Design System of the Two-step Gear Reducer on Case-based Reasoning

The design of the two-step gear reducer is a tedious and time-consuming process. For the purpose of improving the efficiency and intelligence of design process, case-based reasoning（CBR） technology was applied to the design of the two-step gear reducer. Firstly, the current design method for the two-step gear reducer was analyzed and the principle of CBR was described. Secondly, according to the characteristics of the reducer, three key technologies of CBR were studied and the corresponding methods were provided, which are as follows： （a） an object-oriented knowledge representation method, （b） a retrieval method combining the nearest neighbor with the induction indexing, and （c） a case adaptation algorithm combining the revision based on rule with artificial revision. Also, for the purpose of improving the credibility of case retrieval, a new method for determining the weights of characteristics and a similarity formula were presented, which is a combinatorial weighting method with the analytic hierarchy process（AHP） and roughness set theory. Lastly, according to the above analytic results, a design system of the two-step gear reducer on CBR was developed by VC＋＋, UG and Access 2003. A new method for the design of the two-step gear reducer is provided in this study. If the foregoing developed system is applied to design the two-step gear reducer, design efficiency is improved, which enables the designer to release from the tedious design process of the gear reducer so as to put more efforts on innovative design. The study result fully reflects the feasibility and validity of CBR technology in the process of the design of the mechanical parts.

HYBRID STRATIFIED ATMS AND ANN FOR CASE-BASED REASONING

Case Based Reasoning (CBR) is a powerful problem solving technique in AI, but the traditional CBR techniques have its limitations. We hybridized stratified ATMS and ANN for CBR which can deal with case representation, case retrieving, case adapting, learning from failure more effectively. The structure of our CBR system and algorithms of case base reasoning in our CBR system were presented.

A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China

Reference values of erythrocyte sedimentation rate(ESR)are the key to interpret ESR blood test in clinic.The common local reference ESR values are more accuracy in blood test that are established with natural geographical factors by using the multiple linear regression(MLR)model and the artificial neural network(ANN).These knowledge-based methods have limitations since the knowledge domains of ESR and natural geographical factors are limited.This paper presents a new cases-depended model to establish reference ESR values with natural geographical factors and location using case-based reasoning(CBR)since knowledge domain of ESR and geographical factors is weak.Overall 224 local normal ESR values of China that calculated from 13623 samples were obtained,and the corresponding natural geographical factors and location that include altitude,sunshine hours,relative humidity,temperature,precipitation,annual temperature range and annual average wind speed were obtained from the National Geomatics Center of China.CBR was used to predict the unseen local reference ESR values with cases.The average absolute deviation(AAD),mean square error(MSE),prediction accuracy(PA),and Pearson correlation coefficient(r)between the observed and estimated data of proposed model is 33.07%,9.02,66.93% and 0.78,which are better than those of ANN and MLR model.The results show that the proposed model provides higher prediction accuracy than those of the artificial neural network and multiple linear regression models.The predicted values are very close to the observed values.Model results show significant agreement of cases data.Consequently,the model is used to predict the unseen local reference ESR with natural geographical factors and location.In spatial,the highest ESR reference areas are distributed in the southern-western district of China that includes Sichuan,Chongqing,Guangxi and Guizhou provinces,and the reference ESR values are greater than 23 mm/60 min.The higher ESR reference values are distributed in the middle part and northern-eastern of China which include Hubei,Henan,Shaanxi,Shanxi,Jilin and Heilongjiang provinces,and the reference ESR values are greater than 18 mm/60min.The lowest ESR reference values are distributed in the northern-western of China that includes Tibet and Xinjiang,and the reference ESR values are lower than 5 mm/60min.

A Case-Based Reasoning System for Aiding Physicians in Decision Making

Physicians gather a vast amount of information about patients’ medical procedures, treatments, insurance coverage, and other clinical data. Such information is crucial in formulating diagnosis or treatment plans for patients with similar traits. A Case-Based Reasoning (CBR) system has been developed to address the effective organization and retrieval of vital patient information to aid physicians in making decisions. Integers are used to uniquely represent various medical procedures, treatments, etc. In this research, a new algorithm is presented to retrieve suitable cases to recommend to physicians. The system is tested in a simulated environment and the results prove that the system can adapt to changes such as new medical procedures or treatments that take place in the medical field.

Hydraulic Circuit Design and Dynamic Learning Using Case-based Reasoning

This paper describes the design and implementation of a hydraulic circuit design system using case-based reasoning (CBR) paradigm from AI community The domain of hydraulic circuit design and case-based reasoning are briefly reviewed Then a proposed methodology in compuer-aided circuit design and dynamic leaning with the use of CBR is described Finally an application example is selected to illustrate the ussfulness of applying CBR in hydraulic circuit design with leaming.

