一种Linux下支持QoS的移动IPv6与MPLS集成方案

本文提出了一种在Linux下将移动IPv6和MPLS集成，并结合Diff-serv模型提高服务质量的方案。讨论了将这两种协议集成所需的信令过程，以及在集成模型中应用Diff-serv的机制，并给出了实现的方法。     

基于组件的空间集成建模研究*

分析了模块化、组件式和基于服务三种主要的空间集成建模方法的特点和关键问题,指出基于组件方法在研究机构内部或之间的模型共享和复用方面具有更大的优势。描述了所设计的一个基于组件的空间集成建模的方案,对设计的主要内容,即集成模型的结构、模型的接口规范、模型的耦合方式,以及支持模型集成的基础库进行了讨论,并分析了设计所具有的潜在优势。基于以上设计,实现了一个空间集成建模系统,并针对化学品突发事件建立了集成的应急响应辅助支持模型。     

采用时间约束Petri网分析优化广电过程模型

企业过程模型评价效果很难通过简单途径取得定量的对比分析结果,本文提出时间约束Petri网理论,在企业过程模型的主要指标采用Little转化为以时间为主的评价体系的基础上,研究采用时间约束Petri网建模对企业过程模型分析的方法和分析优化步骤,并针对广电网络过程模型的具体流程给出的实际案例加以形式化分析,得出优化的方案.     

企业建模中资源模型与过程模型解耦问题研究

首先介绍了企业建模与资源模型的概念，分析了企业资源的内在结构，并应用面向对象的方法完整而清晰地表达了企业资源视图中应包含的各种对象．在此基础上，重点提出了一种企业建模中实用的资源模型与过程模型解耦方法，并介绍了基于此方法开发的集成建模工具．     

面向供应链管理的企业应用集成技术选择模型

供应链信息系统的集成对于提高供应链管理水平至关重要,企业应用集成(EnterpriseApplicationIntegration,EAI)技术的出现对供应链信息系统的集成提供了一个新的解决方案。论文通过对供应链集成系统的分类和EAI技术的分析,提出了选择适用于不同类型系统的EAI技术的模型,通过该模型的应用,减少了企业在选择供应链信息系统集成技术时的不确定性,为有效地集成供应链信息系统提供了切实可行的方法和途径。     

基于信息模型的企业过程度量研究

探讨了企业过程度量的作用和意义,分析了企业信息模型能够支持企业过程度量的原因和需要进行改进的地方;提出了一个度量信息模型,将企业过程模型中的信息需要映射到企业信息模型,在基本上不改变现有信息模型的情况下,实现对企业过程度量的支持;简单阐述了实现度量信息模型的实现方法;为企业过程模型与信息模型的集成提供一种方法和机制。     

生产执行系统(MES)在卷烟企业中的应用

结合卷烟企业CIMS（Computer Integration Manufacture System现代集成制造系统）工程的实施,分析了卷烟企业实施MES的必要性;以ISA95模型为基础,提出了卷烟企业建设MES的体系结构及功能结构,重点介绍了MES设计与实施中的若干关键技术和解决方案。系统采用模型驱动技术及现代集成技术,在实现生产排产调度、标准管理、质量检验、消耗反馈、物料跟踪等功能的同时,实现了与企业ERP（Enterprise Resource Plan企业资源计划）、SPC（Statistical Process Con-trol统计过程控制）、CAD（Computer Aided Design智能辅助设计系统）及底层自动化系统的完美集成,为高效利用精确生产实时数据,保证生产过程的协调运转,全面提高企业生产管理水平提供了一套可行的方法和途径。     

基于政务数据仓库的数据集成及关键技术

政府机关网络中的数据集成是进行数据挖掘的准备阶段，这一阶段需要以行政决策为依据来抽取和集成数据并导入数据仓库。文章根据政府机关网络中已有数据的特点和数据挖掘过程中对数据的要求，详细讨论了构建数据集成的对象模型的方法、运用元数据库来实现致务数据仓库的数据集成的途径、使用对象模型来实施异构数据集成的关键技术。     

