Integration framework for intelligent manufacturing processes

In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificial intelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.To whom all correspondence should be addressed.     

基于事例学习的建筑结构的虚拟实现

提出基于事例学习的目标规划问题,并进一步研究了建模思想的可行性和建模方法的可操作性。利用知识工程、机器学习理论,人工智能技术来存贮和运用专家的经验,实现了在虚拟环境中建筑结构设计、施工,设备的仿真试验,并可对初学者或尚无经验的设计、施工者进行培训学习。该项研究为目标规划问题开辟了基于事例学习的模型生成途径。     

DCOP FOR SMART HOMES: A CASE STUDY

The aim of this article is to bring forth the issue of integrating the services provided by intelligent artifacts in Ambient Intelligence applications. Specifically, we propose a Distributed Constraint Optimization procedure for achieving a functional integration of intelligent artifacts in a smart home. To this end, we employ Adopt-N , a state-of-the-art algorithm for solving Distributed Constraint Optimization Problems (DCOP). This article attempts to state the smart home coordination problem in general terms, and provides the details of a DCOP-based approach by describing a case study taken from the RoboCare project. More specifically, we show how (1) DCOP is a convenient metaphor for casting smart home coordination problems, and (2) the specific features which distinguish Adopt-N from other algorithms for DCOP represent a strong asset in the smart home domain.     

A prolog based expert system for the allocation of quality assurance program resources

The worldwide proliferation of fraudulent materials has brought about the need for a new approach to the control of purchased material quality. Nuclear power, defense, aerospace and many other industries are affected by the supply of poor quality clones that presume to be original replacement parts. Safety considerations abound as these highly defective materials are used in systems that are critical to the preservation of human lives. Commonly utilized quality assurance programs have not effectively stopped the influx of fraudulent materials. These programs fail to concentrate inspection efforts on materials and inspection attributes that would effectively prevent the acceptance of fraudulent materials. herein a solution is presented to this problem in the form of an expert system application. Information commonly available in industry is formulated into a knowledge based system wherein advisories are given to the user regarding key purchased material receipt inspection strategies.     

Application of Support Vector Machines in Assessing Conceptual Cost Estimates

Total conceptual cost estimates and the assessment of the quality of these estimates are critical in the early stages of a building construction project. In this study, the support vector machine (SVM) model for assessing the quality of conceptual cost estimates is proposed, and the application of SVM in construction areas is investigated. The results show that the SVM model assessed the quality of conceptual cost estimates slightly more accurately than the discriminant analysis model. This shows that using the SVM has potential in construction areas. In addition, the SVM model can assist clients in their evaluation of the quality of the estimated cost and the probability of exceeding the target cost, and in their decision on whether or not it is necessary to seek a more accurate estimate in the early stages of a project.     

基于进化规划的自适应IIR滤波

针对基于梯度下降算法的自适应IIR滤波器（AIIRF）具有潜在的不稳定性和性能指标函数容易陷入局部极小而导致性能下降等问题，本文将进化规划用于AI－IRF的优化设计，不仅解决了AIIRF系统稳定性问题，而且有效地实现了滤波器性能指标函数的全局寻优和快速收敛，同时允许大动态范围的输入号。计算机仿真结果验证了基于进化规划算法的AIIRF的性能优于基于梯度算法的AIIRF，尤其对高阶、极点靠近单位圆的自适应IIR滤波器。     

智能工程与控制技术：历史,发展与未来

本文综述了智能工程和控制技术的发展历程和基本问题。文中着重论述了人工智能在过程控制系统中的必要性及开发实时智能控制系统的步骤；对符号推理系统、耦合推理系统、人工神经网络和集成分布式智能系统的特点和局限进行了比较。最后讨论了计算机集成过程系统（ＣＩＰＳ）的基本问题、基础技术和发展前景。     

多约束的桥梁结构拓扑优化

基于ESO (Evolutionary Structural Optimization)的拓扑优化方法,本文提出了一种适应于桥梁结构的拓扑优化方法。引进了两种性能指标公式来确定最佳拓扑设计。另外,为了更有效地尝试改进最终设计的细节,而又不进行更精细有限元网格的完整分析,这里提出了一种精细网络设计方案。再者,结合细啃技术,完成了考虑应力、位移、频率约束的斜拉桥优化设计。通过几种桥梁的设计优化,表明该方法的可应用性、简洁性和有效性。     

面向对象系统中的特性表机制与复合对象

本文主要讨论特性表机制与复合对象的概念、功能及实现．在基于知识的面向对象系统中引入特性表机制与复合对象概念的目的在于增强系统的知识表达能力．文中提出的特性表机制包括了三种类型的特性说明表：类说明特性表、继承关系特性表和变量特性说明表．复合对象在系统中是通过引入一个称为“影子类”的新概念来构造和实现的．     

Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.