太阳帆航天器三维人工平动点变化特性研究

本文研究了太阳帆航天器在光压因子、锥角和钟角共同作用下的三维人工平动点的变化特性。在光压因子较小的时候,五个平动点会拓展成五个不相连的“人工”平动点面。随着光压因子增大,平动点面SL 3 ,SL 4 和SL5将逐渐扩大并互相融合,最终延展至SL 1 与之融合。而平动点面SL2则始终保持独立的球面,只随着光压因子的增大而扩大但不与其它平动点面发生融合。平动点位置的改变,意味着对应的周期轨道也随之改变,这为平动点周期轨道的转移等任务提供了有效参考。     

一种智能化元器件检测系统的研究

为解决航天元器件筛选检测过程中存在的计划管理不合理、过程管控不透明、灵活性不够、人工参与过多等问题,按照智慧工厂的参考模型,提出了一种元器件智能化检测管理系统的构建方案,并对所需关键技术进行了分析,集成了物联网、机器视觉、大数据分析、ERP技术,实现了检测业务的信息化、自动化、智能化。验证了航天某元器件可靠性中心的智能化检测系统,并进行了技术实现,为同行业的技术研究、应用实施提供了参考。     

Low-altitude,near-polar and near-circular orbits around Europa

The dynamics of orbits around planetary satellites, taking into account the gravitational attraction of a third-body and the non-uniform distribution of mass of the planetary satellite, is studied. The Hamiltonian considered is explicitly time-dependent. Conditions for frozen orbits are presented. Low-altitude, near-polar orbits, very desirable for scientific missions to study planetary satellites such as the Jupiter’s moon Europa, are analyzed. Lifetimes for these orbits are computed through the single and double averaged method. Comparison between the results obtained by the single and double averaged method is presented. The single-averaged model is more realistic, since it does not eliminate the term due to the equatorial ellipticity of the planetary satellite as done by the double-averaged problem. Considering the single-averaged method, we found unstable frozen orbits where the satellite does not impact with the surface of Europa for at least 200 days. We present an approach using the unaveraged disturbing potential to analyze the effects of these terms in the amplitude of the eccentricity.     

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.     

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.     

评分偏差对于推荐质量的影响

从理论上分析了评分偏差对于推荐质量的影响;基于潜在偏好及已知评分对评分偏差进行度量,其中潜在偏好通过心理测量学模型计算得出;通过设定不同的评分偏差水平,对评分偏差的影响进行了实验验证.理论分析及实验验证表明:评分偏差可导致推荐准确度及覆盖度下降;基于高质量的评分数据,协同过滤算法可为用户作出好的推荐.     

基于改进SSD算法的航拍目标检测算法研究

研究基于深度学习技术的无人机航拍图像目标检测算法,首先介绍目标检测算法SSD(Single Shot MultiBox Detector),并对其特征提取网络进行改进,采用稠密特征提取网络替换原网络的主干特征提取网络,提高算法的特征提取能力,从而提升了算法的检测精度。针对网络实时性问题,在算法中引入分组卷积,极大地减少了网络参数量,提升了网络推理速度。为解决训练中出现的正负样本不均衡问题,利用焦点损失（Focal Loss）改进了原算法的损失函数,进一步提升了网络的收敛速度和精度。最后,通过仿真验证了改进算法在目标检测精度上的优越性。     

基于组合不变矩与神经网络的卫星目标识别

首先介绍了Hu提出的不变矩和Jan Flusser提出的仿射不变矩,在不增加特征向量维数的前提下,将不变矩的部分分量与仿射不变矩组合成新的特征向量——组合不变矩。利用组合不变矩提取图像中的不变特征向量,并结合基于人工神经网络的分类器,较好的实现了对卫星目标的分类识别。     

自主空战技术中的机动决策:进展与展望

自主空战（AAC）是指飞机依靠机载等相关设备，自主进行战场感知、决策及控制，以执飞空战的技术，其核心是机动决策模块。针对该模块的研究，调研了国内外的现有方法，并按照有关方法的核心内涵，将其分为基于数学求解、机器搜索、以及数据驱动3类分别简要概述；针对每一类方法列出了具有代表性的示例技术，并讨论了其优缺点。指出了自主空战决策的研究应立足现有相关方法及技术基础，同时积极吸纳机器学习、人工智能等新兴技术，合理利用各类方法的优点，取长补短，实现自主空战决策的长足发展；有关于该研究设想的关键科学问题及其潜在解决方案也在文中进行了初步讨论。希冀基于针对自主空战决策研究框架及实现方法的概念性讨论，促进其技术发展，推广相关思路在有关国家安全、国民经济建设等领域的应用，以满足国家重大需求和工程实践。     

基于人工黏性的二维激波小波多尺度数值计算

基于自适应小波配点法和人工黏性，构造了二维激波问题的小波数值计算格式。利用最细尺度的小波系数构造两组激波定位函数，分别用以控制两个正交方向人工黏性的大小和分布。对三类二维激波问题进行计算和验证，结果表明：自适应小波配点法利用小波阈值滤波删除变化平缓区域大量网格点，而保留变化急剧区域的网格点，比传统方法计算效率高，且分辨率越高，计算效率更高；利用最细层小波系数构建的幂函数形式的激波定位函数能准确判断激波位置和控制X、Y方向人工黏性的大小和分布，从而捕捉不同方向和不同强度的激波。