控制流图上支配关系计算方法的分析与实现

支配关系在数据流分析和静态单赋值等程序分析和优化中应用很广泛.采用位向量表示支配结点集合,描述了采用迭代法计算控制流图上支配结点集合的算法,在支配结点集合的基础上讨论了对直接支配结点、支配边界结点的计算方法,并在NPB和SPEC2000测试集上进行了测试.测试结果表明:控制流图的构建占用了过程内支配关系计算的几乎一半时间;对于不包含goto语句的结构化程序,迭代算法一般只需迭代2次.     

基于加权模糊支持向量描述的旋转机械故障分类

基于支持向量数据描述良好的分类性能,针对旋转机械故障诊断中故障样本获取的特点,提出了基于正负类样本的加权模糊支持向量数据描述多类分类器,不仅考虑了正类样本,而且也充分考虑了负类样本对分类结果的影响.利用模拟故障样本对系统进行了实验,结果表明提出的方法在系统中具有良好的分类能力.     

一种新颖隶属度函数的模糊支持向量机

传统的支持向量机(SVM)训练含有外部点或噪音数据时,容易产生过拟合(over-fitting)。通过模糊隶属度函数来降低外部点或被污染数据的选择。本文提出了一种新的核隶属度函数,这种新的隶属度函数不仅依赖于每个样本点到类型中心的距离,还依赖于该样本点最邻近的K个其他样本点的距离。实验结果表明了具有该隶属度函数的模糊支持向量机的有效性。     

永磁同步电机空间矢量控制方法设计实现

针对永磁同步电机结构复杂、模型非线性从而导致难以控制的问题,通过推导建立了其基于矢量控制下的简化的数学模型,仅通过控制电机交轴电流就可对其进行控制.在此基础上,对交轴电流进行调节,将其转换为基于两相静止坐标系下的电压矢量,设计了以两相静止坐标系下的参考电压作为输入变量的空间矢量控制方法,借助于数字信号处理器(DSP)的强大功能对系统进行实现,给出了所设计的硬件结构图及软件流程图,并进行了仿真验证,且效果良好,证明了所建模型的正确性和系统的可行性.     

SVPWM教学研究之重点解析

SVPWM是电力电子技术教学中的重要内容,本文对空间矢量调制的教学重点做了一个较为全面系统的梳理。重点阐述SVPWM的核心思想,特别是三相正弦交流电SVPWM的基本原理,严格证明了三相电压的空间矢量相等与线电压相等等价。同时,证明在SVPWM下,逆变器输出的最大圆为基本空间矢量构成的正六边形的内接圆、直流母线电压利用率提高了约15.4%。     

略谈平面设计中的触觉体验

本文通过对触觉设计这一设计理念的论述,探讨材质在平面设计中的表现以及如何在平面设计中合理的运用触觉感受来传达信息,提高设计品质。     

平面设计教育现状与展望

我国的平面设计专业教育面临着新的形势,在新的形势下我们高等教育的从业者应该怎样面对这些变化,我们应该培养出什么样的学生?这就是本文要探求的内容。     

Modeling wine preferences by data mining from physicochemical properties

We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.