Mechanical assembly is an important step during product realization, which is an integrative process that brings together the parts of the assembly, the people performing the assembly and the various technologies that are involved. Assembly planning involves deciding on the assembly sequence, the tooling and the processes to be used. Assembly planning should enable the actual assembly process to be as effective as possible.Assembly plans may have to be revised due to issues arising during assembly. Many of these revisions can be avoided at the planning stage if assembly planners have prior knowledge of these issues and how to resolve them. General guidelines to make assembly easier (e.g. Design for Assembly) are usually suited for mass-manufactured assemblies and are applied where similar issues are faced regularly. However, for very specific issues that are unique to some domains only, such as aircraft assembly, only expert knowledge in that domain can identify and resolve the issues. Assembly experts are the sources of knowledge for identifying and resolving these issues. If assembly planners could receive assembly experts’ advice about the potential issues and resolutions that are likely to occur in a given assembly situation, they could use this advice to revise the assembly plan in order to avoid these issues. This link between assembly experts and planners can be provided using knowledge based systems. Knowledge-based systems contain a knowledge base to store experts’ knowledge, and an inference engine that derives certain conclusions using this knowledge. However, knowledge acquisition for such systems is a difficult process with substantial resistance to being automated. Methods reported in literature propose various ways of addressing the problem of automating knowledge acquisition. However, there are many limitations to these methods, which have been the motivations for the research work reported in this thesis. This thesis proposes a dialog-like method of questioning an expert to automatically acquire knowledge from assembly experts. The questions are asked in the context of an assembly situation shown to them. During the interviews, the knowledge required for diagnosing potential issues and resolutions are identified. The experts were shown a situation, and asked to identify issues and suggest solutions. The above knowledge is translated into the rules for a knowledge based system. This knowledge based system can then be used to advise assembly planners about potential issues and solutions in an assembly situation. After a manual verification, the questioning procedure has been implemented on computer as a software named EXpert Knowledge Acquisition and Validation (ExKAV). A preliminary evaluation of ExKAV has been carried out, in which assembly experts interacted with the tool using the researcher as an intermediary. The results of these sessions have been discussed in the thesis and assessed against the original research objectives. The current limitations of the procedure and its implementation have been highlighted, and potential directions for improving the knowledge acquisition process are discussed.

The use of knowledge-based systems to aid in the diagnosis of faults in physical devices has grown considerably since their introduction during the 1970s. The majority of the early knowledge-based systems incorporated shallow knowledge, which sought to define simple cause and effect relationships between a symptom and a fault, that could be encoded as a set of rules. Though such systems enjoyed much success, it was recognised that they suffered from a number of inherent limitations such as inflexibility, inadequate explanation, and difficulties of knowledge elicitation. Many of these limitations can be overcome by developing knowledge-based systems which contain deeper knowledge about the device being diagnosed. Such systems, now generally referred to as model-based systems, have shown much promise, but there has been little evidence to suggest that they have successfully made the transition from the research centre to the workplace. This thesis argues that knowledge-based systems are an appropriate tool for the diagnosis of faults in complex devices, and that both deep and shallow knowledge have their part to play in this process. More specifically this thesis demonstrates how a wide-ranging knowledge-based system for quality assurance, based upon shallow knowledge, can be developed, and implemented. The resultant system, named DIPLOMA, not only diagnoses faults, but additionally provides advice and guidance on the assembly, disassembly, testing, inspection and repair of a highly complex hydro-mechanical device. Additionally it is shown that a highly innovative modelbased system, named MIDAS, can be used to contribute to the provision of diagnostic, explanatory and training facilities for the same hydro-mechanical device. The methods of designing, coding, implementing and evaluating both systems are explored in detail. The successful implementation and evaluation of the DIPLOMA and MIDAS systems has shown that knowledge-based systems are an appropriate tool for the diagnosis of faults in complex hydro-mechanical devices, and that they make a beneficial contribution to the business performance of the host organisation. Furthermore, it has been demonstrated that the most effective and comprehensive knowledge-based approach to fault diagnosis is one which incorporates both deep and shallow knowledge, so that the distinctive advantages of each can be realised in a single application. Finally, the research has provided evidence that the model-based approach to diagnosis is highly flexible, and may, therefore, be an appropriate technique for a wide range of industrial applications. / Science and Engineering Research Council, and Alvey Directorate

No description available.

