Integrating independent component analysis and support vector machine for multivariate process monitoring

This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA–PCA and PCA–SVM.     

OWA rough set model for forecasting the revenues growth rate of the electronic industry

Business operation performance is related to corporation profitability and directly affects the choices of investment in the stock market. This paper proposes a hybrid method, which combines the ordered weighted averaging (OWA) operator and rough set theory after an attribute selection procedure to deal with multi-attribute forecasting problems with respect to revenue growth rate of the electronic industry. In the attribute selection step, four most-important attributes within 12 attributes collected from related literature are determined via five attribute selection methods as the input of the following procedure of the proposed method. The OWA operator can adjust the weight of an attribute based on the situation of a decision-maker and aggregate different attribute values into a single aggregated value of each instance, and then the single aggregated values are utilized to generate classification rules by rough set for forecasting operation performance.To verify the proposed method, this research collects the financial data of 629 electronic firms for public companies listed in the TSE (Taiwan Stock Exchange) and OTC (Over-the-Counter) market in 2004 and 2005 to forecast the revenue growth rate. The results show that the proposed method outperforms the listing methods.     

迭代学习的高速伺服系统位置控制策略

针对高速运动伺服控制系统的精确位宣跟踪控制问题,在A型迭代学习控制算法的基础上,提出了一种基于比例一预期迭代学习控制的位置控制策略,并将其应用于建立的交流永磁同步电机模型,最后利用Matlab进行仿真研究。结果表明,此控制策略仅需少量对象信息就能满足系统的性能要求。它与常见迭代学习控制算法相比,具有位置控制精度高、学习收敛速度快等优点。     

A5/1算法的概率模型分析

建立A5/1算法的概率模型,分析其中间状态序列的马尔可夫性、遍历性以及严平稳性,并计算A5/1算法的输出序列与相应的移位寄存器的输出序列和钟控后的输出序列之间的符合率。结果表明,各移位寄存器钟控后的输出序列不是独立的随机序列,A5/1算法的输出序列是均匀的独立随机变量序列,可抵御相关攻击。     

基于改进联合模型的人脸表情识别

在联合主动表观模型和主动形状模型的基础上,充分挖掘标定点之间的联系,提出一种局部纹理模型构建方法。通过改进匹配算法提高特征点的定位精度和匹配速度。将该算法提取到的人脸表情特征输入最近邻分类器,分类结果表明其识别率较高。     

On the design of multi‐rate tracking controllers: Application to rotorcraft guidance and control

This paper presents a new methodology for the design and implementation of gain‐scheduled controllers for multi‐rate systems. The proposed methodology provides a natural way to address the integrated guidance and control problem for autonomous vehicles when the outputs are sampled at different instants of time. A controller structure is first proposed for the regulation of non‐square multi‐rate systems with more measured outputs than inputs. Based on this structure, an implementation for a gain‐scheduled controller is obtained that satisfies an important property known as the linearization property. The implementation resembles the velocity implementation for single‐rate systems. The method is then applied to the problem of steering an autonomous rotorcraft along a predefined trajectory defined in terms of space and time coordinates. By considering a convenient error vector to describe the vehicle's dynamics, the trajectory tracking problem is reduced to that of regulating the error variables to zero. Because of the periodic multi‐rate nature of the onboard sensor suite, the controller synthesis is dealt with under the scope of linear periodic systems theory. Simulation results obtained with a full non‐linear rotorcraft dynamic model are presented and discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

浅谈C#程序中的异常处理机制

程序设计中,异常是一个不可忽略的元素,因此,有效的异常处理是程序完善的重要标志。重点介绍了C#语言中的异常处理机制。主要包括异常的基础知识、异常处理机制、程序设计中的异常三个部分。异常基础知识部分,讲解了什么是异常,异常发生的情况及主要的异常类,使读者对程序异常有基本的认识。异常处理机制部分,从应用程序和理论两方面对C#语言中对异常的处理机制进行讲解,详细分析了三种异常处理语句。程序设计中的异常部分,通过一个简单的程序开发中的例子扩展异常机制在程序设计中的应用,拓宽软件开发人员的设计思路。     

基于城市文化的城市礼品设计研究

2010上海世博会首次将"城市"作为主题,为城市未来的发展提供多种新的思考方式。城市文化是城市主题中的一个核心要素,各个城市都在为如何审视自己城市文化的内涵特征、呈现方式及传播方式进行探讨。在此背景下,城市礼品作为城市文化载体,越来越受到人们的关注。文章通过对城市礼品设计相关的理论学习和对设计实务的调查分析,利用设计管理思维技术的框架,开发出一件基于城市文化进行城市礼品设计的应用型设计工具,以期提升城市礼品设计的功能和价值。     

大型低速柴油机装配工艺专家系统的设计

运用专家系统的知识,将人工智能、网络数据库、软件工程等理论与技术应用到低速柴油机装配工艺编制设计中,建立了一个能进行柴油机装配工艺编制和管理的专家系统应用程序基本框架,实现了专家系统的主要功能。     

基于LabWindows/CVI的电能质量分析仪

针对传统电能质量分析系统存在存储容量小、显示功能单调等缺点,设计了以高性能数据采集卡PCL-818H与工业控制计算机为核心的硬件系统。该系统对实时信号进行采集与分析,并利用虚拟仪器LabW indows/CVI的强大测试分析功能,完成了基波和谐波的幅值与相位、电压偏差、有功和无功畸变率等参数计算,且具有数据存盘的软件功能。通过反复验证,该分析仪具有精度较高、直观易读、工作可靠和性价比高等特点。