Ku波段宽带平面阵的设计

设计和制作了一种Ku波段宽带微带圆环缝隙平面天线阵.整个天线阵分成9块独立设计,采用并联馈电网络,利用圆形一分九威尔金森功分器连接组成整个天线阵.这种结构的平面阵阻抗带宽达到了20.56%(VSWR＜2),增益达到34.5dB,满足卫星通信等领域的应用.     

流量预测下智能寻优门限值的AOS帧生成算法

针对AOS空间通信系统的自适应帧生成算法帧生成时间门限值需要人工设置问题,提出一种流量预测下智能寻优门限值的AOS帧生成算法。该算法使用小波神经网络对ON/OFF模型产生的自相似流量进行预测,根据预测流量计算复用效率和平均包时延,使用人工鱼群算法对建立的综合评价函数进行智能寻优,从而得到帧生成算法的最佳门限值。该算法可以随自相似流量的变化自适应地确定门限值,在保证高效的复用效率的基础上,大大降低了系统的时延,从整体上提高了系统性能。     

混合法(权函数─—光测技术)在断裂力学中的应用研究

本文采用作者提出的混合法（将光测技术与权函数相结合）,求解了含孔板在不同应力场裂纹的应力强度因子。     

内重力波传播的3维传输函数模式研究

在考虑背景风场及大气耗散的条件下,建立了3维内重力波传输函数数值模式.分析了300 km高度3维传输函数在频率波数域的特性,并以近地面单位脉冲点源为激发源,得到了内重力波在3维空间中的时空分布.讨论了不同时空尺度地面方波源激发的内重力波在电离层高度的能量分布特征.结果表明,(1)对内重力波而言,背景大气相当于一个带通滤波器,只有波动周期和波长分别在15～30 min和200～400 km之间的重力波扰动最容易上传到300km高度;(2)在背景风场的作用下水平面上以同心圆扩散的波阵面以及垂直方向上成漏斗状的波阵面发生了变形,并且逆风方向比顺风方向更有利于声重力波由对流层向电离层高度传播;(3)300km高度对时间尺度和空间尺度分别在20～30 min和150～250 km之间的地面方波源响应的总能量最强.      

基于小生境遗传自适应RBFN的歼击机故障认定方法

在模式识别领域中,如何实现更高精度的分类一直是个核心问题。本文提出了将自适应RBF神经网络与小生境遗传算法相结合的方法,其中自适应RBF神经网络通过对样本判断,自动实现对RBF网络添加新的隐层节点或者将样本归于已存在的隐层节点所属的类;小生境遗传算法用于寻找最优的网络宽度值。两者相结合最后确定一个隐层节点数与类别数相同的俭省的网络。用歼击机故障数据进行仿真,比较结果表明此方法能实现更高精度的故障认定。     

采用PQLF的不确定模糊系统最优保性能控制

对一类模有界的参数不确定T-S模糊系统的保性能控制问题进行了分析研究。一个二次指标函数表征了闭环系统的性能。采用分段二次Lyapunov函数(Piecewise quadratic Lyapunov function,PQLF)方法,在使闭环系统渐近稳定的前提下以线性矩阵不等式(Linear matrix inequality,LMI)的形式给出了一种鲁棒最优保性能控制律。同时提出了一种新型分段并行补偿(Parallel distributed compensation,PDC)控制策略。该PDC控制器同样反映了模糊系统前提变量的规则结构信息。数值仿真显示,采用该设计方法所得到的闭环保性能值优于传统二次Lyapunov函数(Common quadratic Lyapunov function,CQLF)方法下的闭环保性能值,且系统动态性能良好,鲁棒性强。     

基于图像特征分类和RBF网络的两轴车辆动态称重技术

汽车在行驶过程中的总重信号通常由各轴重信号确定,各轴重信号的测试精度取决于对车辆运动参数和振动信号等的精确分析。本文采用径向基函数(Radial basis function,RBF)网络处理轴重信号,针对该网络的泛化能力与拟合精度的矛盾,将车辆按照重量分为大、中、小3种类型,并进行整车建模和网络训练;实际测试过程中,利用汽车俯视图像提取类型特征,然后根据汽车的类型将测试参数输入不同的神经网络进行处理,以静态测量结果为相对真值。仿真结果表明,分类建模比单一混合建模具有更高的测试精度。     

A variational Bayesian-based robust adaptive filtering for precise point positioning using undifferenced and uncombined observations

In the application of precise point positioning (PPP), especially in the dynamic mode, the classical Kalman filter (KF) usually produces a large number of estimation errors or diverges when there are gross errors in the observation data or unexpected turbulences occur in target motion state or both of them. For such problem, a variational Bayesian (VB)-based robust adaptive Kalman filtering (VB-RAKF) is proposed in this paper. This filter introduces a classification robust equivalent weight function to resist observation gross error and the inverse Wishart prior to model inaccurate process noise covariance matrix (PNCM). To improve the instantaneous accuracy of state estimation, the VB approach is used to obtain better estimations of inaccurate PNCM. Several sets of observation data collected by IGS reference stations and vehicles are employed to check the robustness and positioning accuracy of the VB-RAKF model. The results show that the VB-RAKF algorithm is more robust than the KF, and can effectively resist the gross error in observation data and control state disturbance. In the IGS reference station tests, when compared to the KF, the static positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 13%, 8% and 22%, respectively, and the simulated dynamic positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 19%, 9% and 21%, respectively. The in-vehicle dynamic test verifies that the VB-RAKF outperforms the KF, and shows that the VB-RAKF has better performance than the KF when dealing with observation data which has obvious gross errors, and similar performance as the KF when gross errors are small.     

气流速度振荡场中幂律液膜不稳定性分析

采用线性稳定性分析研究了处于气流速度振荡场中幂律液膜时间模式的不稳定性。振荡的气流速度导致动量方程为含有时间周期系数的希尔方程，采用Floquet理论进行求解。详细研究了不同振荡幅值和振荡频率下表观雷诺数、幂律指数及无量纲速度因子对各不稳定区间的影响。结果表明：振荡幅值的增加或振荡频率的减小会使液膜不稳定区域的个数增加，且Kelvin-Helmholtz（K-H）不稳定区域的最大增长率、主导波数和截止波数随振荡幅值和振荡频率的增加而增加；表观雷诺数、幂律指数和无量纲速度因子的增加增强了K-H不稳定区域内的不稳定性，使参数不稳定区域内的增长率先减小后增加；振荡幅值的变化不改变最大增长率发生转折时对应的流变参数，而当振荡频率较小时，幂律指数和无量纲速度因子的增加却使最大增长率单调增加。     

Reliability estimation of rotary lip seal in aircraft utility system based on time-varying dependence degradation model and its experimental validation

With several attractive properties, rotary lip seals are widely used in aircraft utility system, and their reliability estimation has drawn more and more attention. This work proposes a reliability estimation approach based on time-varying dependence analysis. The dependence between the two performance indicators of rotary lip seals, namely leakage rate and friction torque, is modeled by time-varying copula function with polynomial to denote the time-varying parameters, and an efficient copula selection approach is utilized to select the optimal copula function. Parameter estimation is carried out based on a Bayesian method and the reliability during the whole lifetime is calculated based on a Monte Carlo method. Degradation test for rotary lip seal is conducted and the proposed model is validated by test data. The optimal copula function and optimal order of polynomial are determined based on test data. Results show that this model is effective in estimating the reliability of rotary lip seals and can achieve a better goodness of fit.