A mix method of knowledge capitalization in maintenance

This paper deals with knowledge capitalization in maintenance especially in diagnosis and repair of industrial equipments. The goal is to propose a method of knowledge capitalization in order to develop a decision support system for maintenance operators. The knowledge capitalization cycle was adopted as the underlying principle. It consists of four principal steps: detect, preserve, capitalize and actualize the strategic knowledge. Different knowledge management tools and methods that can be used in the cycle are reviewed. We propose a mix method of knowledge capitalization in maintenance. This method integrates a representation and a reasoning model both completing each other and suitable to represent and manipulate the domain knowledge. The knowledge representation model using unified modelling language (UML) diagram proposes different domain models based on maintenance analysis to guide the domain expertise. The reasoning model uses the case-based reasoning which allows the manipulation of represented domain knowledge. Finally, the method is implemented on the pallet transfer system Sormel in the context of Proteus e-maintenance platform.     

Case based reasoning method for computer aided welding fixture design

This paper presents a case-based reasoning (CBR) method for welding fixture design, a critical issue in the manufacturing of large and complicated equipment. However, previous fixture design research has mainly focused on machining fixtures rather than welding fixtures. In this paper, an approach of data abstraction for fixture design information representation is proposed, first to systemize and manage myriads of fixture related resources, e.g., past fixture design solutions, fixture units depository. Based on this approach, a multi-level CBR method for welding fixture design is then presented. This method could help designers, by referencing previous design cases, to make a conceptual fixturing solution quickly and, finally, finish the detailed solution of fixture design.     

Design and application of a new knowledge engineering tool for solving real-world problems

The results of a broader cognitive research on an intelligent knowledge engineering program environment are described. This intelligent programming tool features an open architecture, modularity and an idea to use multistrategy learning, multistrategy knowledge representation and integration of various schemes of knowledge processing in a single inferential process. Some selected applications of the developed tool, carefully examined at various levels, are briefly dealt with.     

Desktop haptic virtual assembly using physically based modelling

This research investigates the feasibility of using a desktop haptic virtual environment as a design tool for evaluating assembly operations. Bringing virtual reality characteristics to the desktop, such as stereo vision, further promotes the use of this technology into the every day engineering design process. In creating such a system, the affordability and availability of hardware/software tools is taken into consideration. The resulting application combines several software packages including VR Juggler, open dynamics engine (ODE)/open physics abstraction layer (OPAL), OpenHaptics, and OpenGL/GLM/GLUT libraries to explore the benefits and limitations of combining haptics with physically based modelling. The equipment used to display stereo graphics includes a Stereographics emitter, Crystal Eyes shutter glasses, and a high refresh rate CRT Monitor. One or two-handed force feedback is obtained from various PHANTOM haptic devices from SensAble Technologies Inc. The application’s ability to handle complex part interactions is tested using two different computer systems, which approximate the higher and lower end of a typical engineer’s workstation. Different test scenarios are analyzed and results presented.     

Autonomic self healing and recovery informed by environment knowledge

An important goal of autonomic computing is the development of computing systems that are capable of self healing with a minimum of human intervention. Typically, recovery from even a simple fault will require knowledge of the environment in which a computing system operates. To meet this need, we present an approach to self healing and recovery informed by environment knowledge that combines case based reasoning (CBR) and rule based reasoning. Specifically, CBR is used for fault diagnosis and rule based reasoning for fault remediation, recovery, and referral. We also show how automated information gathering from available sources in a computing system’s environment can increase problem solving efficiency and help to reduce the occurrence of service failures. Finally, we demonstrate the approach in an intelligent system for fault management in a local printer network.     

The IKBALS project: Multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers.Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process.Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions.The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.     

