A satellite schedulability prediction algorithm for EO SPS

With notably few exceptions, the existing satellite mission operations cannot provide the ability of schedulability prediction, including the latest satellite planning service (SPS) standard-Sensor Planning Service Interface Standard 2.0 Earth Observation Satellite Tasking Extension (EO SPS) approved by Open Geospatial Consortium (OGC). The requestor can do nothing but waiting for the results of time consuming batch scheduling. It is often too late to adjust the request when receiving scheduling failures. A supervised learning algorithm based on robust decision tree and bagging support vector machine (Bagging SVM) is proposed to solve the problem above. The Bagging SVM is applied to improve the accuracy of classification and robust decision tree is utilized to reduce the error mean and error variation. The simulations and analysis show that a prediction action can be accomplished in near real-time with high accuracy. This means the decision makers can maximize the probability of successful scheduling through changing request parameters or take action to accommodate the scheduling failures in time.     

Four-month Moon and Mars crew water utilization study conducted at the Flashline Mars Arctic Research Station,Devon Island,Nunavut

A categorized water usage study was undertaken at the Flashline Mars Arctic Research Station on Devon Island, Nunavut in the High Canadian Arctic. This study was conducted as part of a long duration four-month Mars mission simulation during the summer of 2007. The study determined that the crew of seven averaged 82.07 L/day over the expedition (standard deviation 22.58 L/day). The study also incorporated a Mars Time Study phase which determined that an average of 12.12 L/sol of water was required for each crewmember. Drinking, food preparation, hand/face, oral, dish wash, clothes wash, shower, shaving, cleaning, engineering, science, plant growth and medical water were each individually monitored throughout the detailed study phases. It was determined that implementing the monitoring program itself resulted in an approximate water savings of 1.5 L/day per crewmember. The seven person crew averaged 202 distinct water draws a day (standard deviation 34) with high water use periods focusing around meal times. No statistically significant correlation was established between total water use and EVA or exercise duration. Study results suggest that current crew water utilization estimates for long duration planetary surface stays are more than two times greater than that required.     

大长细比反射镜侧面支撑结构设计与分析

随着近几年离轴三反(Three-Mirror Anastigmat,TMA)相机在大视场高分辨率成像领域的应用,大长细比反射镜的支撑技术成为离轴TMA相机研制的关键技术之一。文章研究的反射镜尺寸达到986mm×246mm,为了使大长细比反射镜的支撑结构同时满足高刚度和高面形稳定的要求,采用理论研究和有限元分析相结合的方法设计了一种侧面柔性支撑结构。分析结果表明,反射镜组件一阶频率达到114.9Hz,在重力和2℃温变耦合下综合面形变化均方根值只有4.9nm,工艺件的力学振动试验结果和仿真分析基本吻合,反射镜支撑结构设计合理,满足空间应用要求。     

系统仿真及其在防空导弹武器系统研制中的应用

阐述了仿真、仿真模型和仿真系统的应用特点和关键技术，并以战术导弹为例探讨了系统仿真在武器系统研制、试验和降低成本中的重要作用；介绍了国外８０年代以来发展的分布交互式仿真（ＤＩＳ）系统，它们既包括虚拟仿真，也可将真实仿真和构造仿真接入仿真系统，使仿真的范围大为扩展。ＤＩＳ技术构建的人工合成环境，是一个多武器平台的网络式综合仿真系统，具备人机交互功能，可进行武器研究和大系统虚拟对抗演习等；最后，提出了研究和发展我国防空武器仿真系统的几点建议。     

军事后勤决策支持系统研究

对数据仓库、数据采掘、决策支持系统模型、可视化技术等方面内容进行了阐述,提出了决策支持库概念,产生了把数据仓库和决策支持库同时应用于军事后勤决策支持系统中的设想。     

陆基测控资源分配决策支持系统研究

简要地对我国航天陆基测控网的状况进行了介绍,分析了测控资源冲突的形成原因,表明了建立智能化测控资源分配决策支持系统的重要性。并在此基础上,提出了该系统的设计思想,系统结构、功能及其实现方法。该系统融合了丰富的测控资源分配和管理知识,采用定性推理和定量计算相结合的工作方式进行决策问题的求解,最终实现一个基于知识的决策系统,按照本文提出的系统构造,已经开发了有关分系统,而且在神州四号飞船和多颗卫星的测控任务中进行了应用,表明系统的构造是有效的。     

飞行控制模型管理系统

模型管理是决策支持系统的核心，将模型管理应用于飞行控制系统是现代试飞管理的一个重要手段，也是一个新的探索领域。本文从模型及模型库管理系统的概念出发，分析传统的及近年来进入市场的主流数据库技术，研究对飞控系统进行模型管理的可行性。首先提出对飞控模型利用对象关系数据库进行管理的思想并将模型对象化；其次是模型管理概念的提出及模型库的数据模型，模型库组织和存储；最后是飞控系统模型管理的实现。     

大厚度Ni_(75)B_(17)Si_8非晶合金的制备

 研究了净化Ni_(75)B_(17)Si_(8)合金液的方法以及净化对非晶形成能力的影响,通过净化工艺消除合金液内的异质质点可提高合金液的非晶形成能力;釆用净化和急冷相结合的工艺制备出了20×10×0.5(mm~3)的Ni-(75)B_(17)Si_(8)非晶合金试样。     

航空发动机承力系统试验建模技术研究

通过建立航空发动机整机振动分析模型,对试验与理论相结合的建模技术进行了研究,提出了试验建模的具体算法,并进行了精度分析。     

支持向量机增量学习方法及应用

基于支持向量机的增量学习作为一种数据挖掘与知识发现技术,已在目标识别,网页分类等诸多领城得到应用.在概述其机理的基础上,从如何提高学习精度与学习速度着手,分析了现有算法及其优缺点和需要改进的问题;论述了增量学习的应用现状,并提出进一步的研究方向.