使用困难报告安全风险状态监控方法研究

基于使用困难报告(SDR)数据,参照相关标准给出了五类SDR事件严重度分类方法,提出了SDR事件的严重度度量方法、事件的发生概率计算方法和事件风险值的计算方法,在此基础上建立了SDR风险警戒值计算方法和四类SDR风险状态的确定方法,最后通过实例对此进行了验证。     

基于服务的高性能多学科优化计算在飞机设计中的应用

基于服务构架(SOA)的高性能多学科优化计算在飞机设计的应用中,能够解决超大规模设计变量和资源不足带来的一系列问题.本文介绍了先进的优化计算方法和基于高性能计算平台的解决途径及优势,并以BRIDGE项目为例介绍了该平台的具体应用.这一研究工作得到了国家"863"计划项目的支持.     

长波授时附加二次相位因子修正方法

附加二次相位因子（ASF）是影响长波授时精度的主要因素,而其理论计算复杂、实测成本太高,不便于工程推广。本文选取华北平原区域为研究范围,提出一种简单高效的ASF修正方法———建立对应的ASF数据网格,采用插值算法进行ASF修正研究。通过对ASF数据网格的验证和分析,得到了和预期效果相一致的实验结果。实验结果表明：建立区域性的ASF数据网格有利于快速地修正ASF误差,便于实际工程应用。     

以体验提升航空服务质量的策略研究

航空服务若想在激烈的市场竞争中取胜,将体验融于服务,开发设计出令人难忘的客户体验,无疑将对提升服务质量,保持客户忠诚度起到关键作用。本文从体验与航空服务的互动关系出发,分析了体验在航空服务中的重要作用,探讨了航空服务中体验设计的方法,帮助探寻民航企业体验设计中的重要依托要素。     

浅谈建设监理行业服务的观念创新

对我国目前工程建设监理存在的主要问题进行了分析，阐述了我国监理行业服务观念创新的内容，对解决建设监理行业存在的问题所起的重要作用。     

空防对抗仿真的情景想定设计

为使空防对抗仿真更接近实战，对仿真情景想定设计作了改进。将防空区等分成4个扇形区，在确定对突防飞机威胁最大的影响因素的基础上，根据动态规划法得出进袭飞机采用不同航线时防空方不同部署形式的赢得矩阵，并由布朗算法计算该矩阵，得出攻防双方作战方案的选取概率。仿真计算结果表明，该设计方法较合理，避免了仿真的盲目性，对战术仿真有一定的参考价值。     

气流展纤工艺中不同压差和送丝速度对碳纤维展宽的影响

为探究气流展纤工艺中不同出口压差和送丝速度对碳纤维丝束展宽效果的影响,依据Venturi原理,搭建气流展纤试验平台,并记录大丝束碳纤维展宽过程,计算不同工艺参数下碳纤维丝束在不同阶段的展宽率和展宽均匀程度。研究结果表明：通过搭建的气流展纤试验台,能获得展宽率高于400%,展宽均匀程度在90%以上的碳纤维丝束,得到的碳纤维丝束的展宽率与出口压差呈正相关,与送丝速度呈负相关;碳纤维丝束的展宽均匀程度与出口压差呈负相关,与送丝速度呈正相关。     

构建农村公共文化服务体系的现状及其对策研究

加快农村公共文化服务体系建设，对为农民群众提供更多更好的精神食粮，构建和谐社会具有重大意义。为此，应牢固树立三大意识，加快构建农村公共文化服务体系。     

Carbon monoxide spatial gradients over source regions as observed by SCIAMACHY: A case study for the United Kingdom

Carbon monoxide (CO) is an important air pollutant whose emissions and atmospheric concentrations need to be monitored. The measurements of the SCIAMACHY instrument on ENVISAT are sensitive to CO concentration changes at all atmospheric altitude levels including the boundary layer. The SCIAMACHY CO measurements therefore contain information on CO emissions. Until now no studies have been published where the SCIAMACHY CO measurements have been used to quantify CO emissions by applying, for example, inverse modelling approaches. Here we report about a step in this direction. We have analysed three years of CO columns to investigate if spatial gradients resulting from United Kingdom (UK) CO emissions can be observed from space. The UK is an interesting target area because the UK is a relatively well isolated CO source region. On the other hand, the UK is not the easiest target as its emissions are only moderate and because the surrounding water has low reflectivity in the 2.3 μm spectral region used for CO retrieval. We determined horizontal CO gradients from seasonally and yearly averaged CO during 2003–2005 over the UK taking into account daily wind fields. We show that the measured CO longitudinal (downwind) gradients have the expected order of magnitude. The estimated 2σ error of the gradients depends on time period and applied filtering criteria (e.g., land only, cloud free) and is typically 10–20% of the total column. The gradients are barely statistically significant within the 2σ error margin. This is mainly because of the relatively high noise of the SCIAMACHY CO measurements in combination with a quite low number of measurements (∼100) mainly due to cloud cover.     

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.