No / A conceptual design approach is an important stage for the development of a hybrid Knowledge-Based System (KBS) for Low Volume Automotive Manufacturing (LVAM). The development of a hybrid KBS, which is a blend of KBS and Gauging Absences of Pre-requisites (GAP), is proposed for LVAM research. The hybrid KB/GAP system identifies all potential elements of LVAM issues throughout the development of this system. The KBS used in the system design stage of the LVAM system analyses the gap between the existing and the benchmark organisations for an effective implementation through the GAP analysis technique. The proposed KBLVAM model at the design stage explores three major components, namely LVAM car body parts manufacturing perspective, LVAM competitive priorities perspective and LVAM lean environment perspective. Initial results reveal that the KBLVAM system has identified, for each perspective modules and sub-modules, the Problem Categories (PC) in a prioritised manner. / The financial support by the Malaysian Government, Universiti Malaysia Pahang and University of Bradford for this research is grateful acknowledged.

L'interopérabilité entre les systèmes a été identifiée comme un problème majeur auquel sont confrontées les entreprises lorsqu’ils ont le besoin de collaborer avec d'autres organisations et de participer au sein d’un réseau d’entreprises. Pour atteindre une qualité d'interopérabilité supérieure et garantir une collaboration efficace, un certain nombre d'Exigences d'Interopérabilité (EI) doivent être satisfaites. Ainsi, l'interopérabilité doit être vérifiée et continuellement améliorée. L’Analyse de l’Interopérabilité (ANIN) est une manière de vérifier l’interopérabilité des systèmes. Cependant, en général, la notion « d’exigence » est implicite et présentée sous forme de critères d'évaluation dans les approches ANIN. Il a également été identifié que les interdépendances entre les EI ne sont pas explicitement définies. En effet, leurs interdépendances doivent être prises en compte car elles peuvent aider à identifier les impacts sur l'ensemble du système. De plus, la majorité des approches ANIN sont manuelles, ce qui est un processus laborieux et long qui dépend souvent des connaissances « subjectives » des experts. Dans ce contexte, cette recherche propose un Système d'Analyse de l'Interopérabilité basé sur la Connaissance (SAIC) pour soutenir la prise de décision au sein des entreprises en réseau. Une méthodologie « Design Science Research » (DSR) a été adoptée pour mener à bien la contribution proposée. Premièrement, une approche basée sur l’ingénierie des exigences a été adaptée pour obtenir des EI pertinentes, établir un lien entre les EI obtenues et les composantes du système concerné et définir les interdépendances entre les EI. Pour conceptualiser formellement les connaissances sur l’ANIN, en englobant l'ensemble des EI, les problèmes et solutions d'interopérabilité ainsi que leurs relations, nous avons proposé l’Ontologie de l'Analyse de l'Interopérabilité (OAI). Une approche d'Ingénierie Système basée sur des Modèles a été appliquée pour définir les concepts de l'ontologie. Un prototype du SAIC utilisant l'OAI comme modèle de connaissance a été développé sur une plate-forme Java. L'outil résultant peut exploiter les connaissances sur l'interopérabilité et les informations provenant de la situation actuelle des systèmes évalués pour identifier les problèmes et améliorations potentiels. La contribution proposée a été évaluée grâce à une étude de cas basée sur une véritable entreprise en réseau / Enterprise systems’ interoperability has been identified as a significant issue faced by enterprises, which need to collaborate with other companies and participate within Networked Enterprises. To achieve a higher quality of interoperability and ensure an effective collaboration, a certain number of Interoperability Requirements (IRs) should be satisfied. Thus, interoperability should be verified and continuously improved. A manner for verifying the enterprise systems’ interoperability is through the Interoperability Assessment (INAS). However, in general, the notion of “requirement” is implicit and presented as Interoperability Evaluation Criterion (IEC) in the INAS approaches. It also has been identified that the IEC interdependencies are not explicitly defined. Indeed, their interdependencies should be considered as they can support the identification of impacts on the overall system. Further, the majority of the INAS approaches are manual-conducted, which is a laborious and time-consuming process and in many times depends on the “subjective” knowledge of experts, which can be expensive in time and money when hiring external consultants. In this context, this research proposes a Knowledge-Based Interoperability Assessment System (KBIAS) for supporting decision-making within Networked Enterprises. A Design Science Research (DSR) methodology has been adopted for conducting the work. First, A Requirement Engineering (RE) approach has been adapted to elicit and define relevant IRs, which are father related with system’s components. Such IRs are used as IEC during the INAS process. To formally conceptualise the knowledge about the INAS (subsuming the set of IRs, interoperability problems and solutions), we proposed the Ontology of Interoperability Assessment (OIA). A Model-Based System Engineering approach has been applied for defining and organising the concepts of the proposed ontology. A prototype of the KBIAS using the OIA as its knowledge model has been developed in a Java platform. The developed tool can exploit the knowledge about interoperability issues and information from the as-is situation of the assessed systems for identifying potential problems and improvements. The contribution proposed in this research has been evaluated through a case study based on a real Networked Enterprise

