Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.     

A triangle voting algorithm based on double feature constraints for star sensors

A novel autonomous star identification algorithm is presented in this study. In the proposed algorithm, each sensor star constructs multi-triangle with its bright neighbor stars and obtains its candidates by triangle voting process, in which the triangle is considered as the basic voting element. In order to accelerate the speed of this algorithm and reduce the required memory for star database, feature extraction is carried out to reduce the dimension of triangles and each triangle is described by its base and height. During the identification period, the voting scheme based on double feature constraints is proposed to implement triangle voting. This scheme guarantees that only the catalog star satisfying two features can vote for the sensor star, which improves the robustness towards false stars. The simulation and real star image test demonstrate that compared with the other two algorithms, the proposed algorithm is more robust towards position noise, magnitude noise and false stars.     

基于任务的作战飞机效能评估模型

针对作战飞机的任务效能评估问题,提出了基于作战飞机任务的任务效能新概念;采用概率分析方法将飞机作战过程分解并抽象为一组概率值,建立了相应的任务效能评估模型;该模型可以在兼顾传统仿真评估模型结果精度的同时,具有模型简明、计算消耗低、方便快捷等特点;更具有实际作战指挥意义,其快速可信的评估结果有利于作战指挥官进行合理的、科学的任务规划,从而有利于在执行特定任务时更好地运用现有的作战飞机和武器;算例结果表明该模型可以较为有效地对作战飞机携带不同机载武器时在不同作战环境中打击不同作战目标进行效能评估.     

大学语文课程中人文教育探讨

本文从大学语文课程的角度审视人文教育的现状、背景及现实意义,探讨了大学语文课程中人文教育的特点及实践.     

一种推广的靶效应模型及其对剂量-存活率的拟合探讨

辐射生物物理模型对于准确、有效地评估空间辐射引起的生物损伤及风险具有重要意义。目前较为流行的基于地基模拟的“靶效应模型”和“非靶效应模型”，是进一步探索空间辐射生物物理模型的基础。在考虑了辐射的非靶效应和机体复杂的修复过程的基础上，将传统理论中“击中”即“失活”的线性关系进行了推广，提出了基于否定算子的靶效应模型。通过对辐射钝感和敏感的两类细胞辐射实验中剂量与细胞存活率数据进行拟合发现，对于正常皮肤成纤维细胞的存活率数据，经典的靶学说与基于否定算子的改进靶效应模型都有较好的拟合效果；而对于人类胚胎肝细胞的存活率数据，基于否定算子的改进靶效应模型的拟合结果明显优于传统的靶学说模型。拟合结果中的模型参数值在人成纤维细胞和胚胎肝细胞中明显不同，说明了本模型的参数与传能线密度，细胞的种类、以及修复能力等物理和生物因素有关。     

基于正弦拟合的空间目标短弧关联算法

星敏感器是一种高精度姿态传感器，具有断续拍摄空间目标的能力，可作为空间目标监视平台。将星敏感器断续观测的短弧准确关联是实现空间目标精确定轨的前提。通过对空间目标的大量观测数据统计发现，空间目标的赤纬随赤经的变化始终满足一条周期为360°的正弦曲线且峰值与轨道倾角有关。对新旧航迹的赤经赤纬变化规律进行研究，提出一种基于正弦拟合的空间目标短弧关联算法，避免了空间目标初定轨的步骤，节约了算法运行时间。仿真中观测时长最短为50s，通过对目标密集的GEO带增加额外约束，可区分倾角相差0.01°的轨道。当航迹段间隔3h时，3组噪声水平目标关联的准确率均达90%以上。     

Costas编码信号的循环谱特征分析与参数估计

研究了准确反映Costas编码信号特点的关键参数集合。基于循环谱相关密度估计方法,分析了Costas编码的循环谱特征及其与辐射源侍号参数的对应关系,提出了一种没有先验知识条件下Costas编码截获信号特征提取方法。通过对典型的Costas编码信号参数提取归一化均方根误差的计算,验证了该算法的有效性。     

月球主要构造特征：嫦娥一号月球影像初步研究

月球在31亿年前已基本停止地质活动,从而保留了其形成初期的信息．这些信息对于认识月球、地球乃至太阳系的形成演化具有重要意义．在已有研究成果的基础上,结合嫦娥一号探月卫星CCD影像数据,从月海穹窿、撞击坑、月岭、断裂、月坑链、月溪及月谷等方面介绍了月球主要构造形式的地质特征、形貌特征及遥感影像特征,对其成因以及所隐含的地质意义进行了分析．结果表明,嫦娥一号CCD影像信息丰富,影像清晰,利用其CCD影像数据进一步研究月球的构造现象是可行的．     

基于贯序正则极端学习机的时间序列预测及其应用

为实现对液压泵特征参数的在线预测,提出一种贯序正则极端学习机(SRELM),并研究了基于SRELM的预测方法.SRELM根据结构风险最小化原理实现网络训练,其网络权值可随新样本的逐次加入而递推求解,具有泛化能力强与训练速度快的优点,因此适于特征参数的在线预测.基于SRELM的预测方法利用特征参数训练SRELM模型,以逐...     

基于自抗扰控制的导弹三维制导规律

针对导弹制导系统中存在模型不确定性和外部扰动的特点,提出了利用自抗扰控制器设计导弹三维制导规律的新方法.在建立导弹三维制导模型的基础上,把系统非线性部分、回路间耦合项以及目标机动加速度视为对制导系统的未知总扰动,从而将模型变换为适于自抗扰控制的仿射系统.通过为俯仰和偏航回路分别引入自抗扰控制器,实现了双回路的动态解耦,...