图形技术在系统仿真中的应用

本文首先从应用对系统仿真提出的要求出发，讨论了图形技术在系统仿真中的地位及应用范围，然后着重介绍了系统仿真中的动画仿真和图形建模技术，提出了实现动画仿真和图形建模必须解决的关键技术问题，并结合ＩＭＳＳ给出了实现动画仿真和图形建模的一种方法。     

自定义特征的管网图形建模研究*

提出用自定义特征的方法设计管网图形模型,提供了新元件设计的接口,需要对元件进行扩展建模时,用户可先自定义生成特征元件进入特征库,再进行管网图形建模设计。该方法有效地解决了管网图形建模的可扩展性问题。系统采用面向对象技术来实现,以提高程序的移植性与通用性。实例研究表明,该方法实现的系统具有良好的接口与方便的用户操作,是一种有效的管网图形建模方法。     

OpenGL在基于图形建模技术中的应用

介绍了传统的虚拟现实场景建模技术--基于图形的建模技术,说明了图形建模、图形变换、纹理映射等在OpenGL中的实现方法.     

虚拟现实技术综述与基于Internet的虚拟培训系统设计

文章对虚拟现实技术的发展和建模方法--基于图形建模和基于图像建模两种方法的原理及实现技术进行综述,给出了在Internet上实现虚拟现实的方法,及多种软件开发平台的特点和比较,从而设计了一个远程职业技术虚拟现实系统的模型。     

交互式实时水面渲染*

介绍了一种利用可编程图形硬件来实现水面实时渲染的方法。该渲染过程分为两个阶段,即水面建模和光照实现。通过当前图形硬件新提供的顶点纹理技术来对水面进行建模,并结合环境纹理映射技术和二维纹理映射技术实现了水面上的反射与折射等光照现象。实验证明,该方法大大提高了渲染速度,增强了水面渲染的交互性和实时性。     

交互式实时水面渲热

介绍了一种利用可编程图形硬件来实现水面实时渲染的方法。该渲染过程分为两个阶段,即水面建模和光照实现。通过当前图形硬件新提供的顶点纹理技术来对水面进行建模,并结合环境纹理映射技术和二维纹理映射技术实现了水面上的反射与折射等光照现象。实验证明,该方法大大提高了渲染速度,增强了水面渲染的交互性和实时性。     

Managed DirectX三维建模技术中的类实现

三维建模技术是进行三维仿真的基础,使用高级语言和图形接口进行三维建模的开发是根本的解决方法。针对目前.NET技术的快速发展与应用,采用.NET语言和Managed DirectX技术进行开发,对基于Managed DirectX的三维建模技术中的三维模型类进行了研究及实现,并在.NET环境下开发了三维模型基类库,其中使用了参数化建模方法。最终实现了基本的三维建模类,在此基础上可进一步开发出通用的三维建模器。Managed DirectX作为DirectX中重要的成员,为基于.NET框架开发三维图形应用程序提供了极大的方便。     

基于SVG的电网故障处理自动化系统

将SVG运用于10 kV电网故障定位、隔离与恢复系统的网络建模与表达,通过研究电网SVG图形描述和设备图元建模,提出基于坐标和节点融合的设备连接关系生成方法,实现基于设备模型的拓扑自动生成、完整性检查和故障信息的容错,设计并实现系统SVG图形Web发布方法。现场运行取得了良好的效果。     

电站仿真建模用图形编辑器－GEDITOR

电站仿真图形建模环境的一个重要工具是方便直观的图形编辑器。编辑器的方便与否很大程度上决定了整个图形建模环境的方便程度。本文介绍了电站仿真用图形编辑器ＧＥＤＩＴＯＲ的总体功能、开发以及实现。该编辑器目前已应用在电站仿真图形建模系统中，能够满足一般图形编辑功能以及电站仿真模型开发所要求的特殊功能。     

一个可配置的可视化建模工具

可视化建模语言是采用图形方式对系统／软件进行描述的语言，可视化建模工具为其提供了开发环境。不同的建模工具存在的共性，使得开发一种通用的可配置的可视化建模工具成为可能。基于理论研究和工具开发的实践经验，提出定义了一套支持静态语义的可视化建模语言描述方法，并且设计实现了一个从语义、语法、图形到编辑器界面可配置的可视化建模工具。     

基于在线学习的目标跟踪方法研究*

针对视频目标跟踪问题,提出了一种基于co-training框架下的在线学习跟踪方法。该方法首先根据两种不同的局部特征,利用在线 Boosting算法分别建立模型, 然后采用co-training框架来协同训练,有效避免了模型误差累积和跟踪丢帧等问题。实验证明了该方法的有效性。     

Fuzzy-UCS: A Michigan-Style Learning Fuzzy-Classifier System for Supervised Learning