It has been suggested 'expert systems' might have a significant role in the future through enabling many more people to access human experts. It is, therefore, important to understand how potential users interact with these computer systems. This study investigates the effect of extension agents' attitudes towards the features and use of an example expert system for rice disease diagnosis and management(POSOP). It also considers the effect of extension agents' personality traits and intelligence on their attitudes towards its use, and the agents' perception of control over using it. Answers to these questions lead to developing better systems and to increasing their adoption. Using structural equation modelling, two models - the extension agents' perceived usefulness of POSOP, and their attitude towards the use of POSOP, were developed (Models ATU and ATP). Two of POSOP's features (its value as a decision support tool, and its user interface), two personality traits (Openness (0) and Extraversion (E)), and the agents' intelligence, proved to be significant, and were evaluated. The agents' attitude towards POSOP's value had a substantial impact on their perceived usefulness and their attitude towards using it, and thus their intention to use POSOP. Their attitude towards POSOP's user interface also had an impact on their attitude towards its perceived usefulness, but had no impact on their attitude towards using it. However, the user interface did contribute to its value. In Model ATU, neither Openness (0) nor Extraversion (E) had an impact on the agents' perceived usefulness indicating POSOP was considered useful regardless of the agents' personality background. However, Extraversion (E) had a negative impact on their intention to use POSOP in Model ATP indicating that 'introverted' agents had a clear intention to use POSOP relative to the 'extroverted' agents. Extension agents' intelligence, in terms of their GPA, had neither an impact on their attitude, nor their subjective norm (expectation of 'others' beliefs), to the use of POSOP. It also had no association with any of the variables in both models. Both models explain and predict that it is likely that the agents will use POSOP. However, the availability of computers, particularly their capacity, are likely to impede its use. Although the agents believed using POSOP would not be difficult, they still believed training would be beneficial. To be a useful decision support tool, the expert system's value and user interface as well as its usefulness and ease of use, are all crucially important to the preliminary acceptance of a system. Most importantly, the users' problems and needs should be assessed and taken into account as a first priority in developing an expert system. Furthermore, the users should be involved in the system development. The results emphasise that the use of an expert system is not only determined by the system's value and its user interface, but also the agents' perceived usefulness, and their attitude towards using it. In addition, the agents' perception of control over using it is also a significant factor. The results suggested improvements to the system's value and its user interface would increase its potential use, and also providing suitable computers, coupled with training, would encourage its use.

