夹具六点定位原理在产品设计自动化中的应用

为解决产品设计自动化的问题,在分析用于夹具设计的六点定位原理的基础上,提出了广义定位原理.基于广义定位原理及分解重构等理论,构造了产品自动化设计理论模型,并进行了软件系统的实现.     

锥拟凸映射

利用Minkowski泛函，文中定义了一类新的锥拟凸函数（广义锥拟凸，广义严格拟凸和广义强锥拟凸），获得了相应的性质，讨论了这类映射与以往拟凸映射的关系．     

广义垂足三角形的面积关系

由于各种文献的差异,在本文中广义垂足三角形定义为：以锐角三角形内任意一点在其三边上的射影点为顶点的三角形称为该点的广义垂足三角形．例如,我们知道三角形的三条高交于一点（垂心）,以三条高的垂足为顶点的三角形,即是垂心的广义垂足三角形．     

Generalized Sampling Series Approximation of Random Signals from Local Averages

Signals are often of random character since theycannot bear any information if they are predictable forany timet,they are usually modelled as stationary ran-dom processes. Balakrishnan[1]and Lloyd[2]investiga-ted the Shannon sampling on stationary randomp…     

一种基于广义奇异值分解的无关联线性判别分析算法

有监督学习旨在样本数据集中找到最优判决向量。线性判别分析（LDA）和无关联线性判别分析（ULDA）是解决该问题的常用方法。研究中改进了古曲LDA方法使其与ULDA等价,并给出了相应求判决向量的ULDA/QR算法来简化ULDA中对判决向量的求解;为了有效地解决LDA方法和ULDA方法中类内散布矩阵奇异性的问题,提出了一种基于ULDA/QR,正则LDA和广义奇异值分解（GSVD）的无关联线性判别分析算法。     

计算智能的研究与思考

通过对近20年来基于连接机制和基于进化机制的人工智能形式体系所 取得的成就和存在的问题进行了系统的回顾,就目前引起学术界广泛关注的一些热点问题进 行了剖析,探讨了这些热点问题产生的根源,并提出了一些新的研究思路和解决方案。在此 基础上,对该领域的未来发展趋势进行了预测。     

混合模型中的广义置信域

讨论同方差及异方差下的平衡混合效应模型中的推断。利用推断方法,导出了和该模型中固定效应相关的广义置信域和广义p值。模拟结果表明,该置信域有满意的表现。     

基于MUGG的轨迹建模与异常检测

构建视频场景中目标轨迹分布的概率模型--混合单边广义高斯模型,通过计算目标轨迹的信息量分析目标轨迹是否异常.该方法不依赖场景的先验知识,模型建立过程无监督,且模型能实时更新以适应时变环境.实验表明,该方法的有效性和鲁棒性,具有一定的应用价值.     

单向S-粗模糊集

结合模糊集理论和单向S-粗集理论,基于动态系统提出了单向S-粗模糊集的概念、结构及其性质,对此进行了讨论并给出应用实例。     

Iris Localization Algorithm Based on Improved Generalized Symmetry Transform

Accuracy and fastness of iris localization are very important in automatic iris recognition. A new fast iris localization algorithm based on improved generalized symmetry transform (GST) was proposed by utilizing iris symmetry. GST was improved in three aspects: 1 ) A new distance weight function is defined. The new weight function, which is effective in iris localization, utilized the characteristic of irises that the iris is a circular object and it has one inner boundary and one outer boundary. 2) Each calculation of the symmetry measurement of a pair of symmetry points was performed by taking one point of a pair as the starting point of the transformation. This is the most important reason for fast iris localization, due to which, repetitious computation was largely excluded. 3) A new phase weight function was proposed to adjust GST to locate circle target much better because the inner part of iris is darker than the outer part. The edge map of iris image was acquired and GST was only implemented on the edge point, which decreased computation without loss of accuracy. The modification of distance weight function and phase weight function leads to the accuracy of localization, and other ideass peed up the localization. Experiments show that the average speed of new algorithm is about 7.0-8.5 times as high as traditional ones including integro-differential operator and Hough transform method.