图形用户界面的回归测试方法研究

对图形用户界面(GUI)进行回归测试是一项困难的工作,因为当GUI的结构稍作修改则可导致大量的测试用例不可再用.给出一种GUI的回归测试方法,介绍该方法的思想及GUI回归测试器模型.由于该方法可对GUI修改前所用的测试用例进行选择、修复,使之变为可再用的测试用例,因此可以减少开销,提高GUI的回归测试效率.     

基于椭圆曲线的一种高效率数字签名

为给出一种基于椭圆曲线密码的高效率的数字签名方案.不仅在算法设计时完全避免了费时的求逆运算,而且利用消息HASH值的汉明重量作为消息摘要进行签名与验证.结果在同等安全性下,该方案比通用的ECDSA等方案运行时间更短.新方案可适用于网络等对签名实时性要求较高的场合.     

基于ADS的LFM脉压雷达系统建模与仿真

指出雷达系统基于传统仿真软件建模与仿真时的缺点;阐述了ADS(Advanced Design System)软件在雷达系统仿真中的优势;然后建立一个基于ADS软件平台的雷达系统仿真模型库.当对某种雷达系统仿真时,只需根据要求修改各个模块的参数或更换子模块即可准确迅速地搭建起系统仿真模型,大大缩减了建模所花费的时间和人力物力.以一个线形调频脉冲压缩雷达系统建模的过程为例,对ADS软件在雷达系统仿真中的应用加以介绍,并给出最终仿真结果.     

不同功能单体对分子印迹聚合物识别性能的模拟

以槲皮素为模板分子,以甲基丙烯酸(MAA)、三氟甲基丙烯酸(TFMAA)、丙烯酰胺(AM)、N-乙烯基吡咯烷酮(NVP)、2-乙烯基吡啶(2-VP)、4-乙烯基吡啶(4-VP)六种物质分别作为功能单体,运用量子化学方法模拟模板分子与不同功能单体的分子印迹聚合物预组装体系的构型、能量及复合反应的结合能△E,以讨论不同功能单体对分子印迹聚合物识别性能的影响,结果4-VP的印迹效应最好.通过模拟,有助于解释实验现象以及选择印迹效应更好的功能单体.     

基于粗糙集神经网络的燃煤发热量预测模型

通过对粗糙集和BP神经网络的分析研究,以专家系统为核心,提出了一种基于粗糙集神经网络的燃煤发热量预测模型;选取影响燃煤发热量的6个参数,利用粗糙集理论对原始信息表进行约简操作,去除冗余的属性和属性值,得到约简规则,并将其作为BP神经网络的输入,对燃煤发热量进行预测;通过分析对比线性回归方法和粗糙集神经网络方法,说明该模型能有效地简化神经网络的网络结构,减少网络的训练步数,提高网络的学习效率,能够较准确地对燃煤发热量进行预测。     

基于半实物仿真的电路板故障注入系统设计与实现

为了满足某型装备电路板测试性与诊断设计评价的需求,提出了一种基于DSP技术,利用半实物仿真方式实现的故障注入系统;该系统以TMS320C5416系列DSP器件为核心器件,利用CPLD扩展外部电路,主控计算机与该系统的通信通过USB接口实现,在故障注入时通过半实物仿真模式实现对电路板典型故障的注入;通过对典型电路的故障注入试验表明,该系统可靠性高,配置灵活,使用方便;目前该系统已经在工程实践中得到应用,效果明显,对提高电路板测试性设计水平具有重要意义。     

超高速水下航行器航向控制研究

超高速水下航行器在水下航行时,表面大部分被超空泡所包覆,其运动特性发生显著变化,使得其工况变得更为复杂、控制难度加大;通过分析它的运动特点,建立了系统模型方程,并提出把空化器作为控制面,通过操纵其舵角改变量来控制航行器航向;最后运用PI极限舵控制算法进行仿真分析,结果表明此控制方法简单易实现,产生的偏差小,同时还提高了系统的响应速度,较好地满足了系统要求,分析结论可为超高速水下航行器控制系统设计提供参考。     

Computational simulation of Belousov–Zhabotinskii oscillating chemical reaction

The suitability of computational simulation of the Belousov–Zhabotinskii oscillating chemical reaction by differential kinetic methodology for resolving nonlinear multi-component system is demonstrated in this work. According to the Field–KÖrÖs–Noyes mechanism and the Oregonator model, the change of the concentrations of HBrO₂, bromide ion and cerium ion are simulated. The results of computational simulation are consistent with experimental results very well. At the same time, the effect of variables and parameters, especially the rate constant on the oscillation curve, are investigated deeply. A simple method of estimating rate constants is obtained through simulation the concentrations of key components of the system, and then comparison the simulation results with the experimental ones. The reasonable rate constant is also proposed.     

Stereo-Based Stochastic Mapping for Robust Speech Recognition

We present a stochastic mapping technique for robust speech recognition that uses stereo data. The idea is based on constructing a Gaussian mixture model for the joint distribution of the clean and noisy features and using this distribution to predict the clean speech during testing. The proposed mapping is called stereo-based stochastic mapping (SSM). Two different estimators are considered. One is iterative and is based on the maximum a posteriori (MAP) criterion while the other uses the minimum mean square error (MMSE) criterion. The resulting estimators are effectively a mixture of linear transforms weighted by component posteriors, and the parameters of the linear transformations are derived from the joint distribution. Compared to the uncompensated result, the proposed method results in 45% relative improvement in word error rate (WER) for digit recognition in the car. In the same setting, SSM outperforms SPLICE and gives similar results to MMSE compensation of Huang A 66% relative improvement in word error rate (WER) is observed when applied in conjunction with multistyle training (MST) for large vocabulary English speech recognition in a real environment. Also, the combination of the proposed mapping with CMLLR leads to about 38% relative improvement in performance compared to CMLLR alone for real field data.