太阳质子事件警报

采用人工神经网络预报方法,利用太阳耀斑的日面位置、X射线辐射的峰值流量及其上升时间、2695MHz和8800MHz微波辐射的半积分流量等5个物理参量,提出了一个新的太阳质子事件警报方案,预报太阳质子事件的发生及其流量和时间．该方案在本文检验中达到93．75％的预报准确率．     

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。     

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.     

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.     

人工智能技术在未来装备应用的发展思考

加快装备智能化发展是战略发展的需要。为了促进人工智能技术军事应用发展,梳理了军事人工智能技术应用发展现状,探讨了军事人工智能技术的发展需求与挑战,分析给出了军事智能应用关键技术,并提出了后续发展思考。     

材料粗糙度变化对卫星光谱红化效应的影响

在轨人造目标的地基观测光谱存在红化效应,影响对目标反射光谱的理解应用.目标在进入空间环境后外露材料表面粗糙度发生变化.本文结合材料的光谱散射特性、卫星轨道和观测条件,仿真分析了卫星表面材料粗糙度变化对红化效应的影响.基于文献中实验数据,设计不同粗糙度的卫星表面材料样本,模拟空间环境中材料表面粗糙度的变化.利用双向反射分布函数,建立不同粗糙度材料样本的光谱散射模型.理论模拟结果表明,材料表面粗糙度的变化会影响其光谱散射特性,散射光谱形态和散射分布取决于粗糙度及入射-反射几何关系.引入网格剖分和可见面片,利用材料双向反射分布函数,仿真计算卫星表面材料在不同粗糙度状态下,整个卫星在可见光波段的反射光谱信号.仿真结果表明,随着卫星表面材料粗糙度的增加,其反射光谱在600nm之后随波长增加而呈现上升趋势,这表明空间环境中外露材料表面粗糙度变化是卫星产生红化效应的原因之一.      

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。     

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。