一种可实时化的多光谱图像融合系统

在多光谱摄影和光电成像技术基础上，本文提出了一种可实时化的多光谱图像融合系统。与多光谱摄影相比，不仅可实现多光谱实时化摄像，而且光谱响应进一步向近红外延伸，并引入图像处理技术。实验表明：这种技术对于增加系统的识别能力具有明显的作用，可进一步构成不同用途的新型多光谱图像融合系统。     

实时任务动态调度算法

任务调度算法是提高多任务系统效率的一种有效途径,特别是在时间紧迫环境下,实时任务调度算法更具有重要的应用价值.在研究建立实时任务调度模型方法的基础上,对模型进行理论分析,提出了实时任务调度的算法,并通过实例加以验证.     

多自由度测试技术的发展

介绍目前较常用的多种多自由度测试技术，给出每种测试技术的测试原理和应用场合，比较各种技术的优缺点和适用范围，讨论该技术发展的方向。     

基于BP网络分类器的交通标志识别

针对中国全部3大类116个交通标志,即禁令标志、指示标志、警告标志,用BP网络实现分类功能.实验中使用了3种测试集,即加高斯噪声、水平扭曲和日本交通标志实景图,对BP网络的分类性能进行了测试.实验结果表明:用BP网络实现交通标志粗分类功能的效果是比较理想的,对交通标志的颜色失真和形状失真均具有较好的容错性和鲁棒性.     

基于多Agent的宏观经济智能预测决策支持系统研究

在分析智能决策支持系统的发展基础上，将人工智能思想，特别是Agent应用于构建智能宏观经济预测决策支持系统。提出面向任务的Agent设计思想，以任务为核心设计多种类型Agent共同完成预测决策任务，给出基于多Agent的系统组成结构，并给出系统中不同类型Agent的具体实现结构，同时探讨了整个系统的运行机制以及系统中多Agent之间的合作。整个系统的构建过程也是面向Agent的程序设计的过程。     

可调实时时间差电子错位散斑技术及应用

本文首次提出一种可调实时时间差电子错位散斑干涉技术,并成功地用于热变形测量,该方法还可用于长时间连续变形和瞬态大变形的位移测量。叙述了该技术的原理,给出了一些应用的实验结果。     

成像光谱技术中机上实时数据压缩方法研究

根据成像光谱仪的特点用一系列串行的一维压缩实现二维图像压缩.提出了在机上对原始数据从光谱方向进行实时压缩的“二真值线性预测方法”.该法可做到压缩/复原后光谱特征信息不丢失.实验表明,对大多数光谱数据在1%的重建精度下,至少可获得2:1的压缩比.从总体而言,压缩后原始数据率可降低3～4倍.文中还对压缩数据的编码和误差进行了讨论.     

The Intelligent Properties of Micro－reactors for Preparating Nanoparticles

TiO2 nanoparticles were synthesized by using micro-reactors. The shape and size of the nanoparticles produced from the original micro-reactors and the five times recycled micro-reactorsmother liquor were investigated on transmission electron microscopy (TEM) by using the original sample, freeze prepared sample, and dyeing treated sample, respectively. UV-VIS spectrometry was used to study the growth process of TiO2 nanoparticles in main reactors. The results showed that micro-reactors with nanometer magnitude had spherical or oval structures, and could restore to their original structure after they were destroyed. The products prepared in the original micro-reactors were similar to that in the micro-reactors recycled for many times, suggesting that the micro-reactors had memory function.     

Real-time onboard wind and windshear determination,part 2: Detection

This paper is concerned with windshear detection in connection with real-time wind identification (Ref. 1). It presents a comparative evaluation of two techniques, one based on the shear/downdraft factor and one based on the wind difference index. The comparison is done with reference to a particular microburst, that which caused the 1985 crash of Flight Delta 191 at Dallas-Fort Worth International Airport.The shear/downdraft factor has the merit of combining the effects of the shear and the downdraft into a single entity. However, its effectiveness is hampered by the fact that, in a real situation, the windshear is accompanied by free-stream turbulence, which tends to blur the resulting signal. In turn, this results in undesirable nuisance warnings if the magnitude of the shear factor due to free-stream turbulence is temporarily larger than that due to true windshear. Therefore, proper filtering is necessary prior to using the shear/downdraft factor in detection and guidance. One effective way for achieving this goal is to average the shear/downdraft factor over a specified time interval . The effect of on the average shear/downdraft factor is studied.     

Real-time onboard wind and windshear determination,part 1: Identification

Standard wind identification techniques employed in the analysis of aircraft accidents are post-facto techniques; they are processed after the event has taken place and are based on the complete time histories of the DFDR/ATCR data along the entire trajectory. By contrast, real-time wind identification techniques are processed while the event is taking place; they are based solely on the knowledge of the preceding time histories of the DFDR/ATCR data.In this paper, a real-time wind identification technique is developed. First, a 3D-kinematic approach is employed in connection with the DFDR/ATCR data covering the time interval preceding the present time instant. The aircraft position, inertial velocity, and accelerometer bias are determined by matching the flight trajectory computed from the DFDR data with the flight trajectory available from the ATCR data. This leads to a least-square problem, which is solved analytically every seconds, with / small.With the inertial velocity and accelerometer bias known, an extrapolation process takes place so as to predict the inertial velocity profile over the subsequent -subinterval. At the end of this subinterval, the extrapolated inertial velocity and the newly identified inertial velocity are statistically reconciled and smoothed. Then, the process of identification, extrapolation, reconciliation, and smoothing is repeated. Subsequently, the wind is computed as the difference between the inertial velocity and the airspeed, which is available from the DFDR data. With the wind identified, windshear detection can take place (Ref. 1).As an example, the real-time wind identification technique is applied to Flight Delta 191, which crashed at Dallas-Fort Worth International Airport on August 2, 1985. The numerical results show that the wind obtained via real-time identification is qualitatively and quantitatively close to the wind obtained via standard identification. This being the case, it is felt that real-time wind identification can be useful in windhsear detection and guidance, above all if the shear/downdraft factor signal is replaced by the wind difference signal (Ref. 1).This paper and its companion (Ref. 1) are based on Refs. 2–4.This research was supported by the Aviation Research and Education Foundation and by Texas Advanced Technology Program, Grant No. TATP-003604020.