基于时段的间歇过程统计建模、在线监测及质量预报

首先针对基于多元统计技术的间歇过程统计建模、在线监测、故障诊断及质量预测等热点问题进行了论述, 回顾了各类方法的发展, 并分析了各自的优缺点. 接下来重点针对间歇工业过程多时段及时段过渡特性, 详细介绍了基于时段的统计分析策略, 分析了各时段的潜在过程行为及其对产品质量的影响与作用关系, 探讨了该思想方法的本质依据, 揭示了其研究价值和重要意义. 最后从解决实际问题的角度出发, 发掘了其存在的潜在问题及今后的研究前景与发展空间. 基于时段的间歇过程多元统计分析是一个既有理论意义又有较高实际应用价值的研究课题, 必将有利于后续的过程监测、故障诊断及质量改进.     

基于多时段MPCA模型的间歇过程监测方法研究

针对间歇过程的多时段特性, 提出一种新的生产操作时段划分方法. 该方法利用反映过程特性变化的主成分个数、负载矩阵以及主成分矩阵的变化实现间歇过程子时段的三步划分. 根据各时间片主成分个数不同, 对生产操作时段进行粗划分. 为了更客观地反映负载矩阵以及主成分矩阵的相似性, 提出了基于加权负载向量夹角余弦的负载矩阵相似度度量以及基于加权主成分欧氏距离的主成分矩阵相似度度量方法. 以相似度最小原则, 对时间片矩阵进行奖惩竞争聚类, 进而实现了生产操作子时段的细划分. 将基于改进时段划分方法的MPCA建模应用于注塑成型过程在线监测, 实验结果验证了该方法的有效性.     

间歇过程滑动窗口子时段PCA建模和在线监测

针对在短期内不容易获得充足建模数据的间歇工业过程,提出一种间歇过程监视方法,该方法只需要一次正常间歇操作数据,利用滑动窗口,进行子时段划分、建立初始主元分析(PCA)监测模型,同时提出了基于子时段PCA模型的在线监测算法和一种模型更新策略.通过在注塑过程的成功应用,表明了所提出方法的可行性和有效性.     

基于KECA和FWA-SVM的间歇过程分时段故障诊断方法

针对间歇过程的高度复杂性、强非线性、强时段性等特点,提出一种基于核熵成分分析（KECA）特征变量降维,利用烟花算法（FWA）优化支持向量机（SVM）参数的间歇过程分时段故障诊断方法。首先,通过多向核主元分析（MKPCA）进行在线故障监测,输出故障数据;其次,利用K-means分类方法将间歇过程划分为若干个子时段,对故障数据进行KECA特征变量处理,按熵值贡献率来确定选取主元的个数,深层提取特征信息;最后,在各子时段内分别构建FWA优化SVM参数故障诊断模型,将降维处理后的故障数据代入各自所属子时段FWA-SVM诊断模型内进行故障诊断。通过对青霉素仿真实验数据进行各种对比实验研究,验证了该方法的可行性与有效性。     

基于核独立成分分析的人脸识别研究

在人脸识别中提出一种基于非线性子空间的核独立成分分析(KICA)方法。在简单介绍了ICA方法的基础上,对KICA方法的基本原理和算法作了较为详细的描述。为了验证基于KICA和ICA的人脸识别方法的识别效果,进行了对比实验和分析。实验和分析结果表明,在人脸识别中,基于KICA的方法优于基于ICA的方法。     

基于瞬时频率响应函数的间歇过程时段划分

多时段特性是间歇过程的本质特性之一,对间歇过程实现有效的时段划分是故障监测的基础。传统的时段划分方法大多针对过程的输入输出数据,对输入输出数据突变较为敏感。本文提出一种基于瞬时频率响应函数的间歇过程时段划分方法,该方法基于系统的瞬时动态特性,用瞬时频率响应函数替代输入输出数据进行时段划分,利用小波变换估计系统的瞬时频率响应函数进行核主元分析降维,通过模糊C均值聚类对降维后频率响应函数进行聚类划分时段。实验结果表明,本文所提出的方法能够实现对间歇过程的时段划分,并具有较高的鲁棒性。     

基于子时段MPCA-SVM的间歇过程在线故障诊断

针对基于支持向量机(Support Vector Machine,SVM)的间歇过程故障诊断准确率低的问题,结合间歇过程的时段特性,提出了一种基于子时段MPCA-SVM的间歇过程在线故障诊断方法。首先,利用多向主成分分析(Multi-way principal component analysis,MPCA)提取出间歇过程正常运行状态下的每个采样点的主成分,将相邻的且具有相同主成分个数的采样点归到同一粗划分时段内,再在每一个粗时段内利用相邻采样点的负载矩阵的角度信息作为相似性判据来细化分时段;其次,对每个时段建立MPCA在线过程监测模型,同时,利用MPCA提取每个时段内各个类型故障的特征,并用特征数据建立SVM故障诊断模型;最后,MPCA监测模型实施监测功能,当检测到故障时,相应时段的SVM故障诊断模型进行诊断。将该方法应用于青霉素发酵过程仿真平台进行验证,该方法相比于不分时段的SVM的故障诊断方法,平均可提高故障诊断准确率11%,实验结果表明了该方法的有效性和可行性。     

基于核独立成分分析和BP网络的人脸识别

用基于非线性子空间的核独立成分分析方法（KICA）对人脸图像进行特征提取,用三层的BP网络作为分类器,对人脸进行识别。在简单介绍基本的独立成分分析（ICA）的基本原理的基础上,对KICA的原理和算法作了详细的描述,并详细介绍了三层BP网络的设计。最后为了验证KICA+BP网络的效果,进行对比实验和分析。实验和分析的结果表明,在人脸识别中,该方法的效果明显好于其它方法。     

基于分布式集成学习的入侵检测模型

针对入侵检测系统的高漏报率及高误报率问题,提出一种混杂入侵检测模型。该模型分别构造基于核主成分分析(KPCA)和核独立成分分析(KICA)的特征提取器,并采用集成学习对特征提取结果进行整合学习。采用分布式神经网络对集成结果进行再学习,从而实现对大规模数据的分布式处理。通过反馈机制调节KPCA和KICA的集成学习权重,达到最优检测效果。采用KDD CUP’99数据集进行测试实验,结果表明：该模型能够获得较高的检测正确率,同时具有较低的漏报率及误报率。     

基于核主元分析–主元分析的多阶段间歇过程故障监测与诊断

具有过渡特性的多阶段间歇过程故障监测是一个复杂的问题,既需要考虑稳定阶段下的故障监测,也需要考虑不同阶段间的过渡故障监测.为克服传统硬划分方法导致误警和漏报率高的缺陷,同时也为实现更加精确、有效的故障监测与诊断,提出一套完整的基于核主元分析-主元分析(KPCA-PCA)的多阶段间歇过程故障监测与诊断策略.该方法依据数据相似度实现阶段划分,定义模糊隶属度辨识相邻阶段间的过渡,最后对稳定阶段和过渡过程分别建立具有时变协方差的PCA和KPCA故障监测与诊断模型.通过对青霉素发酵过程的仿真平台及工业应用研究表明,该方法具有更可靠的监控性能,能及时、准确的检测出过程中存在的异常情况.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.