基于光谱信息散度的光谱解混算法

光谱解混是高光谱遥感定量化的关键,提出了一种基于光谱信息散度和光谱混合分析的光谱解混改进算法(SID-SMA,Spectral Information Divergence-Spectral Mixed Analysis).以光谱信息散度判定最优端元子集,端元选择时采用端元的初选和二次选择来提高端元选择的精度,得到较小的丰度估计误差.通过光谱库模拟数据的结果可以看出,SID-SMA的端元选择精度和丰度估计精度要优于其他算法,当信噪比为100∶ 1时,算法端元选择正确率达到了99.8%,29个端元的丰度估计总误差小于0.1,并且算法的速度较快.     

网状BOM构造方法的分析与研究

目的提出了一种实用性较强的网状物料清单的构造方法。方法分析两种常用物料清单构造方法的优缺点,并且给出了网状物料清单在对象关系数据库中的实现方法。结果网状物料清单的构造方法适合于种类繁多且结构复杂的产品。结论网状物料清单在ERP系统的设计和开发中有着非常重要的意义。     

Satellite selection methods for multi-constellation advanced RAIM

The increased number of potential threat modes under multi-constellation advanced receiver autonomy integrity monitoring (ARAIM) requires an increase in the number of subsets and a correspondingly high computational load. A new satellite selection method based on integrity support message (ISM) parameters is proposed and compared with GDOP-based selection. The performance was tested on five days of data measurements from 21 multi-global navigation satellite system experiment (MGEX) stations distributed around the world, as well as simulation using the broadcast ephemeris. The results show that the proposed ISM-based satellite selection method is highly compatible with the baseline ARAIM. This method could reduce the computational times by about 60–70% quickly, with minimising vertical protection level (VPL) loss, which was consistently within 1 m, even a reduced VPL value in some epochs, and resulting in an improved availability. The simulation results were similar to the MGEX data. It appears that the application of ISM-based satellite selection can effectively reduce computational burden with a minimal impact on availability.     

关于加工中心选型订货的几个问题

阐述了选型、订货加工中心的基础知识，订货原则，选型订货方法及工作步骤。     

SMP2标准的原始数据生成模型框架分析

原始数据生成(RDG)是导航系统设计验证的重要组成部分。由其生成的数据可进行导航系统链路预算和误差预算,也可用来对完好性处理工具(IPF)和轨道与同步处理工具(OS-PF)的算法进行调整和确认。然而原始数据生成涉及到如天文地理、物理学等多学科领域的模型,不同模型之间的接口关系十分复杂,使得将这些模型集成并实现仿真验证功能变得十分困难,也难以有效实现仿真模型的可重用性和可移植性。基于SMP2标准的仿真系统设计开发方法能有效地解决上述问题,文章介绍了原始数据生成的概念,并在面向对象分析的基础上,提出了基于SMP2规范的模型集成开发的方法和步骤。     

一种新的导航星选取算法研究

在导航星表的建立过程中，由于恒星的数量太多，往往要进行筛选，通常这种选择复杂费时，而结果往往并不是最优的。本文引入了动态星等阈值分布函数，将传统星等阈值过滤算法中的静态阈值用动态星等阈值代替，建立了一种新的动态星等阈值过滤选择模式。而基于统计学习理论的支持向量机方法为求解高维非线性动态星等阈值分布函数提供了新的途径。本文讨论了这种基于支持向量机的导航星自动选择算法——回归选取算法。实验表明，用该算法所选取的导航星表，导航星数量少、分布均匀性好。同时它还能适应多种任务的导航星选取要求，具有很强的通用性。     

高效图像资源管理器的设计与实现

针对数据存储量大引起的访问广域网服务器速度慢、不能对访问的客户进行细粒度的权限控制问题,研究了B/S模式下基于.NET平台的图像资源管理器的设计和实现技术。系统综合采用权限管理模型、动态加载、文档对象模型（DOM）等技术以及非点击无刷新伸缩控制方法,实现了图像资源管理器的分权限控制、高速访问以及无刷新高效联动等操作。实验验证了方法的可行性。     

挠性杆的镗加工技术

针对挠性杆的结构特点和加工难点,提出了有效的工艺措施,从装夹定位、刀具材料与几何参数选择、刃磨方法、切削用量的选择,以及挠性杆内应力的分析等方面详细地阐述了加工过程中的关键技术,解决了挠性杆镗削加工的技术难题。     

Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas

Urban land cover information extraction is a hot topic within urban studies. Heterogeneous spectra of high resolution imagery—caused by the inner complexity of urban areas—make it difficult. In this paper a hierarchical object oriented classification method over an urban area is presented. Combining QuickBird imagery and light detection and ranging (LIDAR) data, nine kinds of land cover objects were extracted. The Spectral Shape Index (SSI) method is used to distinguish water and shadow from black body mask, with 100% classification accuracy for water and 95.56% for shadow. Vegetation was extracted by using a Normalized Difference Vegetation Index (NDVI) image at first, and then a more accurate classification result of shrub and grassland is obtained by integrating the height information from LIDAR data. The classification accuracy of shrub was improved from 85.25% to 92.09% and from 82.86% to 97.06% for grassland. More granularity of this classification can be obtained by using this method. High buildings and low buildings can, for example, be distinguished from the original building class. Road class can also be further classified into roads and crossroads. The comparison of the classification accuracy between this method and the traditional pixel-based method indicates that the total accuracy is improved from 69.12% to 89.40%.     

一种载人航天概率风险评价数据选用与分析方法

概率风险评价(PRA)方法是一项成熟的量化风险评估技术,而可靠性相关数据不足,制约着该方法在国内载人航天领域的应用。针对这一问题,结合NASA的PRA工作经验,提出了一种适合国内载人航天PRA数据选用与分析的方法,综合运用产品的通用数据与特定数据,采用贝叶斯等数据融合方法,对产品进行量化评价。通过航天典型产品案例,与经典评估方法进行对比分析,结果表明,此方法更能反映产品的失效特性。文章提出的方法,可用于载人航天等具有小子样特征的产品PRA数据的选用与分析,为我国载人航天开展PRA工作提供参考。