ECO-Case Based Reasoning Tool for Environmental Product Design

Case Based Reasoning(CBR)is one of the artificial intelligent methodologies that is widely used in problem solving by reusing the most similar previous experiences stored in the library.A framework of ECO-CBR for Life Cycle Assessment data collection has been used and the process was carried out using SolidWorks program.The practicality of the tool has been validated using case study,which then provides solution.The output enable researchers to determine forecast error and forecast accuracy,by valuing the calculation from Total Carbon Footprint,Energy Consumption,Air Acidification,and Water Eutrophication.ECO-CBR is able to assist designers in product design.Due to the limitation of environmental impact consideration in product sustainability,there is a demand to propose a tool that can assist designers to reduce environmental impact of product design at early stage.The model works as an essential guideline to lessen repeated mistakes in the product development process and help designers measure the risks before concluding ideal decisions.Minor errors that occur through the study showed that ECO-CBR is reliable to be implemented in order to find a better solution.

A Methodological Approach to Model CBR-Based Systems

Artificial intelligence (AI) has been used in various areas to support system optimization and find solutions where the complexity makes it challenging to use algorithmic and heuristics. Case-based Reasoning (CBR) is an AI technique intensively exploited in domains like management, medicine, design, construction, retail and smart grid. CBR is a technique for problem-solving and captures new knowledge by using past experiences. One of the main CBR deployment challenges is the target system modeling process. This paper presents a straightforward methodological approach to model CBR-based applications using the concepts of abstract and concrete models. Splitting the modeling process with two models facilitates the allocation of expertise between the application domain and the CBR technology. The methodological approach intends to facilitate the CBR modeling process and to foster CBR use in various areas outside computer science.

DESIGN TECHNOLOGY FOR INJECTION MOLD PARTING SURFACE BASED ON CASES AND KNOWLEDGE

On the basis of the comprehensive analysis about the automatic generation of the injection mold parting surface, the parting surface design method which introduces knowledge and case-based reasoning （CBR） into the computer-aided design is described by combining with the actual characteristic in injection mold design, and the design process of case-based reasoning method is also given. A case library including the information of parting surface is built with the index of main shape features, The automatic design of the mold parting surface is realized combined with the forward-reasoning method and the similarity solution procedure. The rule knowledge library is also founded including the knowledge, principles and experiences for parting surface design. An example is used to show the validity of the method, and the quality and the efficiency of the mold design are improved.

Analyzing and Designing Business Processes Management System Based on Transformation of Instantiated Ontology’s State

The paper discusses efforts of finding a simple and transparent method to analyze business processes and developing its management system architecture. By introducing ontology and its state, the approach tried to find a unified representation and a flexible choreography of business processes. The main idea of the paper is the transformation of ontology＇s states, which are the most important scenarios of enterprises. The business activity composition, that is, case composition based on an AI technique, Case Based Reason （CBR）, which is to solve new problems by retrieving solutions to previous problems, and then store the modified solution. The main interest in CBR relies on that it allows a system to avoid past failures and exploit past successes.

基于RBR+CBR双重推理的带式输送机驱动系统故障诊断研究

基于规则推理(RBR)和基于案例推理(CBR)的方法均适用于带式输送机故障诊断领域。针对二者在故障诊断中或侧重原因分析或侧重维修手段的问题,对基于RBR+CBR双重推理的推理机制进行了研究,并建立了专家系统,对带式输送机驱动系统进行了故障诊断。首先,在传统专家系统的结构基础上构建了故障诊断专家系统;然后,根据故障机理建立了驱动系统故障树,采用产生式表示法设计了故障规则库,根据故障案例与专家经验,采用矩阵表示法设计了故障案例库;基于规则推理(RBR)和基于案例推理(CBR)的方法,设计了专家系统推理机制;利用LabVIEW程序开发平台设计了专家系统人机交互界面,实现了传感器—知识库—客户端之间的串联通信;最后,为了验证上述专家系统的可行性,在某矿带式输送机运行现场进行了实验,并进行了结果分析。研究结果表明:该带式输送机专家系统的故障原因分析准确率超过95%,故障维修方案有效性超过94%;与RBR、CBR算法支持下的专家系统相比,该专家系统具有更优的收敛性、有效性和准确性,为带式输送机的故障智能诊断提供了一种有效的方法。

