基于谱直方图与支持向量机的人脸检测

提出了一种基于谱直方图和支持向量机的人脸检测算法。首先使用梯度滤波器、LoG滤波器和LBP算子计算图像的谱直方图,然后使用支持向量机进行分类。经实验表明,算法可以克服光照、姿势、表情、眼镜等干扰,并允许有局部遮挡,具有较强的鲁棒性。     

基于SQL Server2008决策支持系统模型的研究和应用

分析了决策支持关键技术之间的关系,提出了一个基于SQL SERVER 2008的决策支持系统模型,并以该模型为参考开发了珠宝行业决策支持系统。     

基于ARM9-Linux的注塑机伺服控制系统

全电动注塑机的控制系统任务繁多且要求实时性强。结合全电动注塑机的工艺特点,提出了以ARM9处理器为核心的全电动注塑机硬件系统,并利用嵌入式操作系统Linux提供的多进程多线程通信机制进行软件设计,从而满足全电动注塑机的各种伺服控制要求。     

基于模糊控制的纸机蒸汽冷凝水控制系统

针对造纸干燥部的工况多变、选用传统控制方式不易控制的情况,设计了一种基于模糊PID算法设计的S7-300控制系统,介绍了理论上和实际组态、编程的实现方法。该系统鲁棒性强,可适应复杂工况,实际应用结果表明该系统算法合理、易实现,控制效果好。     

网络信息两层过滤系统的设计

随着Internet的普及和应用,网络应用在人们生活中占据越来越大的比重,对网络中的信息进行实时过滤,具有十分重要的意义。本文对贪婪随机自适应算法(GRASP)进行扩展,得到基于权值的贪婪随机自适应算法(WGRASP),利用WGRASP、多模式匹配分类、支持向量机等算法构造了一个网络信息两层过滤系统。该系统能自动获取一个优良的非法关键词集合,且对网络信息的过滤具有较高的过滤速度和文本识别查准率、查全率。     

模糊自整定PID在电机调速系统中仿真研究

针对传统直流双闭环调速,不能有效克服非线性因素,不能满足对高精度、高性能等场合的要求。本文提出直流电机调速控制系统采用模糊自整定PID控制策略,从而实现PID参数实时整定,并介绍了控制器的整个设计过程。根据一个实际电机参数模型在Matlab/Simulink中搭建仿真模型,仿真结果表明模糊自整定PID的控制器比传统PID更具有好的控制精度和鲁棒性,并提高了电机动、静态性能。仿真结果与理论研究相符,验证了方案设计的合理性,实现了被控对象的最佳控制。     

在线旅游业务中Web页面主体块提取方法研究

Web信息提取是在线旅游业务的重要技术。页面的主体语义块集中了最主要的信息量,它的正确提取是Web信息提取的基础。本文在对现有页面分割方案总结的基础上,提出了结合机器学习方法的Web页面主体语义块节点识别算法,并基于启发式规则对正结果集进行后续的校验,以定位最佳的主体语义块节点。通过实验,本文提出的方案达到了比较理想的准确率。     

A rough margin based support vector machine

By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers. Similar to the classical SVM, the RMSVM searches for the separating hyper-plane that maximizes the rough margin, defined by the lower and upper margin. In this way, more data points are adaptively considered rather than the few extreme value points used in the classical SVM. In addition, different support vectors may have different effects on the learning of the separating hyper-plane depending on their positions in the rough margin. Points in the lower margin have more effects than those in the boundary of the rough margin. From experimental results on six benchmark datasets, the classification accuracy of this algorithm is improved without additional computational expense compared with the classical ν-SVM.     

Spacebased estimation of moisture transport in marine atmosphere using support vector regression

An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere (Θ) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data (Θ calculated from rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of Θ derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation compared with rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed.     

Predicting defect-prone software modules using support vector machines

Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models.