空间目标态势行为与事件本体设计与构建

针对空间目标态势威胁响应和预警研究不完善的问题，为实现空间目标行为和事件的智能推理、实时响应和主动预警，保障航天活动和空间利益，提出面向行为与事件的空间目标态势本体模型(BEO-SO2)。基于基本形式本体(BFO)，构建空间目标态势行为与事件本体模型。在统一时空框架下建立空间目标关系模型，实现空间目标行为执行和事件发生过程的动态推演。以空间目标碰撞威胁为背景，设计对象实体等级体系和碰撞威胁行为与事件要素，建立碰撞威胁推理规则，基于空间目标态势行为与事件本体进行实现、验证与展示。结果表明,空间目标态势行为与事件本体能够实现空间目标碰撞威胁的等级推理与预警，并指导进一步的空间活动，为航天任务与空间活动提供有效保障。     

Temporal variation and asymmetry of sunspot and solar plage types from 1930 to 1936

Using the recently converted to digital format heliophysics catalogues of the Ebro Observatory published in the 1930s, we analyse simultaneously the temporal variation and asymmetry of two different solar structures located at different layers of the solar atmosphere: sunspots and solar plages. In particular, we do the research for all the types of sunspots and plages, including the daily and relative frequencies over the solar cycle. The data were catalogued using the sunspot Cortie classification and a solar plage classification scheme proposed by the Ebro Observatory, which group the phenomena by size and shape. For all types of both sunspots and plages, we observe a decrease in their frequency up to the end of solar cycle 16 and an increase over the beginning of solar cycle 17. Furthermore, we note that small sunspot groups are more likely to happen than bigger groups, although single big spots dominate near the solar minimum. The daily frequency of solar plage occurrences shows that there is not a dominance of compact or scattered solar plages. The North-South occurrence distribution of every type in both sunspots and solar plages shows an asymmetry during the solar cycle: in its declining phase, such asymmetry is directed to the north, while in the beginning of a new cycle is directed to the south.     

Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery

A statistical model is proposed for analysis of the texture of land cover types for global and regional land cover classification by using texture features extracted by multiresolution image analysis techniques. It consists of four novel indices representing second-order texture, which are calculated after wavelet decomposition of an image and after texture extraction by a new approach that makes use of a four-pixel texture unit. The model was applied to four satellite images of the Black Sea region, obtained by Terra/MODIS and Aqua/MODIS at different spatial resolution. In single texture classification experiments, we used 15 subimages (50 × 50 pixels) of the selected classes of land covers that are present in the satellite images studied. These subimages were subjected to one-level and two-level decompositions by using orthonormal spline and Gabor-like spline wavelets. The texture indices were calculated and used as feature vectors in the supervised classification system with neural networks. The testing of the model was based on the use of two kinds of widely accepted statistical texture quantities: five texture features determined by the co-occurrence matrix (angular second moment, contrast, correlation, inverse difference moment, entropy), and four statistical texture features determined after the wavelet transformation (mean, standard deviation, energy, entropy). The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features. The results obtained from the neural network classifier showed that the proposed texture model yielded an accuracy of 92.86% on average after orthonormal wavelet decomposition and 100% after Gabor-like wavelet decomposition for texture classification of the examined land cover types on satellite images.     

基于共享最近邻的专利自动分类技术研究

专利文献是一种具有法律效力的科技文献,其内容覆盖了几乎所有的技术领域的最新、最活跃的创新技术信息,因此对专利文献的分类和组织在专利管理中有着重要意义。本文通过对现有的专利自动分类技术的归纳和分析,融合BM25相似度计算方法和样本邻域信息提出一种基于共享最近邻的KNN专利自动分类方法。本方法的有效性在NTCIR-8专利分类评测任务中得到充分验证。     

基于最小距离法的遥感图像分类

本文在ENVI遥感图像分析软件环境下,利用最小距离法对遥感图像进行了有监督分类。仿真实验通过对遥感图像中地物目标的光谱信息和空间信息进行分析及特征提取,利用判别函数计算到中心的距离,解读遥感图像,并对分类结果进行定量评估。实验结果表明用最小距离法来分析遥感图像可以获得较高精确度。     

基于经验学习的空间机器人精细抓捕方法

机器人在空间环境下完成对复杂形状物体的精细操作,要求其具有较强的自主操作规划能力.从学习人类经验出发,模仿人类经验生成来建立经验数据库,提出有效的物体分类方法,在物体类别基础上,识别同类物体的可抓取区域,并提出“单拇指”姿态规划方法,将对物体的操作经验泛化到同类物体的操作规划中.试验证明了算法的有效性.     

小行星与陨石的光谱联系

Bus-DeMeo光谱分类系统中有24种小行星光谱类型以及约27种陨石矿物学类型。小行星和陨石之间的光谱联系在质量和可靠性上差异很大原因主要是某些矿物学中缺乏诊断特征和空间风化作用。将回顾从可见光和近红外光谱中远程获取的小行星矿物学信息,讨论如何从地质学家的角度理解小行星的光谱辨识。     

二氧化碳探测仪瞬态热分析模型修正

为实现碳卫星载荷在轨温度的准确预测,对其试验状态瞬态热分析模型的修正进行研究。先比较热分析计算结果与热平衡试验结果,求出两者对应温度监控点的温差;再利用蒙特卡洛法对二氧化碳探测仪热分析模型参数进行灵敏性分类,将模型参数分为整体灵敏、局部灵敏与不灵敏参数。然后根据热平衡试验数据,用拉丁超立方和单纯形法的混合法对模型各个参数进行分层修正,得到满足目标函数各个灵敏性参数的最优值。最后将参数最优值代入热分析模型计算验证该修正方法正确性,并进行残差分析。结果显示修正后各温度监控点热分析计算与热试验温差δ_a小于±0.5℃,残差修正率θ高于80%,修正后多数温差比修正前减少了一个数量级。结果表明修正取得的效果明显,修正方法合理可行。     

基于BoF模型的多特征融合纹理图像分类

针对特征词袋（BoF）模型缺乏空间和几何信息,对纹理图像内容表达不明显等问题,提出一种基于BoF模型的多特征融合纹理分类算法。将灰度梯度共生矩阵（GGCM）和尺度不变特征转换（SIFT）融合特征作为纹理图像的区域特征描述,通过动态权重鉴别能量分析进行最优参数特征选择,并用BoF量化纹理特征,使用支持向量机对图像进行训练和预测,得出分类结果。实验结果表明,本文算法对有旋转扭曲的纹理、边缘模糊纹理、有光照变化的纹理及杂乱纹理等均能取得较好的分类效果,相对于传统BoF模型及凹凸划分（CCP）方法等算法在UIUC纹理库上的分类正确率均有不同程度的提高,平均分类正确率分别提高12.8%和7.9%,说明本文算法针对纹理图像分类具有较高的精度和较好的鲁棒性。      

航天器开普勒轨道和非开普勒轨道的定义、分类及控制

给出了航天器开普勒轨道（KO）和非开普勒轨道（NKO）的来源、定义、分类和特点,阐明了KO和NKO之间的关系,介绍了相关的轨道控制与轨道确定、制导与导航的涵义.