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.

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.

Developing a geographic Case-Based Reasoning approach

Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This paper intends to further extend current CBR to a geographic CBR （Geo-CBR）. First, the concept of Geo-CBR is proposed. Second, a representation model for geographic cases has been established based on the Tesseral model and on a further extension in spatio-temporal dimensions for geographic cases. Third, a reasoning model for Geo-CBR is developed by considering the spatio-temporat characteristics and the uncertain and limited information of geographic cases. Finally, the Geo-CBR model is applied to forecasting the production of ocean fisheries to demonstrate the applicability of the developed Geo-CBR in solving problems in the real world. According to the experimental results, Geo-CBR is an effective and easy-to-implement approach for predicting geographic cases quantitatively.

Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization

To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.

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.

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.

环境污染应急处置技术的CBR-MADM两步筛选法模型

突发环境污染事件进行应急处置时,需要在历史案例库和处置技术库的基础上通过筛选模型筛选出最适宜的应急处置技术.其中筛选的效率和准确性是构建技术筛选模型的首要因素,目前尚无较为满意的解决方案.本文结合案例推理技术(CBR)和模糊多属性群决策模型(MADM)的优势,建立了环境污染应急处置技术的CBR-MADM两步筛选法模型:第1步,先利用基于熵权G1法的CBR推理从案例库中匹配案例,再从相似度最高的几个案例中提取应急处置技术作为备选技术;第2步,利用MADM对备选技术进行筛选和决策.CBR-MADM两步筛选法既充分利用了历史处置经验,又极大地提高了应急处置技术的筛选速度和效率.将该方法应用于2012年底山西长治浊漳河苯胺泄漏污染事件中,成功筛选出"投加混凝剂-活性炭坝拦截"技术,同实际情况吻合,验证了其适用性和可行性.

基于AHP与CBR技术的注射模智能报价方法研究

提出了一种新的注射模报价思路,即在通用模具报价软件的基础上做到个性化定制,将可调整性和可适应性纳入到报价软件的基本设计目标中。结合层次分析法和基于事例推理技术,提出先整体报价再分组报价的方法,可以更好地贴近各模具制造企业的实际,所建立的报价数学模型更合理,用户可以自行控制报价因素,从根源上制定符合本企业的报价模式,应用前景广阔。

CBR/VTS融合系统及其应用

针对船舶海上遇险情况,运用CBR系统对VTS船舶管理系统进行补充,构建CBR/VTS融合系统,提高海上船舶信息服务水平,改善航道安全状况。

TCBR中案例相关度及案例冗余处理模型

首先介绍基于文档的案例推理技术TCBR,以及CRN、IEs等基本理论,然后详细介绍CRN的构建过程和相关的函数模型,并提出一个求和函数来计算案例与当前问题的相关值,在文章的后面阐述了TCBR的案例学习方法以及案例相似程度的一个判断函数,并提出在这种情况下对案例冗余的处理方案。

基于GIS与CBR的城市绿地规划支持系统及其关键技术

分析和指出了现行城市绿地规划设计方法的缺陷.在此基础上,基于GIS与CBR技术,尝试性地提出了建立城市绿地规划支持系统进行规划设计的思想,同时给出了城市绿地规划支持系统的总体结构设计,并着重阐述了实现这种系统的关键技术,即基于GIS技术的现状信息网络发布与修改技术及CBR技术支持下的城市绿地规划参考方案的产生方法.

New Algorithm for CBR-RBR Fusion with Robust Thresholds

The case-based reasoning（CBR） and rule-based reasoning（RBR） fusion systems include a diverse range of fusion methods and their tasks are characterized by interleaving combination of the reasoning procedures. Existing approaches cannot clarify the complex relationships between data from the knowledge sources nor uniformly represent the heterogeneous case and rule knowledge in one fusion space. As a result, existing approaches fail to solve system fragility due to knowledge uncertainty and reasoning unreliability. For the purpose of addressing the difficulties, a novel algorithm for CBR-RBR fusion with robust thresholds（CRFRT） is proposed. Heterogeneous case and rule knowledge are uniformly represented in one defined fusion unitary space. The robust thresholds have been achieved to distinguish the complex relationships between meta-knowledge in the fusion space and to enhance system capacity of knowledge identification. Furthermore, fusion reasoning strategies are constructed for CRFRT and its procedure based on which robust solution of the fusion reasoning problem is obtained. Finally, CRFRT is validated by benchmark problems in machine learning. Compared with other CBR and RBR approaches, the reasoning efficiency and accuracy are increased by 5% and 2.2% respectively. The variations of system accuracy are decreased by 2% to 3.8%. The above results show that the CRFRT algorithm boosts the system＇s effectiveness and robustness. The proposed CRFRT can solve the fragility of complex intelligence decision system and give quality performance for fault diagnosis.

