Pattern recognition of collections

The task of simultaneously classifying a set of objects has unusual problems. Three categories of such problems are identified. Characteristics of context-dependent and context-independent classifications are considered. The effects of having parallepipeds or ellipsoids as decision regions are compared.     

A simple decomposition algorithm for support vector machines with polynomial-time convergence

Support vector machines (SVMs) are a new and important tool in data classification. Recently much attention has been devoted to large scale data classifications where decomposition methods for SVMs play an important role.So far, several decomposition algorithms for SVMs have been proposed and applied in practice. The algorithms proposed recently and based on rate certifying pair/set provide very attractive features compared with many other decomposition algorithms. They converge not only with finite termination but also in polynomial time. However, it is difficult to reach a good balance between low computational cost and fast convergence.In this paper, we propose a new simple decomposition algorithm based on a new philosophy on working set selection. It has been proven that the working set selected by the new algorithm is a rate certifying set. Further, compared with the existing algorithms based on rate certifying pair/set, our algorithm provides a very good feature in combination of lower computational complexity and faster convergence.     

Pattern classification of non-sparse data using optimal interpolative nets

In this paper we introduce two pattern classifiers for non-sparse data (i.e. data with overlapping class distributions) which use the optimal interpolative neural network (OI-net), derived by one of the authors based on a generalized Fock (GF) space formulation. We present a statistical pattern classifier operating as a two-stage algorithm. The first stage consists of a pre-processing operation involving a k-N N editing of the original training set T. The operation results in a new training set, T_e, which in the second stage is classified by an OI-net constructed by the recursive least squares algorithm. We also propose a new data specific classifier which has an additional third computational stage, in which samples of the original training set are added to the network piece by piece until satisfactory classification results are obtained. During the computation process the training set is iteratively updated until the number of mis-classified samples is minimized. The performance of these two classifiers has been evaluated in some illustrative examples.     

高性能油基切削液的试验研究(待续)——试件表层烧伤级别的模糊聚类分析

提出了一种利用试件侧表面烧伤色斑来判定试件表层磨削热损伤程度的方法、数学模型及其计算机软件．所建立的模糊聚类模式识别数学模型具有一定的通用性,适合于特征值连续递变的大样本的分级、分类,已被成功地用于切削液的使用效能评价．     

小波域红外能量图人脸识别中小波基的选取

小波域红外能量图人脸识别技术不但可实现全天候人脸识别,而且能够减少人脸识别中的数据复杂度,但是其识别率和时间花费与小波基的选取有关。为此对几种常用的小波基分别进行了实验,并对实验结果进行了分析和总结。实验结果表明,具有正交性和对称性的小波具有较高的红外人脸识别率。     

标准型女装衣身纸样的计算机辅助设计

我国目前使用的服装ＣＡＤ系统，主要用于推档、排料等方面，需要输入１：１的衣片纸样。但服装基本模型是具有普遍性的，只需根据尺寸，按一定的算法，即可得到标准纸样，如果和现行的服装ＣＡＤ系统结合，由衣身尺寸可直接得到排料图。本文以标准女装衣身基本纸样为例，讨论基本纸样的计算机辅助设计。     

基于后验概率SVM的交通标志识别研究

在无人车交通标志识别系统中,以传统的神经网络算法或标准的支持向量机算法（SVM）设计的分类器,只能反映样本是否属于某类而不能确定样本属于某类的可信度,提出一种后验概率SVM交通标志识别方法。首先对检测与跟踪得到的交通标志大概区域图像进行彩色分割以精确定位交通标志区域,然后采用最大类间方差法分割交通标志的内部图案,最后将分割的结果进行大小归一化作为交通标志的特征图像以训练分类器和进行识别。实验结果表明,基于后验概率SVM的交通标志识别系统在复杂的室外环境下具有很强的鲁棒性和可行性。     

复合钻井工况下牙轮钻头井底轨迹仿真分析及钻头改进

运用井底轨迹仿真软件生成复合钻井工况下牙轮钻头井底轨迹,寻找在复合钻井工况下,牙轮钻头齿槽磨损导致断轮尖的原因,提出适合于复合钻井作业的牙轮钻头解决方案。     

策略模式在虚拟校园开发中的应用

介绍了利用策略模式实现虚拟校园系统多样性的方法.初始的设计是基于继承的,通过父类和子类之间的重写和抽象实现.改进的设计应用了策略模式,区分出系统中会变化部分,获得了良好的可扩展性和可维护性,可以适应将来的升级要求.通过对比这两种设计方式,得出了利用策略模式的优缺点.     

计算机视觉技术在原木材积测量中的应用

文中简单介绍了计算机视觉技术的特点，详细阐明了它在原木材积测量中的应用，并指出了这项应用的特点及效果。