基于ART1神经网络的统计过程控制系统

统计过程控制(SPC)在改进过程水品、提高产品质量方面作出了巨大贡献.本文讨论了一种基于自适应谐振理论(ART)神经网络的SPC系统.与一般SPC系统相比,本系统不仅可以在线检测过程异常,对各种控制图异常模式还具有实时学习、在线识别功能.同时,本系统对过程的分析,无需如常规控制图一样,建立在正态假设的前提下,因此应用更方便、范围更广泛.作为一种新的SPC工具,ART1神经网络为改进控制图的应用提供了一种新的可能.     

基于视觉技术的Wire Bonding中焊点质量的自动检测方法

用WireBonder(引线键合机)进行键合后的芯片,其焊点可能会出现位置偏移、断线和线尾(Pigtail)过长等缺陷。该文针对上述几种质量问题,利用WireBonder现有的硬件,运用图象处理及模式识别技术,提出了一套自动视觉检测的方法。实验表明,该方法所需硬件低廉,操作方便,能很好地检测WireBonding中焊点的质量。     

基于统计模式识别的人脸检测方法综述

随着计算机科学的发展和社会的进步,人脸检测的研究和应用越来越得到广泛的关注。但是设计快速有效的人脸检测方法仍然是一个难度很大的问题。该文基于统计的模式识别分析了当前人脸检测的主要问题和方法,同时也给出了模式识别的概念、原理、方法。     

基于统计模式识别的岩石节理图像分割方法

岩石节理裂隙形状复杂,图像中含有很多噪声。而使用统计模式识别方法在分割图像时,可以首先使分类器学习图像中不同样本的特征,进而利用这些特征对图像中每个像素进行分类,实现分割。在设计统计模式识别的分类器时,我们提出使用基于核函数Fisher判别法构造分类器。使用该方法可以将图像高维的属性空间上的非线形判别转化为图像特征空间上的线形判别,而不需要知道从属性空间到特征空间的具体映射形式。通过对岩石节理裂隙图像分割实验表明,该方法能达到较其他方法更好的分割效果。     

机器智能与模式识别研究中的统计学习方法

简要介绍了学习算法的发展状况;讨论了机器智能与模式识别研究中的统计学习方法和图模型的一般理论;重点叙述了图模型的统计推断过程和学习算法以及应用统计学习方法解决问题的一般步骤;最后给出了用于时间序列分析的动态贝叶斯网络的实例.     

电梯群控系统的交通模式识别

介绍了应用两个模糊神经网络分两步进行电梯群控系统交通模式识别,提出了利用专家知识获取样本和训练网络的步骤,实际数据测试结果表明,该方法能准确地辨识出各种交通式所占的比例,对群控器的派梯可起到很好的指导作用,从而提高电梯群控系统的服务性能。     

基于SPC和神经网络的卷烟制丝生产质量监控方法研究

为解决卷烟制丝生产过程中现有SPC监控方法存在的问题,提出了基于SPC和BP神经网络的质量监控方法.首先在传统控制图的基础上,提出了适合在线监控的移动窗口式控制图,然后分别建立了用于控制图模式识别和质量缺陷原因诊断的两个神经网络模型,最后通过松散回潮工序中出口物料含水率的质量监控实例,证明了该质量监控方法的有效性.     

基于图像处理和模式识别的网络图像监控系统

随着科学技术和生活水平不断的提高,人们的财产和人身安全问题已经不仅仅来自于传统的经验,而是逐渐的转变到虚拟的网络当中;为此,提高人们的隐私安全和财产安全需要加强网络监控,其中图像监控是较为主要的方面;加强图像监控的可靠性和实时性,是实现社会稳步发展和人民网络隐私安全的首要目标;应用计算机网络技术和现代化通讯技术相结合,综合图像处理技术和模式识别技术的应用,设计应用于网络图像监控系统是当前社会发展的安全技术保障之一;文章通过对图像监控进行简析,阐述图像处理技术和模式识别技术的相关方面,探讨研究网络图像监控中图像识别和处理方面的技术;通过对图像处理中的色彩均化和模糊识别实验,得出其对于网络中传播的图片辨识度和提取信息能力具有很高的发展。     