This paper presents Fuzzy-UCS, a Michigan-style learning fuzzy-classifier system specifically designed for supervised learning tasks. Fuzzy-UCS is inspired by UCS, an on-line accuracy-based learning classifier system. Fuzzy-UCS introduces a linguistic representation of the rules with the aim of evolving more readable rule sets, while maintaining similar performance and generalization capabilities to those presented by UCS. The behavior of Fuzzy-UCS is analyzed in detail from several perspectives. The granularity of the linguistic fuzzy representation to define complex decision boundaries is illustrated graphically, and the test performance obtained with different inference schemes is studied. Fuzzy-UCS is also compared with a large set of other fuzzy and nonfuzzy learners, demonstrating the competitiveness of its on-line architecture in terms of performance and interpretability. Finally, the paper shows the advantages obtained when Fuzzy-UCS is applied to learn fuzzy models from large volumes of data.     

Inferential estimation of kerosene dry point in refineries with varying crudes

A bootstrap aggregated model approach to the estimation of product quality in refineries with varying crudes is proposed in this paper. The varying crudes cause the relationship between process variables and product quality variables to change, which makes product quality estimation by soft-sensors a difficult problem. The essential idea in this paper is to build an inferential estimation model for each type of feed oil and use an on-line feed oil classifier to determine the feed oil type. Bootstrap aggregated neural networks are used in developing the on-line feed oil classifier and a bootstrap aggregated partial least square regression model is developed for each data group corresponding to each type of feed crude oil. The amount of training data in crude oil distillation is usually small and this brings difficulties for classification and estimation modelling. In order to enhance model reliability and robustness, bootstrap aggregated models are developed. The inferential estimation results of kerosene dry point on both simulated data and industrial data show that the proposed method can significantly improve the overall inferential estimation performance.     

ARX过程突发性故障的检测与辨识

在分析突发性故障对带外生变量自回归过程(ARX)影响的基础上，本文建立了脉冲 型故障检测与故障幅度辨识算法，提出了采用脉冲型故障的检测与辨识方法处理阶跃型故障 的技术途径，并通过仿真计算验证了本文方法的有效性．     

Soft sensing modelling based on optimal selection of secondary variables and its application

The composition of the distillation column is a very important quality value in refineries, unfortunately, few hardware sensors are available on-line to measure the distillation compositions. In this paper, a novel method using sensitivity matrix analysis and kernel ridge regression （KRR） to implement on-line soft sensing of distillation compositions is proposed. In this approach, the sensitivity matrix analysis is presented to select the most suitable secondary variables to be used as the soft sensor＇s input. The KRR is used to build the composition soft sensor. Application to a simulated distillation column demonstrates the effectiveness of the method.     

基于网络化新闻回传及媒资共享的应用

随着信息技术在广播电视领域的发展,实现新闻回传网络化,媒资系统共享化已经成为可能。文章针对我台实际需求设计并建成一套完整的新闻回传与媒资共享系统进行了说明。     

On-line trust: concepts, evolving themes, a model

Trust is emerging as a key element of success in the on-line environment. Although considerable research on trust in the offline world has been performed, to date empirical study of on-line trust has been limited. This paper examines on-line trust, specifically trust between people and informational or transactional websites. It begins by analysing the definitions of trust in previous offline and on-line research. The relevant dimensions of trust for an on-line context are identified, and a definition of trust between people and informational or transactional websites is presented. We then turn to an examination of the causes of on-line trust. Relevant findings in the human–computer interaction literature are identified. A model of on-line trust between users and websites is presented. The model identifies three perceptual factors that impact on-line trust: perception of credibility, ease of use and risk. The model is discussed in detail and suggestions for future applications of the model are presented.     

基于非压缩后缀树的在线PPM预测模型

PPM模型适合预测用户的下一个请求,但已有的PPM模型不具备在线性,更新通过重构来实现,不能满足实时更新的要求。该文提出基于非压缩后缀树的在线PPM预测模型,采用非压缩后缀树实现增量式在线更新,提高了模型的更新速度。该模型的优点是具备在线性。     

An imperfect production process for time varying demand with inflation and time value of money – An EMQ model

The paper deals with an economic manufacturing quantity (EMQ) model for time-dependent (quadratic) demand pattern. Every manufacturing sector wants to produce perfect quality items. But in long run process, there may arise different types of difficulties like labor problem, machinery capabilities problems, etc., due to that the machinery systems shift from in-control state to out-of-control state as a result the manufacturing systems produce imperfect quality items. The imperfect items are reworked at a cost to become the perfect one. The rework cost may be reduced by improvements in product reliability i.e., the production process depend on time and also the reliability parameter. We want to determine the optimal product reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process using Euler–Lagrange theory to build up the necessary and sufficient conditions for optimality of the dynamic variables. Finally, a numerical example is discussed to test the model which is illustrated graphically also.