数字余度飞行控制系统中的几个问题

本文讨论了数字余度飞行控制系统中的几个主要问题。根据随机过程假设提出了一种故障监控门限值的选取方法:余度管理软件的分时多速率结构,以及基于卡尔曼滤波器的解析余度方案。另外,本文还提出了伺服器均衡回路的独立性概念,从而解决了均衡回路的设计问题。     

双通道人-机控制系统中的驾驶员模型识别

针对驾驶员完成飞机纵向速度和俯仰角的跟踪任务,对双通道人-机系统中驾驶员描述函数的识别方法进行了系统的研究.通过驾驶员描述函数识别结果和线性相关性分析,提出了一种双通道控制时驾驶员描述函数识别的简化方法.将双通道人-机系统近似分解成两个单通道系统,并从理论和实验两方面验证了这种近似方法的可行性.该方法对双通道人-机控制系统中驾驶员模型建模技术以及用于人机系统的分析具有实用价值．     

无尾飞机布局方向控制特性研究

介绍了无尾飞机布局方向控制特性风洞试验研究的主要结果。在两种典型布局上研究了扰流板、开裂副翼、机头边条、活动和偏转翼梢及舵面的方向控制特性。认为机头边条、开裂副翼、活动(偏转)翼梢及其舵面组合是一种极具潜力的方向控制方案,可供无尾飞机布局参考。     

基于广义背景填充的塔式多相水平集的脑肿瘤分割算法

背景填充技术和Chan-Vese模型的塔式多相水平集算法能够在给定的图像中检测出多个目标,而背景填充技术中采用了固定的填充色彩,故该算法得到的边缘是不可调的.为此提出了一种采用可调填充色的广义背景填充技术,使得轮廓可调以便获得所需边缘.脑肿瘤分割实验表明,采用不同的填充色可以得到不同的脑肿瘤轮廓,有利于临床诊断.     

基于水平集的多相活动轮廓图像分割模型

提出了一种新型的多相活动轮廓模型，是无边活动轮廓模型的广义形式。该模型具有如下特点：（1）提出了背景填充技术，可以在检测目标内部弱边缘时去除阻碍检测的背景信息；（2）在单次二相水平集收敛的基础上，采用多次收敛方式实现了多相分割模型（n-1次收敛实现n相分割模型，n〉1）；（3）介绍了一种提升算法，进一步增强了模型的计算稳定性。实验结果表明，该模型对弱边缘检测特别有效。     

1.2m跨超声速风洞超扩段栅指控制系统

对 1 .2m跨、超声速风洞栅指M数控制系统作了较为详细的介绍 ,包括硬件系统的设计、硬件配置、功能选择和软件设计 ,以及本系统与风洞总压控制系统的连接和通讯 ,最后给出了 1 .2m跨、超声速风洞栅指M数控制系统的调试结果     

通用有限元NASTRAN中的阻尼计算问题

阻尼是影响动力学模型分析，尤其是动态响应分析的关键问题。在通用元有限元NASTRAN软件中，阻尼不仅有多处设置，而且与分析类型有关。文中分析了各阻尼设置与阻尼项的关系及物理机理及其注意的问题。最后给出了阻尼计算的实例。     

细长体大迎角绕流的滚转角特性

尖拱和钝拱细长体的截面侧向力分布呈现明显的滚转角效应。不同滚转角下尖拱侧向力沿轴向分布曲线(CC～x/D)的相位和半周期各不相同,截面侧向力随滚转角变化(CC～)呈现一定的无规律性;而钝拱CC～x/D曲线的相位和半周期基本相同,但振幅值大小依赖于滚转角,CC～分布呈完全的无规律性。尖拱模型头部的加工精度导致的微扰动使得尖拱CC～x/D分布变成型式基本一致的两簇曲线,其特征点和半周期分别对应相同,CC～分布呈现典型的双周期方波。     

缝隙耦合HT对特性的分析

分析了通过窄缝隙耦俣的矩形波导Ｈ面Ｔ形接头对（以下简称ＨＴ对）的电气特性，利用等效原理和切向场连续性建立积分方程组，采用矩量法化积分方程为代数方程进行数值求解，获得散射场和等效散射参数，以Ｘ波段标准矩形波导ＨＴ接头对为例进行具体求解和计算，以及实验验证，得到良好结果。     

Characterizing slope correction methods applied to satellite radar altimetry data: A case study around Dome Argus in East Antarctica

Slope correction is important to improve the accuracy of satellite radar elevation measurements by mitigating the slope-induced error (SE), especially over uneven ground surfaces. Although several slope correction methods have been proposed, guidance in the form of stepwise algorithm on how to implement these methods in processing radar altimetric data at the coding level, and the differences among these methods need to be presented and discussed systematically. In this paper, three existing types of slope correction methods—the direct method (DM), intermediate method (IM), and relocation method (RM, further divided into RM1 and RM2)—are described in detail. In addition, their main differences and features for various scientific applications are analyzed. We conduct a systematic experiment with CryoSat-2 Low Resolution Mode (LRM) data in a physically stable area around Dome Argus in East Antarctica, where in-situ measurements were available for comparison. The slope correction is implemented separately using the three methods, with the latest high-accuracy Reference Elevation Model of Antarctica (REMA) as the a-priori topography model. The bias and precision of the slope-corrected CryoSat-2 data results from the RM2 is ?0.18 ± 0.86 m based on the comparison with the field Global Navigation Satellite System (GNSS) data. The results from the RM2 indicate higher precision compared to those from the RM1. According to the correlation analysis of the slope-corrected CryoSat-2 data results (RM1 and RM2), the bias enlarges and the precision becomes worse when the surface slope increases from 0 to 0.85°. After a comprehensively comparative analysis, we find that the results from the RM1 and RM2 are superior in precision (0.93 m and 0.86 m) with respect to the GNSS data. The relatively low precision (1.22 m) from the IM is due to the potential error from the a-priori digital elevation model (DEM). The DM has the lowest precision (2.66 m). Another experiment over rough topography in West Antarctica is carried out for comparison, especially between the RM1 (precision of 15.27 m) and RM2 (precision of 16.25 m). In general, the RM is recommended for the SE elimination among the three methods. Moreover, the RM2 is firstly considered over smooth topography due to the superior performance in bias and precision, while the RM1 is more suggested over the rough topography because of the slightly smaller bias and better precision. The IM relies much on the accuracy of the a-prior DEM and is not usually recommended, because of the strict requirement in the sampling time between the radar altimetry data and the a-priori DEM to avoid any surface change over time.