Classification of face images using local iterated function systems

There has been an increasing interest in face recognition in recent years. Many recognition methods have been developed so far, some very encouraging. A key remaining issue is the existence of variations in the input face image. Today, methods exist that can handle specific image variations. But we are yet to see methods that can be used more effectively in unconstrained situations. This paper presents a method that can handle partial translation, rotation, or scale variations in the input face image. The principal is to automatically identify objects within images using their partial self-similarities. The paper presents two recognition methods which can be used to recognise objects within images. A face recognition system is then presented that is insensitive to limited translation, rotation, or scale variations in the input face image. The performance of the system is evaluated through four experiments. The results show that the system achieves higher recognition rates than those of a number of existing approaches. The author would like to thank the Australian Research Council (ARC) which supports this research with a Discovery Grant.     

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of the SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem.     

不同种类支持向量机算法的比较研究

介绍一种新型的机器学习方法-支持向量机.论述了不同种类支持向量机算法并指出了每种算法的优劣.实验结果显示了核函数中选择合适的参数对分类器的效果是很重要的,通过实验还重点比较了Chunking、SMO和SVMlight三种典型分解算法,并分析了训练速度优劣的原因.文章最后给出了今后SVM研究方向的一些预见.     

弧齿锥齿轮数控加工和齿面接触点求解算法研究

在保证弧齿锥齿轮加工过程中刀盘相对于工件的运动关系不变的基础上,通过矩阵变换实现铣齿加工的数控展成.由于在数控加工每个插补周期中难以实现这种涉及大量运算的运动转换,因此将数控坐标轴的展成运动表示为以工件齿轮转角为参数的五次参数样条函数,保证了插补的实时性和精确性.文中将传统用于确定接触点位置时求解非线性方程组的问题,转化为带有3个设计变量的约束优化问题,简化了法线重合的条件,建立配对齿轮位置矢量相等的目标函数.最后给出实例对算法进行了说明和验证.     

SMS4算法S盒的密码学性质

S盒是分组密码的重要组成部分,在很大程度上决定了分组密码的安全性。该文研究了中国分组密码标准SMS4算法S盒的平衡性、差分性质、线性结构、非线性、Walsh谱等性质,通过与美国高级加密标准、欧洲分组加密标准Camellia的S盒作比较,说明了SMS4算法S盒一些较好的安全特性。     

基于遗传算法和模拟退火算法的TDOA定位技术

提出一种基于遗传算法与模拟退火算法的TDOA定位估计算法,该算法通过对求解定位坐标计算时的最大似然函数进行求解,实现了利用所有TDOA测量值对移动台的定位估计。该算法采用实数编码,自适应交叉率和变异率实现遗传算法的全局搜索,引入模拟退火的Boltzmann机制,解决遗传算法容易陷入局部最优的问题。实验结果表明,该算法定位精度高、收敛速度快。     

基于可复用功能组件的测试用例生成方法

针对交互式软件的频繁交互、状态复杂等特点,基于功能测试和层次型结构,从需求规范中提取基于操作流程的功能组件,通过映射、重组测试脚本,导入测试数据,并自动生成测试用例。功能组件的构建具有层次型结构的特点,即一个功能组件可以包含一个或多个功能组件,更大程度地共享了测试脚本。     

求解柔性作业调度的共生进化算法

在柔性作业处理系统中,运行操作的机器、操作运行顺序及完成特定加工的操作集等均可含有柔性,作业调度的最优性依赖流程设计的结果。该文在共生遗传算法求解此问题的基础上,定义了一种新的适应度函数,将个体所参与的所有组合解的算术平均值作为此个体的适应度。引进较优的遗传交叉方法。仿真结果证明,新的适应度函数表现优异,对给定的复杂调度问题得到了更好的解。     

核子类凸包样本选择方法及其SVM应用

提出一种基于核函数方法的类内训练样本选择方法——核子类凸包样本选择法,并将其用于支持向量机。该样本选择方法通过迭代方法,逐一选择了那些经映射后“距离已选样本”,并将其映射、生成“凸包最远的样本”。实验结果表明,该方法选择的少量样本使支持向量机获得了较高的识别比率,减少了存储需求,提高了分类速度。     

一般模糊极大-极小神经网络的研究与应用

分析一般模糊极大-极小神经网络的基本原理,阐述模糊计算方法在分类中的准确性和高效性。将一般模糊极大-极小神经网络应用于企业资信评估中,实现模糊区间的输入,缩小企业评估指标定量化中的误差范围。资信评估结果表明,该算法能快速、有效地对企业进行分类,为资信评估提供了解决方案。