基于神经网络模型的层次诊断方法及应用

对神经网络和故障诊断的层次分解模型进行了研究,把神经网络技术和层次分解模型相结合,利用神经网络来实现分层诊断。以卫星能源系统为背景,建立了该系统故障模拟试验台的层次分解模型,在此基础上构造了合适的分层神经网络,通过分层神经网络诊断系统故障,给出的两个实例说明了这种方法的有效性。     

基于分段FOX方法的民航LTO黑碳质量排放清单计算分析

根据民用航空发动机压比提出分段FOX(formation and oxidation)方法,估算民用航空发动机LTO(landing and takeoff)循环黑碳(BC)颗粒质量排放指数,结合航空发动机机场实际运行时间和燃油流量修正,编制了2018年国内某国际机场某周国内民航航班黑碳颗粒质量排放清单。结果表明:考虑燃油流量修正的分段FOX方法对民用航空发动机慢车、进近等低工况的黑碳颗粒质量估算精度较FOX方法提高1~2倍,爬升、起飞等高工况估算值更接近实验值。考虑民航实际运行时间和燃油流量后,2018年此国际机场国内民航进出港LTO循环黑碳颗粒质量周排放量约300 kg,其中B737、A320等中型机离港(起飞和爬升阶段为主)阶段排放贡献超70%。      

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from “normal” (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions.     

Selection of classification techniques for land use/land cover change investigation

The concerns over land use/land cover (LULC) change have emerged on the global stage due to the realisation that changes occurring on the land surface also influence climate, ecosystem and its services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them. The classifiers algorithms are well optimized for the gamma, penalty, degree of polynomial in case of SVM, while for ANN minimum output activation threshold and RMSE are taken into account. The overall analysis shows that the ANN is superior to the kernel based SVM (linear, radial based, sigmoid and polynomial) and MLC. The best tool (ANN) is then applied on detecting the LULC change over part of Walnut Creek, Iowa. The change analysis of the multi temporal images indicates an increase in urban areas and a major shift in the agricultural practices.     

美国调整军事航天器的发展策略

美国对军事航天器的依赖性不断增强,也面临着航天器越来越长的研制周期和越来越高的研制成本,因此,改变军事航天器的发展策略成为当务之急。美国为提升战场实战能力正在进行发展思路的战略性调整,主要包括:拓展搭载军事有效载荷的途径;推进将大型卫星有效载荷拆分成小卫星的模式;开发分布式军事太空系统结构;对运载资源挖潜;优化商业模式实现业务拓展。对已有卫星的后续系统,进行规模改造与能力提升途径的调整,主要包括:对于军事通信卫星,扩大宽带或"超高频"(SHF)系统,突出窄带或"特高频"(UHF)系统及"先进极高频"(AEHF)系统;分步升级GPS的地面部分,将GPS-3提升为国家关键基础设施;确保导弹预警卫星系统重点。在开发新系统时,美国注重提高效能和降低成本,发展有效的支持能力和低成本小卫星系统,提高低成本机动发射和快速进入太空的能力,并以轨道资源利用为目的开拓新途径。     

多约束下备件多层多级库存优化模型

备件库存优化是提高装备保障效率和战备完好性的有效途径之一,针对目前部队现有多约束多层多级备件供应模式及工程实践要求的问题,以舰艇编队索马里护航任务准备阶段备件携带方案优化为研究背景,以舰艇保障经费、载荷和仓库体积为约束条件,以舰艇编队期望短缺数最小和可用度最大为目标函数,运用拉格朗日乘子法和边际效应法求出舰艇编队最优多层多级备件携带方案。通过实例将优化前后基地和后方仓库备件总的质量、体积及费用与对应约束指标上限进行对比分析,结果表明:本文模型和方法可以有效解决编队备件携带问题,同时也可为其他领域解决此类问题提供借鉴。      

考虑维修效能的修理级别优化

修理级别分析(LORA)是在装备研制阶段为建立使用维修制度而进行产品维修决策的重要方法,而修理级别分析无法决策每级站点的备件配置数量.为此,相对于传统研究,提出了一种在装备保障系统中重点考虑维修时间的库存与修理级别分析联合优化方法,并对该方法作了系统的分析, 确定了修理级别优化模型的目标函数和建模条件,解析了故障送修数量、备件需求、备件短缺等随机变量在多级多站点保障组织中的传递耦合关系,将维修时间参数引入到传统的只考虑维修费用的修理级别量化分析中,建立了联合修理级别和备件库存的单层三级优化模型;分析了优化问题的特点,设计了高效率的多变量凸优化算法,使得该模型能够高效地应用于多级多站点多类型备件的复杂保障系统;最后构建了优化方法应用案例,并利用仿真验证了该算法和模型的正确性,为修理级别分析工作的开展提供了一套可行的优化方法.      

基于空谱融合特征主动学习的高光谱图像分类

针对高光谱图像分类过程中存在的样本量少和分类精度低的问题,提出一种基于空谱融合特征主动学习的高光谱图像分类方法。主要包括构造三通道图像,全卷积网络提取空间特征,空谱特征结合,主动学习方法选择训练样本几个部分。通过结合像素的光谱特性和相邻像素间的空间关联,提取出可以反映像素空谱联合特性的综合特征,提高了像素特征的表达能力。为克服高光谱图像标注数据少、缺乏训练样本的问题,应用主动学习算法,充分选择更具有代表性的样本进行训练,达到少样本情况下较高的分类正确率。通过在标准数据集上进行实验,结果表明:该方法可以达到在总样本数1%作训练样本的情况下,分类正确率达到99.79%,优于传统的高光谱分类算法。     

一种航天器图像分类模型快速学习方法

为了有效分类空间目标,提出了一种航天器图像分类模型快速学习方法。分类模型的学习过程利用了分治思想,首先利用无监督聚类方法将图像数据集散列为类别桶,然后利用每个类别桶中的图像样本训练支持向量机完成学习过程。分类时利用支持向量机对待分类图像样本进行分类。实验结果表明,所提方法具有较好的实时性和准确率,能够为空间应用打下良好基础。     

Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional（2-D）histogram region oblique division and artificial bee colony（ABC）optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional（1-D）method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization（NCPSO）,the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.