一种保障VoIP QoS的集成模型及其改进策略研究

VoIP的服务主要归结为承载网络的问题。本文首先对综合服务(IntServ)、区分服务(DiffServ)以及多协议标签交换(MPLS)三种服务模型进行分析，然后提出一种基于这三种服务模型的集成模型，并对这种集成模型的结构及主要部件进行详细分析。在此基础上着重讨论了在这种模型上传输VolP业务的QoS保障技术，并对存在的问题，提出一种自适应LSP策略，以更好地满足端到端的VoIP QoS需求。     

一种统一授权和访问控制模型的设计实现

企业信息平台(EIP)是一个基于企业模型的平台,目标是为了实现模型驱动的企业设计、分析和评价。EIP的目标之一就是建立一个可以容易地实现不同系统集成的架构,如:政府和其他企业中过程、结构、任务、目标和信息等的集成。论文的重点不是EIP中数据的集成,也不是应用的集成,而是授权的集成,主要针对的是EIP中工作流管理和资源管理中的授权集成问题。在现有的EIP系统中,工作流管理和资源管理一般都有各自独立的授权和访问控制模块,这种非一体化的授权方法容易引发多种安全问题。而先前的研究大都集中在单独的授权系统上,对它们的集成很少有讨论。论文提出了一种统一的授权和访问控制模型,可以以一种统一的策略来表示处理EIP系统中的各种授权和访问控制,解决了分散授权模型带来的一些安全问题,较好地实现了授权的集成。     

构建远程ICAI的研究

该文介绍了将远程网络教学与ＩＣＡＩ相结合并构建远程ＩＣＡＩ的研究工作,提出了一个基于远程网络的ＩＣＡＩ模型,以及验证该模型的实验系统中有关知识表示、智能综合集成、协同教学模式和在超文本中增加教学控制描述等问题的解决方法。     

A framework for integrated CASE

An organizational framework for integrated CASE development and research that is based on the reference model for integrated software-engineering environments being developed by the NIST and the European Computer Manufacturers Association is proposed. Services defined in the reference model permit three forms of integration: data integration, control integration, and presentation integration. The reference model, which describes a wide range of CASE environments, and frameworks, can guide standards development and serve as a basis for educating software engineers. The organizational framework divides systems development and management into three activity levels: IS infrastructure planning and design is undertaken at the enterprise level, systems project management and decisions are made at the project level, and software-development processes are carried out at the individual and team level. The ways in which the organizational framework, which complements the technical framework, can guide the development and deployment of integrated CASE environments, direct future research, and help CASE users select and configure tools in an integrated CASE environment are discussed     

一种提高系统柔性的工作流模型

工作流技术是实现过程集成的有效途径之一，其研究和应用日益受到重视。工作流模型是工作流技术的重要组成部分，本文介绍了一种基于活动网络的柔性工作流模型，从模型的角度描述了组成工作流的基本元素类型及图形表示，讨论了此工作流模型的形式定义，并给出了一个工作流模型的例子。     

炼油企业生产计划和能量系统集成优化研究

炼油企业通常由生产过程和公用工程两个主要部分组成。将生产计划和公用工程集成优化,不仅获得真正的全局最优解,而且克服了传统生产计划人为制定生产过程与公用工程之间中间物流（各压力等级的蒸汽,瓦斯等）价格的缺陷,避免了蒸汽减温减压和放空,以及炼厂气放空的经济损失和环境破坏。首先采用IDEF0方法建立了生产计划与公用工程的功能模型,描述了两者之间的相互关系。接着建立了生产计划与公用工程集成的混合整数线性规划模型。模型描述了装置能耗不仅与加工量相关,而且与装置的生产方案相关,更真实地反映了生产过程与能量系统之间的定量关系。模型还在全厂范围内进行各压力等级蒸汽和燃料的平衡。最后,将建立的全厂集成优化模型应用于中国北方的某大型炼油厂,验证了模型的有效性。     

