延时特性进行了分析研究.采用Pade逼近对飞机除冰液加热系统的纯时延特性进行近似,将系统的时延问题转化为非时延问题进行处理.为克服由于飞机除冰作业过程中流量变化所造成的系统模型参数变化,采用基于滑模变结构的鲁棒控制方法对系统模型参数摄动进行补偿.为解决系统状态不可测问题,消除Pade逼近误差对出口温度的影响,采用状态观测器对系统的状态进行估计,并对Pade逼近模型输出误差进行补偿.利用系统不同流量下的试验温升曲线模型对设计的控制策略进行了仿真研究,仿真结果验证了所提出算法的有效性.     

异构网络中基于兴趣感知的聚类缓存策略

为应对移动流量的爆炸式增长,在异构网络中引入缓存成为目前的热点研究方向之一。目前缓存部署策略存在缓存命中率不高、缓存数据冗余度较高等问题,没有充分利用存储资源,直接影响网络的性能。针对以上问题,提出一种基于用户兴趣感知的虚拟聚类协作缓存部署策略来提高缓存系统性能。根据用户距离构造虚拟聚类,依据内容请求因子确定系统缓存内容,采用基站协作缓存以及用户间虚拟聚类方式共享缓存内容,以缓存命中率最大化为目标建立优化模型并通过贪婪算法求解。理论分析及仿真结果表明,在异构网络场景下,跟现有方法相比,所提方法在缓存命中率、系统平均传输时延等性能上均有明显提升。     

基于广义随机Petri网的城市道路公交优先建模与性能研究

针对当前城市道路网存在的交通拥堵问题,根据城市地面交通的特点和公交优先的原则,利用广义随机Petri网通过将变迁分为时间变迁和瞬时变迁,结合禁止弧和赋予各弧不同的优先权, 构建公交优先策略模型,为实施公交优先策略提供理论依据.     

White noise estimation for discrete-time systems with random delay and packet dropout

This paper is concerned with the optimal and suboptimal deconvolution problems for discrete-time systems with random delayed observations. When the random delay is known online, i.e., time stamped, the random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique, and then an optimal input white noise estimator is presented based on the stochastic Kahnan filtering theory. However, tb_e optimal white-noise estimator is timevarying, stochastic, and doesn＇t converge to a steady state in general. Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criteria. The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator. It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.     

Risk reduction in a project portfolio

The positive impacts of managing projects as a portfolio are quantified by comparing the value of the integrated risk of a project portfolio and the aggregation of single project risks implemented separately. Firstly, the integrated risk is defined by proposing risky events based on set theory. Secondly, as projects interact with each other in a project portfolio, the integrated risk is evaluated by using a Bayesian network structure learning algorithm to construct an interdependent network of risks. Finally, the integrated risk of a practical case is assessed using this method, and the results show that the proposed method is an effective tool for calculating the extent of risk reduction of implementing a project portfolio and identifying the most risky project, so as to assist companies in making comprehensive decisions in the phase of portfolio selection and portfolio controlling.