基于知识库和实例推理的构件检索方法

提出了一种利用人工智能领域中基于实例的推理（CBR）创建基于知识库的软件构件库进行构件检索的框架方法。重点阐述了利用软件构件的功能和行为知识表达测量检索到的构件实例与问题需求的相似度、构件功能性和构件可重用性的方法。     

Buffer allocation in unreliable production lines using a knowledge based system

The optimization of production lines performance is a problem of great complexity and, therefore, of significant research interest. The problem may involve the optimization of many conflicting objectives, such as increasing throughput and reducing work-in-process time. The majority of existing studies have used various heuristics and search methods based on operations research. These methods have been proved to be computationally inefficient, especially for large production lines. This paper presents ASBA2, a knowledge based system that determines near optimal buffer allocation plans, with the objective of maximising production lines throughout. The allocation plan is calculated subject to a given amount of total buffer slots, in a computationally efficient way. ASBA2 operates in close cooperation with a simulation method, which provides ASBA2 with performance measures concerning production line behaviour. Moreover, to evaluate results provided by ASBA2, we have utilized an exact numerical algorithm for calculating the throughput of unreliable production lines.     

SYMBAD—Similarity based agents for design

This paper presents the SYMBAD (similarity based agents for design) system, exploring multi-agent aspects in an architecture company, capturing, cataloging, and communicating information produced by the team members. The main task managed by the designers is to build stands to present the image of a company, project its presence in the market and emphasize the corporate identity to all prospects. From conceptual design to the construction of a final product, a stand project passes through many hands, each one adding bits and pieces until it is completed. Reuse of materials and ideas is less feasible as design complexity increases. The processes and problems in stand projects are quite common and can be easily found in other design situations. We present an agent framework to improve process awareness in an architecture company. The agents instrument the process to produce global awareness, to facilitate reuse and optimize the process as a whole. In this paper we present the agent architecture, as well as each agent’s general functioning and reasoning rules.     

WCOID-DG: An approach for case base maintenance based on Weighting, Clustering, Outliers, Internal Detection and Dbsan-Gmeans

The success of the Case Based Reasoning system depends on the quality of the case data and the speed of the retrieval process that can be costly in time, especially when the number of cases gets bulky. To guarantee the system?s quality, maintaining the contents of a case base (CB) becomes unavoidably. In this paper, we propose a novel case base maintenance policy named WCOID-DG: Weighting, Clustering, Outliers and Internal cases Detection based on Dbscan and Gaussian means. Our WCOID-DG policy uses in addition to feature weights and outliers detection methods, a new efficient clustering technique, named DBSCAN-GM (DG) which is a combination of DBSCAN and Gaussian-Means algorithms. The purpose of our WCOID-GM is to reduce both the storage requirements and search time and to focus on balancing case retrieval efficiency and competence for a CB. WCOID-GM is mainly based on the idea that a large CB with weighted features is transformed to a small CB with improving its quality. We support our approach with empirical evaluation using different benchmark data sets to show its competence in terms of shrinking the size of the CB and the research time, as well as, getting satisfying classification accuracy.     

基于案例推理的工作流异常处理研究

对工作流的异常和案例推理（Case—Based Reasoning,简称CBR）的机制进行了介绍,给出了一个应用CBR技术进行异常处理的工作流模型．并研究了应用CBR方法处理工作流异常的机制。     

An empirical investigation of reasoning with legal cases through theory construction and application

In recent years several proposals to view reasoning with legal cases as theory construction have been advanced. The most detailed of these is that of Bench-Capon and Sartor, which uses facts, rules, values and preferences to build a theory designed to explain the decisions in a set of cases. In this paper we describe CATE (CAse Theory Editor), a tool intended to support the construction of theories as described by Bench-Capon and Sartor, and which produces executable code corresponding to a theory. CATE has been used in a series of experiments intended to explore a number of issues relating to such theories, including how the theories should be constructed, how sets of values should be compared, and the representation of cases using structured values as opposed to factors.     

