RAM的故障模型及自测试算法

通过对ＲＡＭ故障情况的全面、深入分析,建立了ＲＡＭ的功能故障模型。并在此基础上,提出一种全面、实用的故障自测试算法。该算法分三步完成测试：（１）测试ＲＡＭ中的固定为１（０）故障、固定开路故障、状态转换故障、数据保持故障及第一类耦合故障中的不相关耦合等故障;（２）测试第一类耦合故障中的相关耦合故障;（３）测试第二类耦合故障。对ＲＡＭ的故障注入试验验证了该算法的有效性。并将此算法应用于航空电子模拟器的ＢＩＴ（Ｂｕｉｌｔｉｎｔｅｓｔ）的设计中。     

列车交会动态压力波实车测试系统的开发与应用

简述了列车交会动态压力波实车实验测试系统的组成和基本原理。系统以压阻式压力传感器、数据采集仪和微机为核心,应用超声、红外检测技术,实现了列车交会压力波、交会车辆侧壁间距及相对车速的同时测量,并成功应用于广深线实车实验中。文中还探讨了实车实验中存在的其它问题和解决方法     

高速BPSK相干光通信系统对激光器线宽要求

文章概述了激光器相位噪声对零差相干检测光通信系统性能的影响，对特定的基于平衡探测的科斯塔斯（Costas）环路接收系统相位误差进行了详细的理论分析，最后获得了不同传输码速率下的零差相干检测光通信系统对激光器线宽的具体要求。     

Constellation analysis of an integrated AIS/remote sensing spaceborne system for ship detection

A future system integrating data from remote sensing and upcoming AIS satellites is analyzed through the development of a novel design method for global, discontinuous coverage constellations. It is shown that 8 AIS satellites suffice to guarantee global coverage and a ship location update of 50 min if the spaceborne AIS receiver has a swath of 2800 nm. Furthermore, synergic utilization of COSMO/SkyMed and Radarsat-C data would provide a mean revisit time of 7 h, with AIS information available within 25 min from SAR data acquisition.     

ASW-RTS:一种软件性能数据在线检测方法

在软件健康管理系统中,需要对相关被测构件的性能进行在线检测,从而确定构件的健康状态。针对被测构件的性能衰退检测问题,在AR(p)模型基础上,结合时间序列滚动式算法的准确性,提出了一种具有自适性策略的ASWRTS算法,用于对被测构件革新异常类性能数据的在线检测,提高检测的准确度,并基于该算法设计了构件性能异常数据的在线检测过程。实验结果表明,该算法将检测精度提高了50%左右,能够有效在线检测构件的性能衰退,说明了该算法的正确性和有效性。     

波音737系列飞机过热探测系统原理分析及排故技巧

轮舱火警、机翼及后下部机身的过热探测系统是飞机防火系统的重要组成部分，且故障发生频繁。本文介绍了波音737系列飞机的轮舱火警、机翼及后下部机身的过热探测系统的基本原理及常见故障，并结合外场维修经验介绍了排除该系统故障的一些技巧。     

Hypothesis-test-based landcover change detection using multi-temporal satellite images – A comparative study

Remote sensing images and technologies have been widely applied to environmental monitoring, in particular landuse/landcover classification and change detection. However, the uncertainties involved in such applications have not been fully addressed. In this paper two hypothesis-test-based change detection methods, namely the bivariate joint distribution method and the conditional distribution method, are proposed to tackle the uncertainties in change detection by making decisions based on the desired level of significance. Both methods require a data set of class-dependent no-change pixels to form the basis for class-dependent hypothesis test. Using an exemplar study area in central Taiwan, performance of the proposed methods are shown to be significantly superior to two other commonly applied methods (the post-classification comparison and the image differencing methods) in terms of the overall change detection accuracies. The conditional distribution method takes into consideration the correlation between digital numbers of the pre- and post-images and the effect of the known pre-image digital number on the range of the post-image digital number, and therefore yields the highest change detection accuracy. It is also demonstrated that the class-dependent change detection is crucial for accurate landuse/landcover change detection.     

一种遥感图像建筑物检测新方法

遥感图像中的建筑物检测对于土地规划和地图绘制等具有重要意义。文章针对高分辨率全色遥感图像植被覆盖中隐蔽建筑物检测问题,提出了一种结合屋顶结构信息和纹理信息的快速房屋检测方法。首先利用屋顶边缘的几何关系寻找具有矩形屋顶的建筑物;然后使用形态学方法提取屋顶较亮的建筑物;最后利用局部二值模式（Local Binary Pattern,LBP）描述屋顶的纹理特征,并利用支持向量机（Support Vector Machine,SVM）排除误检区域。通过在全色遥感图像集上的试验证明,所提方法对高分辨率遥感图像植被覆盖区域中的建筑物目标具有较高检测率和较低误检率。     

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.     

基于模块度的复杂网络社团结构聚类新算法

针对传统的分裂聚类算法删除连边会影响节点划分的问题,结合K-means算法的思想并加以改进,提出了一种以邻居节点为聚类备选集,所有社团中心节点同时聚类的同步聚类算法。该算法根据节点中心度和最短路变化率确定社团中心节点集,然后以中心节点为社团中心,以邻居节点为聚类备选集合进行聚类,完成社团的划分。将算法应用到Zachary网络中并与GN算法、Top Leader算法进行比较,仿真结果表明该算法对网络有较好的划分。