TCBR中案例相关度及案例冗余处理模型

首先介绍了基于文档的案例推理技术TCBR,以及CRN,IEs等基本理论,然后详细介绍了CRN的构建过程和相关的函数模型,并提出了一个求和函数来计算案例与当前问题的相关值,在文章的后面阐述了TCBR的案例学习方法以及案例相似程度的一个判断函数,并提出了在这种情况下对案例冗余的处理.

Research of CBR, DM and smart algorithms based design methods for high-rise building structure form-selection

First, the high-rise building structure design process is divided into three relevant steps, that is, scheme generation and creation, performance evaluation, and scheme optimization. Then with the application of relational database, the case database of high-rise structures is constructed, the structure form-selection designing methods such as the smart algorithm based on CBR, DM, FINS, NN and GA is presented, and the original forms system of this method and its general structure are given. CBR and DM are used to generate scheme candidates; FINS and NN to evaluate and optimize the scheme performance; GA to create new structure forms. Finally, the application cases are presented, whose results fit in with the real project. It proves by combining and using the expert intelligence, algorithm intelligence and machine intelligence that this method makes good use of not only the engineering project knowledge and expertise but also much deeper knowledge contained in various engineering cases. In other words, it is because the form selection has a strong background support of vast real cases that its results prove more reliable and more acceptable. So the introduction of this method provides an effective approach to improving the quality, efficiency, automatic and smart level of high-rise structures form selection design.

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.

基于事例推理的电网事故处理专家系统

基于事例推理(CBR)是近年来专家系统领域的一个热点。本文据CBR的基本概念熏按照其一般原理熏结合J2EE技术熏并考虑电力系统的特点,构建了一个运用J2EE模式设计技术的CBR专家系统软件;提出了事例检索时的变权值检索方法熏事例学习时的修正策略和为提高检索效率的事例检索记忆表策略,使CBR专家系统的适应能力与自学习能力大大增强;并运用J2EE的模式设计技术搭建了软件的宏观结构,用JSP、JavaBean、Servlet等技术手段完成具体的微观实现。据此开发的电网事故处理专家系统通过了测试。

集成知识工程和实例推理技术的产品变型设计研究

鉴于复杂机械产品设计存在周期长和难度大的问题,以知识工程和实例推理技术的研究为启示,将知识工程和实例推理技术相结合,以参数型CAD系统为平台,提出了集成知识工程和实例推理技术的复杂产品变型设计系统的总体框架,并对该系统的关键技术进行了研究。在此基础上,提出了一个产品变型设计系统功能模型,此系统是在VB.net环境下,利用面向对象方法,以参数型CAD三维软件Pro/E为开发平台并结合数据库SQL Server2005开发而成。通过应用实例表明,该框架对于构造更加高效的变型设计系统有一定的实际应用价值。

隐性知识在产品概念设计中的应用

工业设计领域中,提高产品的设计质量是利益最大化的首要途径,而产品概念设计直接决定产品设计质量,而隐性知识为产品概念创新设计提供了新的思路,因此,提出一种关于隐性知识应用于产品概念设计中的方法。在理解隐性知识概念的前提下,从产品出发,挖掘隐藏在产品中的隐性知识,将这些隐性知识显性化,用人们容易理解、接受的知识进行表达,便于被重新利用。通过收集大量的产品隐性知识,建立产品隐性知识库,借助案例推理(case-based reasoning,CBR)技术,运用相似度计算法,获取知识库中与目标设计要求相似度最大的案例,将其作为目标设计或作参考,直接运用于新产品的开发中。结果表明,该方法不仅能缩短产品设计周期,而且能大大提高产品的质量。本研究为企业迅速抢占市场,赢得市场份额具有一定意义。

一种大型邮轮乘客舱室智能布局设计方法

为了探索邮轮布置机理、提高舱室设计效率,进而实现邮轮舱室的智能布局,提出了一种基于知识工程的布局设计方法.定义了大型舱室布置适宜度、物资配送难度和风景值等邮轮总布置独有的特征属性,建立了乘客舱室布局目标函数;运用案例推理技术和规则推理技术,建立了乘客舱室智能布局模型,并以公主系列邮轮为例验证了模型的适用性.结果表明,该模型能还原大部分舱室在甲板区域间的布置,并输出基本合理的布局方案,对实际设计工作具有重要指导意义.