砀山酥梨黑星病综合管理专家系统的设计与实现①

梨黑星病（Venturia pirina）是砀山酥梨最为严重的病害之一,梨种植户每年因为这种病害遭受了很大的损失。随着农业信息化的发展,国内外目前已有很多关于农业专家系统报道,但关于砀山酥梨黑星病综合管理专家系统尚未见报道。在搜集梨黑星病相关资料和领域专家生产实践经验的基础上,结合黄河故道地区砀山酥梨黑星病发生发展的现状,基于CBR和RBR混合推理模式,利用Visual Basic 6．0编程和Photoshop图像处理等软件,开发了砀山酥梨黑星病综合管理专家系统。该系统由“砀山酥梨黑星病预测与防治子系统”和“砀山酥梨黑星病综合防治决策支持子系统”两个子系统构成,系统涵盖内容全面、界面简洁、针对性强、操作容易,可为黄河故道地区农业技术人员和梨种植户在防治梨黑星病实践中提供决策咨询。     

基于范例表达语言的设计及其推理架构的实现

基于Web技术,从工程机械设备故障诊断的实例出发,提出、定义和设计实现了基于RDF的范例表达语言,并基于开放式知识服务体系,提出了Web CBR的基本实现架构与方法,以适应目前Internet环境下的知识共享、知识管理和知识应用的发展.     

一种基于本体和CBR的电子政务项目审批决策支持系统

分析了将本体与CBR方法结合开发电子政务项目审批决策支持系统的优点,给出了该系统的总体框架和决策支持流程,着重介绍了基于本体的电子政务项目案例的组织和表示方法以及引入本体后案例检索过程中的相似度计算方法。实验结果表明,利用本体对项目案例包含的知识进行提取和表示,再利用这些知识对案例进行组织和检索,有效地提高了案例检索的查准率和查全率。     

VES：阴道炎症（vaginitis）辅助诊断专家系统设计与实现

阐述基于规则诊断推理技术的阴道炎症辅助专家系统的设计与实现。针对阴道炎症医疗诊断的特点,讨论该系统在建立过程采用的关键技术,描述系统的结构框架、知识表示、推理方法和实现技术。整合数据库技术,人工智能技术及阴道炎症医疗诊断技术,为实现阴道炎症智能诊断系统提供有价值的探讨。     

基于案例的解释研究

解释是专家系统中一个重要环节,优秀的解释技术可以帮助用户增强使用系统的信心,帮助用户进一步理解系统的推理过程和结果.介绍基于案例解释的相关概念,然后从不同角度对近年来国内外研究进展做出综述与评价,并对基于案例的解释技术做出总结与展望.     

Behaviour of sand–clay mixtures for road pavement subgrade

Materials forming sand grains and colluvial soil deposits have a distinct structure, consisting of a composite matrix of coarse and fine soil grains. The influence of sand grains content on the behaviour of sand–clay mixtures was investigated by a series of intensive laboratory experiments. The California bearing ratio (CBR), unconfined compression strength (UCS) and compaction tests were carried out on various contents of sand and clay mixtures. The sand–clay mixtures were prepared with sand contents of 0, 10, 20, 30, 40, and 50% by weight. The laboratory tests on these mixtures have indicated that their behaviour will depend on the relative concentration of the sand and clay samples. The results of the tests showed a decrease in the UCS, and an increase the CBR values with an increase in the amount of sand. An increase in dry unit weight and a decrease in respective moisture content by an increase in the amount of sand were observed in the compaction tests.     

案例推理的故障诊断技术研究综述

基于案例推理(case-based reasoning,CBR)的故障诊断作为一种新的智能诊断技术,模拟人类求解问题的思路,通过历史案例发现新问题的解。概述了CBR的理论基础和基本原理;从工作过程和集成框架两个方面综述了CBR故障诊断技术的研究现状,其中工作过程包括案例的表示、检索和重用,以及案例库的维护,集成框架包括CBR与基于规则推理、CBR与人工神经网络以及CBR与多智能体等三种情况;比较了六种故障诊断技术的特点及应用范围;总结了CBR故障诊断技术有待解决的问题。     

Coordination control of greenhouse environmental factors

Optimal control of greenhouse climate is one of the key techniques in digital agriculture. Greenhouse climate, a nonlinear and uncertain system, consists of several major environmental factors such as temperature, humidity, light intensity, and CO₂ concentration. Due to the complex coupled correlations, it is a challenge to achieve coordination control of greenhouse environmental factors. This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning. Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints. In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm, case-based reasoning (CBR) is seamlessly incorporated into the Q-learning process. The experimental results demonstrate that this approach is practical, highly effective and efficient.     

Extended object model for product configuration design

This paper presents an extended object model for case-based reasoning (CBR) in product configuration design. In the extended object model, a few methods of knowledge expression are adopted, such as constraints, rules, objects, etc. On the basis of extended object model, case representation model for CBR is applied to product configuration design system. The product configuration knowledge can be represented by the extended object. The model can support all the processes of CBR in product configuration design, such as case representation, indexing, retrieving, and case revising. The presented model is an extension of the traditional object-oriented model by including the relationship class used to express the relation between the cases, constraints class used in the product configuration knowledge representation, index class used in case retrieving, and solution class used in case revising. Therefore, the product configuration knowledge used in the product configuration design can be represented by using this model. In the end, a metering pump product configuration design system is developed on the basis of the proposed product configuration model to support customized products.     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.