基于B/S的开放式基础实验教学系统设计

采用B/S模式和ASP技术,对基础实验教学系统进行了全新的设计,这种设计对强化基础实验教学的效果与质量将起到积极和有益的作用。     

基于Hough变换的高分辨SAR图像道路目标检测

给出了一种检测高分辨率SAR图像道路目标的方法。该方法首先对原始图像进行一系列的预处理,然后利用Hough变换识别道路。在提取感兴趣区域时,该算法采用了高斯概率迭代方法代替了一般的CFAR检测或阈值分割;在进行Hough变换时,该算法用平均Hough变换代替一般的Hough变换;在检测变换域峰值点时,采用了结合全局CFAR检测的最大值点搜索的方法;最后对检测出来的准道路,该算法根据道路的独具性质进行鉴别,以确定其是否确实为道路。使用MSTARRedstone实测杂波图进行实验,取得了满意的效果。     

A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy

Feature extraction is an important part of signal processing, which is significant for signal detection, classification, and recognition. The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields. Reverse dispersion entropy (RDE) proposed by us recently, as a nonlinear dynamic analysis method, has the advantages of fast computing speed and strong anti-noise ability, which is more suitable for measuring the complexity of signal than traditional permutation entropy (PE) and dispersion entropy (DE). Empirical wavelet transform (EWT), based on the theory of wavelet analysis, can decompose a complex non-stationary signal into a number of empirical wavelet functions (EWFs) with compact support set spectrum, which has better decomposition performance than empirical mode decomposition (EMD) and its improved algorithms. Considering the advantages of RDE and EWT, on the one hand, we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy; on the other hand, we use RDE as the features of EWFs to improve the signal separability and stability. Finally, we propose a novel signal feature extraction technology based on EWT and RDE in this paper. Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals. Moreover, it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.     

基于小波变换与数学形态学的红外图像分割方法

提出一种基于小波变换和形态学相结合的红外图像分割方法,该方法利用小波变换和形态学消除红外图像的混合噪声,抑制背景干扰和增强目标;通过选择适当的结构元素进行系列形态组合运算,自适应确定阈值,提取目标;利用形态学水线区域分割法对图像进行分割,分离目标区域。实验结果表明,该方法能有效检测和分割出低信噪比复杂背景红外图像中的目标。     

低可观测条件下的一种信号检测方法

针对在低信噪比条件下,检测机载合成孔径雷达(SAR)信号比较困难的情况,综合谱图-Hough变换法可以进行能量积累和小波变换具有比较有效抑制杂波的特点,提出了一种检测SAR信号的方法,即基于谱图-Hough变换和小波变换的检测方法。运用该方法首先对SAR信号进行谱图-Hough变换,谱图-Hough变换能对SAR信号进行能量积累,降低虚警概率,然后使用小波变换排除Hough空间中的虚假峰值。仿真结果表明了该算法能够有效地抑制杂波,达到检测的目的。     

Markovian approximation for manufacturing systems of unreliable machines in tandem

This paper studies production planning of manufacturing systems of unreliable machines in tandem. The manufacturing system considered here produces one type of product. The demand is assumed to be a Poisson process and the processing time for one unit of product in each machine is exponentially distributed. A broken machine is subject to a sequence of repairing processes. The up time and the repairing time in each phase are assumed to be exponentially distributed. We study the manufacturing system by considering each machine as an individual system with stochastic supply and demand. The Markov Modulated Poisson Process (MMPP) is applied to model the process of supply. Numerical examples are given to demonstrate the accuracy of the proposed method. We employ (s, S) policy as production control. Fast algorithms are presented to solve the average running costs of the machine system for a given (s, S) policy and hence the approximated optimal (s, S) policy. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 65–78, 2001     

基于时频分析的飞机目标识别

本文利用小波变换对五种飞机目标的雷达回波进行时频分析,得到了回波信号的时频分布图。全面清楚地描述了目标的散射特性。文中通过提取时频分布图的矩特征作为目标特征量,对五种飞机的目标作了识别实验,取得了良好的识别效果     

电偶腐蚀下非稳态扩散瞬时静电场分析

为分析在浓差极化条件下非稳态过程中的静电场分布情况,在斐克第二定律的基础上利用拉普拉斯变换求解电极表面氧浓度随时间的变化。通过法拉第定律分析受氧浓度控制下的电偶腐蚀电流密度,在此基础上利用电流元模型求解电解质溶液中任意位置处产生的瞬时电位及静电场强度,并通过实验验证结果的正确性。结果表明：浓差极化下的非稳态扩散传质产生的静电场及电位会随着时间的增加而逐渐减小,并最终达到稳态,且电流密度会随着氧的浓度减小而减小。     

Exact analysis of (R,s, S) inventory control systems with lost sales and zero lead time

We study an (R, s, S) inventory control policy with stochastic demand, lost sales, zero lead‐time and a target service level to be satisfied. The system is modeled as a discrete time Markov chain for which we present a novel approach to derive exact closed‐form solutions for the limiting distribution of the on‐hand inventory level at the end of a review period, given the reorder level (s) and order‐up‐to level (S). We then establish a relationship between the limiting distributions for adjacent values of the reorder point that is used in an efficient recursive algorithm to determine the optimal parameter values of the (R, s, S) replenishment policy. The algorithm is easy to implement and entails less effort than solving the steady‐state equations for the corresponding Markov model. Point‐of‐use hospital inventory systems share the essential characteristics of the inventory system we model, and a case study using real data from such a system shows that with our approach, optimal policies with significant savings in inventory management effort are easily obtained for a large family of items.     

基于不同能量值特征优选的水声目标识别

采用Bartlet平均周期图、小波变换和经验模态分解方法分别提取信号的频段能量值特征、IMF能量值特征,重点对IMF能量值特征进行特征优选。通过设计BP神经网络分类器,对实测的四类舰船目标的辐射噪声信号进行测试,取得了较好的识别效果。