临床检验信息集成系统的设计与实现

针对临床检验的特点，提出了多级网络结构下的多级C／S模式检验信息集成系统，设计并实现了适配器，使检验方便进入系统；构造一种基于层次结构知识单元的信息流数据识别模型，并对各知识单元设计了通用的框架式知识表示外壳，使模型能正确提取各种检验设备输出信息中的各个数据项。     

Downstream horizontal integration and multiunit dealership

In this paper, a game theoretical model featuring a manufacturer and two dealers has been proposed to investigate the belief that horizontal integration in an established network and granting of regional multiunit dealership in a growing network improve the manufacturer's profit. Some of the main results are as follows: (1) the manufacturer and dealers are mutually better off when integration and multiunit dealership lead to an increase in consumer demand through higher prices and better investments in services and national advertising, (2) downstream integration and multiunit dealership harm the manufacturer's profit when the integrated dealer aims to free‐ride the manufacturer's brand or local service has no impact on consumer demand, (3) under certain conditions, both the manufacturer and dealers prefer downstream competition to integration regardless of whether it increases or reduces retail prices. Finally, mechanisms to implement horizontally integrated strategies in an established dealer network made of multiple single‐unit dealers such as the use of resale price maintenance (RPM), exclusive territory agreement, and dealer cuts are discussed.     

Pattern recognition in a compartmental model of a CA1 pyramidal neuron.

Computer simulation of a CA1 hippocampal pyramidal neuron is used to estimate the effects of synaptic and spatio-temporal noise on such a cell's ability to accurately calculate the weighted sum of its inputs, presented in the form of transient patterns of activity. Comparison is made between the pattern recognition capability of the cell in the presence of this noise and that of a noise-free computing unit in an artificial neural network model of a heteroassociative memory. Spatio-temporal noise due to the spatial distribution of synaptic input and quantal variance at each synapse degrade the accuracy of signal integration and consequently reduce pattern recognition performance in the cell. It is shown here that a certain degree of asynchrony in action potential arrival at different synapses, however, can improve signal integration. Signal amplification by voltage-dependent conductances in the dendrites, provided by synaptic NMDA receptors, and sodium and calcium ion channels, also improves integration and pattern recognition. While the biological sources of noise are significant when few patterns are stored in the associative memory of which the cell is a part, when large numbers of patterns are stored the noise from the other stored patterns comes to dominate the pattern recognition process. In this situation, the pattern recognition performance of the pyramidal cell is within a factor of two of that of the computing unit in the artificial neural network model.     

Fault handling in large water networks with online dictionary learning

Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through online dictionary learning. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time. This brings the benefit of continuous integration of new data into the existing network model, either in the beginning for training or in production when new data samples are gathered. The proposed algorithms show good performance in our simulations on both small and large-scale networks.     

On-line robust identification of tool-wear via multi-sensor neural-network fusion

Real-time identification and monitoring of tool-wear in shop-floor environments is essential for the optimization of machining processes and the implementation of automated manufacturing systems. This paper analyzes the signals from an acoustic emission sensor and a power sensor during machining processes, and extracts a set of feature parameters that characterize the tool-wear conditions. In order to realize real-time and robust tool-wear monitoring for different cutting conditions, a sensor-integration strategy that combines the information obtained from multiple sensors (acoustic emission sensor and power sensor) with machining parameters is proposed. A neural network based on an improved backpropagation algorithm is developed, and a prototype scheme for the real-time identification of tool-wear is implemented. Experiments under different conditions have proved that a higher rate of tool-wear identification can be achieved by using the sensor integration model with a neural network. The results also indicate that neural networks provide a very effective method of implementing sensor integration for the on-line monitoring of tool abnormalities.