A two-phase approach for setting targets in DEA with negative data

Conventional DEA models have been introduced to deal with non-negative data. In the real world, in some occasions, we have outputs and/or inputs, which can take negative data. In DEA literature some approaches have been presented for evaluating performance of units, which operate with negative data. In this paper, firstly, we give a brief review of these works, then we present a new additive based approach in this framework. The proposed model is designed to provide a target with non-negative value associated with negative components for each observed unit, failed by other methods. An empirical application in banking is then used to show the applicability of the proposed method and make a comparison with the other approaches in the literature.     

Algorithms of target detection on hyperspectral imagery

With the development of airborne and spaceborne remote sensing from the 1980s, as a new and growing technology, hyperspectral imaging is widely used in different fields, such as military investigation, battlefield information acquisition, environmental monitoring, mineral exploration and public security. Because of the unique characteristic of acquiring spectral and spatial information simultaneously, it brings the hyperspectral detection advantages when dealing with target detection problem under complex conditions. Target detecting models of hyperspectral image are established, including the target subspace model and the probability statistical model. And several algorithms are introduced, which are based on original spectral features, sub-space projection and probability statistical model separately. Comparison shows that if the background includes fault objectives, GLRT is the best algorithm, and its SINR is the largest; on condition of anomaly target detection, LPTD is the best, and have a quite high SINR.     

声纳方程的瞬态形式讨论

本文提出用瞬态信号的能谱级代替稳态信号的声强级来定义声纳参数，并以短柱声散射为例，运用数值模拟方法，验证用能谱级定义声纳参数，声纳方程是比较有效的，这为应用声纳方程处理瞬态声信号从而获得目标强度提供了一种有效可行的方法。     

弹道相机白天工作能力的影响因素分析及提高途径

弹道相机通常是在夜间对飞行目标进行拍摄,在相机类型不改变的情况下要达到白天工作能力具有一定困难,主要障碍是拍摄后,底板上目标与背景的衬比(度)下降,难以达到判读要求.针对这一问题,本文详细叙述了各因素对判读要求的影响,提出了改善相机白天工作能力的可行方法.     

目标辐射噪声中线谱序列的提取方法

不同类型目标辐射噪声包络谱中的线谱及其谐波分布存在较大差异，是进行目标检测和分类识别的重要线索。本文提出了一种基于能量判据和子频带综合的线谱序列提取方法，并利用实测数据进行了比较分析。仿真和对实际数据分析的结果表明，该方法能够较准确、稳定提取线谱序列，为目标检测和分类识别提供了可靠依据。     

电子碰撞亚稳态氦电离截面的计算

在电子和激发态原子散射的物理过程中，靶的连续态是非常重要的通道，我们使用等价局部光学势模型计算了从电离阈值到２００ｅＶ能量范围的氦的２３Ｓ亚稳态的电离截面，并同实验结果作了比较。     

Effects of threshold on single-target detection by using modified amplitude-modulated joint transform correlator

Effects of threshold value on detection performance of the modified amplitude-modulated joint transform correlator are quantitatively studied using computer simulation. Fingerprint and human face images are used as test scenes in the presence of noise and a contrast difference. Simulation results demonstrate that this correlator improves detection performance for both types of image used, but moreso for human face images. Optimal detection of low-contrast human face images obscured by strong noise can be obtained by selecting an appropriate threshold value.     

纯方位目标运动分析的一种“检测前跟踪”技术

被动检测中,当目标信噪比较低时,传统的波束形成估计目标方向有较大的误差,为此我们提出一种改进的检测前跟踪技术,即直接对波束输出的方位-功率图序列做运动分析。对目标所有可能的运动路径进行时间积分,理论上与目标真实轨迹最靠近的路径将获得最大的功率累积量。本文结合纯方位目标运动分析的规律,以不降低检测性能为前提,开发了一种简化的路径搜索策略。在计算机仿真实验中,也证实了该简化搜索策略使得运算量至少降低了两个数量级。     

基于加权mean-shift可见光/红外双通道目标跟踪

为提高目标亮度突变时的跟踪性能,在每一帧进行目标跟踪时,首先提取可见光图像的颜色特征,红外图像的垂直投影图像和水平投影图像特征,然后利用可见光/红外各自通道的Bhattacharyya系数计算该通道的权值,并对加权mean-shift双通道跟踪方法进行了推导,提出了基于加权mean-shift可见光/红外双通道目标跟踪算法.该方法使前后两帧目标相似度大的通道取大的权值,从而达到有效利用各通道有利信息、提高跟踪性能的目的.实验表明,用本文提出的算法进行可见光/红外双通道目标跟踪时,与基于mean-shift单通道(可见光或红外)目标跟踪算法相比,可提高目标跟踪的准确度,特别是当目标进入树荫区域,引起目标亮度发生显著变化时,跟踪性能基本不受影响.     

基于快速骨架提取的三维目标识别(英文)

针对三维目标识别问题,提出了一种基于快速骨架提取的方法。根据骨架所反映的目标拓扑结构,建立了不同目标局部结构之间的对应关系;而在对应的局部曲线段上,采用基于曲线配准的方法进行匹配;以各个局部匹配的成本之和评估不同目标的相似性.这种方法在目标出现一定程度的视觉变形时仍具有较好的识别效果,同时避免了基于曲线方法的匹配目标的局部,而忽略局部之间相互的空间组织的缺点所造成的误匹配.算例结果表明这种算法对于三维目标有较好的识别效果.