Statistical Image Object Recognition using Mixture Densities

In this paper, we present a mixture density based approach to invariant image object recognition. To allow for a reliable estimation of the mixture parameters, the dimensionality of the feature space is optionally reduced by applying a robust variant of linear discriminant analysis. Invariance to affine transformations is achieved by incorporating invariant distance measures such as tangent distance. We propose an approach to estimating covariance matrices with respect to image variabilities as well as a new approach to combined classification, called the virtual test sample method. Application of the proposed classifier to the well known US Postal Service handwritten digits recognition task (USPS) yields an excellent error rate of 2.2%. We also propose a simple, but effective approach to compensate for local image transformations, which significantly increases the performance of tangent distance on a database of 1,617 medical radiographs taken from clinical daily routine.     

Adaptive Pattern Recognition

A long term goal of research in artificial intelligence is todetermine and to implement principles which permit a movable machine to direct its actions depending uponsensory feed-back from its environment. This paper concentrates onspatial sensors which input images (2-dimensional arrays). A proposal is put forward in which the machine adaptsto the actual data, and examplesare given of input prediction, of detection of unexpected events, andof recognition of spatial patterns.The image sequence is locally partitioned into temporally contiguous subsequences of afixed spatial extent. The spatial extent is constant over time and the temporal extent of a subsequence is maximizedsubject to the condition that the subsequence has occurred previously. The principle is illustrated on image sequences. It is further demonstrated on images which are structured as pseudo-temporal sequences of their rows. The demonstrations use diverse complex and simple examples to illustrate theversatility of the method. The demonstrations show that to a large degree it is not necessary for the user to supply explicit models for different patternrecognition tasks.     

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.     

基于图像处理的模糊控制埋弧焊缝跟踪研究

焊接工艺由于其本身加工过程及环境的复杂性,使得焊缝难以精确跟踪.针对埋弧焊系统,采用视觉传感器进行焊缝跟踪,得到误差信号后,使用分段拼接控制的方法,使误差快速收敛,且减小超调.首先对焊缝图像进行预处理,使用自动化的阈值进行分割得到焊缝区域,应用边缘检测和Hough变换得到焊缝边缘,计算得到焊缝中心位置、焊矩和焊缝偏差;然后采用模糊增量式分段PI控制处理误差,当误差较大时采用增量式PI控制消除误差,当误差较小时应用模糊控制,设计了隶属度表以及模糊推理机制,采用小误差的模糊控制有效地抑制了超调,使误差快速收敛.最终仿真结果显示,使用PI混合模糊控制后,超调量被控制在0.2个单位以内,调节时间小于1 s,基本实现了对焊缝的快速准确跟踪.     

模式识别与图象处理(PRIP)计算机发展评述

本文分析了促进模型识别与图象处理(PRIP)计算机发展的若干重要因素,评述了近年来发展的现状、特点及其趋势,并提出了结构分类的四种类型.这些探讨作为有益的借鉴,对于我国PRIP计算机的发展是有意义的.     

Pattern Recognition of Construction Bidding System Based on Image Processing

Bidding for construction projects is a very important and representative field in the industry. The system of bidding for construction projects has made considerable progress over the years due to the accumulation of experience and many contributions to the field. Nowadays, with the rapid development of information technology and the intellectualization of the bidding system for construction projects, the accumulation and processing of data has become an essential element of its development. In order to manage the bidding system of construction engineering reasonably, this paper proposes a system based on image processing and pattern recognition technology, which can recognize patterns in the bidding process of construction engineering. Firstly, through binarization and other methods, the bidding project documents of construction projects are preprocessed in order to extract the main information from the documents; secondly, information is obtained through pattern recognition and processing; finally, the results of pattern recognition are imported into the bidding system, and the bidding information is quickly added to the database. This paper proposes a bidding system for construction projects based on image processing and pattern recognition. The system provides a quick and convenient means of importing the bidding information and allows personnel to efficiently process the bidding documents, particularly when there are numerous documents and and too much information. Finally, the user survey shows that the proposed bidding system based on image processing and pattern recognition can reduce the workload of construction project personnel, greatly facilitating the progress of the project.     