A case based approach to expressivity-aware tempo transformation

The research presented in this paper focuses on global tempo transformations of monophonic audio recordings of saxophone jazz performances. We are investigating the problem of how a performance played at a particular tempo can be rendered automatically at another tempo, while preserving naturally sounding expressivity. Or, differently stated, how does expressiveness change with global tempo. Changing the tempo of a given melody is a problem that cannot be reduced to just applying a uniform transformation to all the notes of a musical piece. The expressive resources for emphasizing the musical structure of the melody and the affective content differ depending on the performance tempo. We present a case-based reasoning system called TempoExpress for addressing this problem, and describe the experimental results obtained with our approach. Editor: Gerhard Widmer     

A new process knowledge representation approach using parameter flow chart

Knowledge in various stages of the product development process has become increasingly important for manufacturing companies to improve their performance, especially for those One-of-a-Kind Production (OKP) companies producing highly customized products. Process knowledge is a very special type of knowledge that controls how products are best manufactured. This knowledge can help OKP companies produce high value-added products with better quality at shorter times and at a competitive cost. Process knowledge is normally hard to capture and manage and is even more difficult to represent. This paper proposes a Parameter Flow Chart (PFC) based new knowledge representation method which efficiently combines parameter information, flow chart technology, and visualization technology. The goal of this research is to provide a user-friendly and effective way of representing process knowledge for OKP companies so they can develop and accumulate their own process knowledge repository. The basic definition and principle of the approach will be introduced first and the software system model then proposed. Two related key issues, the modeling of various chart units used in the PFC approach and dealing with expressions containing various parameters, are discussed in detail. The prototype version of the system has been developed and demonstrated with case studies, which has proven the feasibility of the proposed approach.     

Effect of knowledge sharing visibility on incentive-based relationship in Electronic Knowledge Management Systems: An empirical investigation

Previous studies have confirmed the knowledge sharing visibility (KSV) may ensure the incentive-sharing relationship within traditional organizations. However, considering high loafing tendency in IT-based and distributed environments, this effect should be re-examined. This study examines how KSV impacts on incentive-based relationship in IT-based knowledge management systems. The data were collected from real KM systems in an innovative service company. The results show that, in distributed environment, KSV was still a critical moderators in the prediction of employee’s knowledge sharing behaviors. In the electronic KMS environment, the positive relationship between incentive and KS is stronger when employees’ perceived KSV is higher. This research contributes theoretically to KS literature in examing the KSV as the three-way moderators on incentive-based relationship in IT-based environment, from real organizational samples.     

From knowledge based software engineering to knowware based software engineering

The first part of this paper reviews our efforts on knowledge-based software engineering, namely PROMIS, started from 1990s. The key point of PROMIS is to generate applications automatically based on domain knowledge as well as software knowledge. That is featured by separating the development of domain knowledge from the development of software. But in PROMIS, we did not find an appropriate representation for the domain knowledge. Fortunately, in our recent work, we found such a carrier for knowledge modules, i.e. knowware. Knowware is a commercialized form of domain knowledge. This paper briefly introduces the basic definitions of knowware, knowledge middleware and knowware engineering. Three life circle models of knowware engineering and the design of corresponding knowware implementations are given. Finally we discuss application system automatic generation and domain knowledge modeling on the J2EE platform, which combines the techniques of PROMIS, knowware and J2EE, and the development and deployment framework, i.e. PROMIS/KW＊＊.     

推理建模中基于KDD和粗糙集的案例修改

在应用基于案例推理技术进行智能建模时,案例修改后的案例质量好坏直接影响所建模型的精度,但是由于案例修改对领域知识的依赖性很强,采用一般手工案例修改方法尤法保证案例修改的质量,即无法保证智能推理模型的精度。基于以上原因,该文提出了一种新的案例修改方法,利用KDD技术,通过有效的多值关联规则挖掘算法从运行数据库中挖掘出案例各属性间的依赖关系,得到案例修改的基本关联规则集,在此基础上利用粗糙集理论对基本关联规则集进行简约,然后根据简约后的关联规则进行案例修改。在线对比实验证明,应用本文方法进行案例修改,提商了修改后的案例质量,从而提高了整体智能推理模型的精度。