燃气管道泄漏爆炸应急救援物资需求及救援点优化选址研究

为研究燃气管道泄漏爆炸后的应急救援物资需求量和最佳救援点选址,采用案例推理方法,建立了燃气管道泄漏爆炸事故应急资源需求模型。以某化工园区发生燃气管道泄漏爆炸为案例,运用MATLAB编程对应急资源需求进行数值计算;利用ArcGIS可视化、地图数据分析功能,建立网络空间数据集,获得该案例工业园区6个应急救援点。结果表明,可通过化工园区应急救援历史数据获得事故应急资源需求量,利用ArcGIS建立位置分配点和确定救援点数量,可获得优化的应急救援选址。

CBR的应急案例通用表示与存储模式

针对目前基于案例推理的应急辅助决策研究中只能对某一特定类型应急案例进行案例表示和案例推理的局限,设计一种基于应急概念树-突发事件本体模型-事件元模型的三层架构的通用应急案例表示与存储模式,为4大类突发事件应急案例的表示、存储与推理提供一个通用的平台。通过原型系统的开发证明该通用模式的实用性。

基于事例推理(CBR)的同构模具工艺设计模型

从同构模具的实际情况出发,分析了同构模具CAD系统中工艺设计部分开发过程所遇到的困难,在参考事例推理思想的基础上,提出了基于CBR的同构模具CAD软件工艺设计模型(SSPPCBR),并详细分析了模型中CBR推理机、交互工艺生成模块、综合数据库、工艺知识库、CAD系统等主要模块的功能及建立在这些模块基础上的同构模具工艺生成原理。

基于CBR的机械产品智能设计方法研究

为快速有效地设计机械工程产品,提出基于实例的机械工程产品设计方法,可实现重用以往设计知识进行新产品的快速设计.该法应用CBR技术依据共享模型的基础构建智能设计平台,以解决实例的检索和修改问题为重点,最终实现了相似匹配计算及最佳实例;并将技术层面的设计问题归属于人机交互的情感设计,避免或减少重复性劳动,改善了工程技术人员的工作环境.给出基于实例的智能设计方法,以桥式起重机产品为例说明了这一方法实现的可行性.

基于CBR的机械产品模块化设计方法的研究

提出在模块化设计中模块对设计需求的适应性问题 ;论述了 CBR技术同模块化设计的关系 ,研究了基于实例的模板创建方法以及专家知识集成于设计模板的策略 ;分析液压机典型结构 ,创建模板实例及参数化的机身CAD模型 ;构造参数化设计的专家知识系统 ,实现知识的查询、提取。

基于CBR的应急案例本体模型

为了解决案例推理过程中应急案例理解上的语义冲突问题,通过分析突发事件应急案例,在扩展ABC本体模型的基础上,定义了基于CBR的应急案例本体模型,并详细描述了模型中的概念、关系、公理和实例等要素。最后以福岛第一核电站核危机为例对该模型进行了验证。

基于CBR的井下复杂情况与事故智能诊断和处理系统

分析了传统的钻井事故诊断与处理专家系统的不足,根据钻井专家决策时的认知过程,基于实例推理技术开发了井下复杂情况与事故智能诊断和处理系统。该系统收集了各油田已发生的各种复杂的事故实例,具有自学习功能,能实现各种复杂情况与事故的诊断和处理,并提供完整的有关复杂事故预防和处理的知识手册,可为钻井专家、技术人员提供决策支持。对系统的总体结构、工作流程以及系统实现的主要技术包括实例表示、实例库组织、实例检索和自学习等进行了详细描述。

一个基于AHP的CBR系统实例检索模型

分析了实例检索在CBR系统中的重要性,指出了传统检索方法的不足。借助语意距离来刻画相似实例定性属性间的距离。通过一个具体例子来证明这种模型的有效性和可行性。

一种CBR与RBR相结合的智能家庭推理系统

介绍了一种CBR与RBR相结合的智能家庭推理系统。将CBR与RBR两种人工智能技术相结合,运用于普适计算的典型应用智能家庭中,首先通过RBR推理出当前用户的活动以及心情等较高级上下文;然后再用CBR进行上下文的再处理,融合多类型或历史的上下文,自动预测相似度最大的上下文,并基于该上下文为用户提供个性化服务。

基于粗糙集和相似度量的CBR检索方法

检索是CBR中的关键技术,直接影响到范例推理的效率和质量,该文提出先用粗糙集理论去除冗余的范例决策表中的特征属性,再用相似度量理论来实现相似范例检索,这种检索方法检索效率高。同时给出了该方法在天气情况预测上的实例,验证了其有效性和准确性。