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.     

Experimental Study on Polymer Artificial Bone with HA Coated and Fiber Liners

1Introduction HA(hydroxyapatite)wasakindofbioactiveceram ics,whichhadexcellentbiocompatibilityandtissueaffin ityinthatitscomponentsweresimilartothoseofhuman bone[1].Soitwasthebestknownhumanbonesubstitute,andunprecedentedeffecthadbeenharvestedinrecenttwo d…     

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。     

地震数据油气预测中的属性优化方法

本文简单介绍了智能信息处理中新出现的RoughSet（RS）理论及属性选择方法，从双相介质地震波传播理论角度，探讨了地震数据油气预测属性优化原理，提出了基于RS理论的地震数据油气预测属性优化方法。实际应用表明；本方法速度快、易实现，而且在优选属性、最大程度地减少提取地震属性种数、提高分类正确率等方面，明显优于其它方法。本方法将成为地震数据油气预测的一种有效手段。     

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.     

人工神经网络技术在板料激光弯曲中的应用

基于人工神经网络基本理论,建立在板料激光弯曲中预测材料表面最高温度、弯曲角度的BP网络模型。借助于MATLAB仿真软件中的神经网络工具箱作为开发平台,将试验样本数据和经过试验验证的数值计算结果作为补充的样本数据用于BP网络的训练,利用训练好的BP网络对非线性的样本数据规律进行拟合,实现激光加工工艺参数的优化,为实际生产和加工提供有效的依据。     

RSFM网络及其在信号滤波中的应用

介绍了一种适用于模式识别的新型神经网络模型--局部有监督特征映射（Reglonal Supervised Feature Mapping,RSFM）网络,描述了该网络的拓扑结构和学习算法,研究了网络的基本性能,最后将其应用到了信号滤波中.理论研究和仿真实验表明,该网络结构简单、算法简洁,收敛速度快、识别精度高,适用于需要大样本训练、随机干扰严重的复杂模式的分类与识别.     

降雨空间分布模式识别

以广东省内186个站点的年降雨量分布为基础，把模式识别的聚类分析理论方法引入降雨空间特征分类研究中，进行降雨特征空间分布模式识别，以预测无雨量站点的降雨模式，从而分析水资源的空间分布模式，以便制订有效的水资源利用方案。结果表明，广东省年降雨量在空间上的分布可划分为6类模式；同时基于BP人工神经网络算法，建立了以高程、气温、蒸发量为主要参数的可判别和预测无雨量测站区域的降雨空间分布模式所属类别的模型，经过模型的学习和验证，效果较好。