基于目标轮廓特征的SAR图像目标识别

提出了一种基于目标轮廓特征的合成孔径雷达目标识别(SAR ATR)方法,该方法充分利用目标的局部空间结构信息进行识别。利用基于马尔可夫随机场(MRF)的图像分割及形态学处理提取SAR图像目标轮廓,在此基础上使用傅里叶描述子作为目标轮廓的特征量,选择最近邻准则下的模板匹配方法构造分类器,实现了基于轮廓特征的SAR图像目标识别。实验结果表明,本文方法具有优良的识别性能。     

流动的稳定性和转变理论与应用：有效的数值方法及其计算机程序

本书介绍了流动从层流到湍流的转变过程和求解方法，其理论基础为线性稳定性理论，所用方法为线性稳定性理论可精确执行的数值方法。本书主要内容为壁面边界层的转捩，对基于N—S方程的处理方法也有涉及。书中还详细介绍了这些方法在螺旋桨、机翼等方面的应用。     

异步电动机出厂试验智能测试系统的研究

介绍了自行研制开发的异步电动机出厂试验信号自动采集与智能分析系统的原理和设计。根据出厂试验的技术标准，对自动采集到的数据进行分析比较，从而给出电机状态信息。在满足生产实践中对异步电动机出厂试验的质量检测需要的同时，提高了电机出厂试验的智能化水平。     

氡射气探测采空巷道的应用

氡射气测量技术是近年来发展起来的一种新的探测方法，对火区、断层、陷落柱、金矿等探测都很有效。分析了利用氡射气探测采空巷道的原理、途径，对氡异常解释作了有益的探讨。     

On cyclic oscillation in granular gas

In a Maxwell‘s demon experiment on a dilute granular gas made up of two species of particles placed in two closely connected containers, a cyclic oscillation similar to the chemical oscillations is recognized that the two species of particles cluster alternately in the two containers. Then the nonlinear behavior is qualitatively explained based on a flux model.     

Conditional nonlinear optimal perturbation and its applications to the studies of weather and climate predictability

Conditional nonlinear optimal perturbation （CNOP） is the initial perturbation that has the largest nonlinear evolution at prediction time for initial perturbations satisfying certain physical constraint condition. It does not only represent the optimal precursor of certain weather or climate event, but also stand for the initial error which has largest effect on the prediction uncertainties at the prediction time. In sensitivity and stability analyses of fluid motion, CNOP also describes the most unstable （or most sensitive） mode. CNOP has been used to estimate the upper bound of the prediction error. These physical characteristics of CNOP are examined by applying respectively them to ENSO predictability studies and ocean＇s thermohaline circulation （THC） sensitivity analysis. In ENSO predictability studies, CNOP, rather than linear singular vector （LSV）, represents the initial patterns that evolve into ENSO events most potentially, i.e. the optimal precursors for ENSO events. When initial perturbation is considered to be the initial error of ENSO, CNOP plays the role of the initial error that has largest effect on the prediction of ENSO. CNOP also derives the upper bound of prediction error of ENSO events. In the THC sensitivity and stability studies, by calculating the CNOP （most unstable perturbation） of THC, it is found that there is an asymmetric nonlinear response of ocean＇s THC to the finite amplitude perturbations. Finally, attention is paid to the feasibility of CNOP in more complicated model. It is shown that in a model with higher dimensions, CNOP can be computed successfully. The corresponding optimization algorithm is also shown to be efficient.     

论传感器的现代信息技术

结合传感器的定义和原理,对传感器的技术、特性、分类及发展进行了探讨。     

ICL7135与单片机接口电路设计

介绍了双积分A／D转换器ICL7135与AT89C51进行串行数据采集的原理、接口电路的连接方法及时钟频率选择，并且通过实验数据及图形证明了该方法的可行性。     

基于Zernike矩和Gabor变换的脱机签名鉴定

本文介绍了一种利用Zernike矩和Gabor变换的脱机签名鉴定方法。通过特征融合技术将两种特征联合起来作为签名的特征。试验表明融合后的特征对签名的分类效果要优于单独使用Zernike矩或Gabor特征。     

New judging model of fuzzy cluster optimal dividing based on rough sets theory

To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory: information system discrete algorithm (algorithm 1) and samples representatives judging algorithm (algorithm 2). On the principle of the farthest distance, algorithm i transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.