基于模式识别的自整定控制及应用

本文提出一种基于模式识别的自整定控制方法,以模式表示和模式分类来描述系统的动态特性和调节器的结构,用模式识别优化方法求取调节器的参数,当系统动态模型参数未知时,这种自整定方法是建立在系统测量数据序列基础上的。该方法在实验性二元精馏塔上用IBM-PC微机获得成功应用。其结果优于常规PID调节器。     

Statistical Modeling of Colour Data

In this paper we investigate how best to model naturally arising distributions of colour camera data. It has become standard to model single mode distributions of colour data by ignoring the intensity component and constructing a Gaussian model of the chromaticity. This approach is appealing, because the intensity of data can change arbitrarily due to shadowing and shading, whereas the chromaticity is more robust to these effects. However, it is unclear how best to construct such a model, since there are many domains in which the chromaticity can be represented. Furthermore, the applicability of this kind of model is questionable in all but the most basic lighting environments.We begin with a review of the reflection processes that give rise to distributions of colour data. Several candidate models are then presented; some are from the existing literature and some are novel. Properties of the different models are compared analytically and the models are empirically compared within a region tracking application over two separate sets of data. Results show that chromaticity based models perform well in constrained environments where the physical model upon which they are based applies. It is further found that models based on spherical representations of the chromaticity data provide better performance than those based on more common planar representations, such as the chromaticity plane or the normalised colour space. In less constrained environments, however, such as daylight, chromaticity based models do not perform well, because of the effects of additional illumination components, which violate the physical model upon which they are based.     

基于奇异值特征和统计模型的人像识别算法

人像识别是模式识别领域中的一个前沿课题。目前多数研究者采用人脸的一维和二维几何特征来完成识别任务。人脸的几何特征抽取以及这些特性的有效性都面临着很多问题，至今人像识别的研究仍然处于较低的水平。作者证明了图象矩阵的奇异值特征矢量具备了代数上和几何上的不变性以及稳定性，提出用它作为识别人脸的代数特征。本文的人像识别算法是基于奇异值特征矢量建立Ｓａｍｍｏｎ最佳鉴别平面上的正态Ｂａｙｅｓ分类模型。在本文的     

基于双树复小波二元统计模型的图像去噪方法

为了更有效地进行图像去噪,提出了一种基于双树复小波二元统计模型的图像去噪方法,该方法先用带参数的二元广义高斯分布(GGD)来模拟原图双树复小波系数的统计分布;然后结合最大似然估计(MLE)得到优化的参数估计;最后在此先验分布的基础上,运用最大后验概率(MAP)来估计从噪声图的小波系数中恢复原图的系数,从而达到去噪的目的。实验表明该新方法不仅可以干净地去除图像的噪声,还可以有效地保留图像细节,取得了良好的去噪效果,尤其是去噪图像的视觉效果要明显优于目前的很多算法。     

适应智能质量控制的多分类支持向量机

分析了现有控制图识别器在实际应用中存在的缺陷，并提出了一种基于支持向量机(SVM)的新方法．为了克服HAH多分类SVM(HAH SVM)的缺陷，提高识别速度和准确率，设计了一种有针对性的SVM多分类器进行模式识别．仿真实验结果表明，该方法相对现有的BP和HAH SVM方法能得到更高的识别率和识别速度，适合于工序的实时在线控制．