Manifold structure preservative for hyperspectral target detection

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.     

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C₄ index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.     

燃气的基准表及图制定

本文在阐述基准的热力学概念基础上,提出了燃气基准的计算方法,应用CHAPPELL推荐的有关燃气热力性质表达式,导出计算燃气基准的公式,通过微机计算,将结果整理成燃气基准的有关表、图。     

故障检测与分离的广义一致性算子法

本文提出了故障检测与分离的广义一致性算子法的一般形式。得出了两个故障可分离的充要条件和多个故障可分离的充要条件。最后给出了检测和分离故障的过程。     

一种具有较强泛化能力的神经网络模型研究与应用

分析了影响神经网络模型泛化能力的因素。以电解铝过程中氧化铝浓度的神经网络软测量为例 ,提出了利用先验知识确定网络结构 ,采用特定实验保证样本数量和质量 ,离线训练加在线学习修正模型等措施 ,改善神经网络模型的泛化能力。现场应用表明这些措施是有效的。这样建立的神经网络模型准确 ,泛化能力强 ,为实现过程的先进控制提供了可靠保障     

低轨道带可变翼的平板型极高真空分子屏

本文计算了在低轨道自由飞行的平板型分子屏（ＷａｋｅＳｈｉｅｌｄＦａｃｉｌｉｔｙ）加了可变翼后，分子屏后实验区的压力分布。计算中考虑了分子屏和翼材料出气对实验区压力的影响。本文并对加可变翼和不加可变翼、考虑出气和不考虑出气几种情形进行了计算和比较。我们的结果表明：给轨道分子屏加上可变翼后，其实验区的压力可比不加可变翼降低一个数量级以上，压力达１０－１３Ｐａ。本文的研究表明，这种给轨道分子屏加可变翼的模型对更好的利用分子屏环境是非常有利的。     

神经网络在未来超分辨雷达中的应用

最新的技术发展已为研制超分辨雷达创造了条件。它能够突破普通雷达基于匹配滤波原理的分辨能力,而实现超分辨。超分辨雷达的巨大计算量通常来自求解非线性最小二乘问题时的多维搜索。本文针对这一问题,研究了雷达信号处理中的两个典型例子:(1)利用阵列处理检测个数已知而方向未知的雷达目标;(2)对波音727飞机进行超分辨距离多普勒成像。说明Hop-field神经网络通过集体运算能够求解各种困难的最优化问题,因而在未来的超分辨雷达中有广阔的应用前景。     

一种适于动态环境的盲多用户检测算法

比较两种盲多用户检测算法在动态环境中的收敛性能,并通过计算机进行模拟仿真。其中一种是能随输入信号矢量变化而变化的变步长最小输出能量盲算法;另一种是既保持最小输出能量检测器的全局收敛性,又具有最小均方误差检测器高输出信干比优点的判决反馈变步长盲算法。利用前者在动态强干扰下收敛快及后者在稳态时输出信干比高的优势,将二者结合起来应用到动态环境中,提高系统性能,同时抑制多址干扰和窄带干扰。     

燃气收缩式作动器--一种新颖高效的着陆缓冲装置

文章介绍一种新颖的着陆缓冲装置——燃气收缩式作动器。可是在回收物体乘降落伞着陆前,通过回收物体快速向上（向降落伞方向）提升,抵消物伞系统的垂直下降速度,从而达到回收物体以很小的速度着陆,实现回收物体的软着陆。     

一种新型级间分离技术研究

本文对一种含固体燃气发生器的级间分离技术进行了研究。首先给出了分离的总体方案，建立了级间段气体压强瞬变的数学模型和导弹两级的运动模型，并利用流体动力学软件对级间段充气过程进行了模拟。简要介绍了固体燃气发生器的设计，并给出燃气发生器装药的选取原则。最后通过对某型号导弹的计算，证明这种级间分离方案是可行的。