<p>The thesis describes steps taken towards using graphic representation and visual modelling support for the knowledge acquisition process in knowledge-based systems – a process commonly regarded as difficult. The performance of the systems depends on the quality of the embedded knowledge, which makes the knowledge acquisition phase particularly significant. During the acquisition phase, a main obstacle to proper extraction of information is the absence of effective modelling techniques.</p><p>The contributions of the thesis are: introducing a methodology for user-centred knowledge modelling, enhancing transparency to support the modelling of content and of the reasoning strategy, incorporating conceptualisation to simplify the grasp of the contents and to support assimilation of the domain knowledge, and supplying a visual compositional logic programming language for adding and modifying functionality.</p><p>The user-centred knowledge acquisition model, proposed in this thesis, applies a combination of different approaches to knowledge modelling. The aim is to bridge the gap between the users (i.e., knowledge engineers, domain experts and end users) and the system in transferring knowledge, by supporting the users through graphics and visualisation. Visualisation supports the users by providing several different views of the contents of the system.</p><p>The Unified Modelling Language (UML) is employed as a modelling language. A benefit of utilising UML is that the knowledge base can be modified, and the reasoning strategy and the functionality can be changed directly in the model. To make the knowledge base more comprehensible and expressive, we incorporated visual conceptualisation into UML’s diagrams to describe the contents. Visual conceptualisation of the knowledge can also facilitate assimilation in a hypermedia system through visual libraries.</p><p>Visualisation of functionality is applied to a programming paradigm, namely relational programming, often employed in artificial intelligence systems. This approach employs Venn-Euler diagrams as a graphic interface to a compositional operator based relational programming language. </p><p>The concrete result of the research is the development of a graphic representation and visual modelling approach to support the knowledge acquisition process. This approach has been evaluated for two different knowledge bases, one built for hydropower development and river regulation and the other for diagnosing childhood diseases.</p>

The thesis describes steps taken towards using graphic representation and visual modelling support for the knowledge acquisition process in knowledge-based systems – a process commonly regarded as difficult. The performance of the systems depends on the quality of the embedded knowledge, which makes the knowledge acquisition phase particularly significant. During the acquisition phase, a main obstacle to proper extraction of information is the absence of effective modelling techniques. The contributions of the thesis are: introducing a methodology for user-centred knowledge modelling, enhancing transparency to support the modelling of content and of the reasoning strategy, incorporating conceptualisation to simplify the grasp of the contents and to support assimilation of the domain knowledge, and supplying a visual compositional logic programming language for adding and modifying functionality. The user-centred knowledge acquisition model, proposed in this thesis, applies a combination of different approaches to knowledge modelling. The aim is to bridge the gap between the users (i.e., knowledge engineers, domain experts and end users) and the system in transferring knowledge, by supporting the users through graphics and visualisation. Visualisation supports the users by providing several different views of the contents of the system. The Unified Modelling Language (UML) is employed as a modelling language. A benefit of utilising UML is that the knowledge base can be modified, and the reasoning strategy and the functionality can be changed directly in the model. To make the knowledge base more comprehensible and expressive, we incorporated visual conceptualisation into UML’s diagrams to describe the contents. Visual conceptualisation of the knowledge can also facilitate assimilation in a hypermedia system through visual libraries. Visualisation of functionality is applied to a programming paradigm, namely relational programming, often employed in artificial intelligence systems. This approach employs Venn-Euler diagrams as a graphic interface to a compositional operator based relational programming language. The concrete result of the research is the development of a graphic representation and visual modelling approach to support the knowledge acquisition process. This approach has been evaluated for two different knowledge bases, one built for hydropower development and river regulation and the other for diagnosing childhood diseases.

This International Workshop is a high quality seminar providing a forum for the exchange of scientific achievements between research communities of different universities and research institutes in the area of innovation information technologies. It is a continuation of the Russian-German Workshops that have been organized by the universities in Dresden, Karlsruhe and Ufa before. The workshop was arranged in 9 sessions covering the major topics: Modern Trends in Information Technology, Knowledge Based Systems and Semantic Modelling, Software Technology and High Performance Computing, Geo-Information Systems and Virtual Reality, System and Process Engineering, Process Control and Management and Corporate Information Systems.

The modeling of real-world scenes through capturing 3D digital data has proven to be both useful andapplicable in a variety of industrial and surveying applications. Entire scenes are generally capturedby laser scanners and represented by large unorganized point clouds possibly along with additionalphotogrammetric data. A typical challenge in processing such point clouds and data lies in detectingand classifying objects that are present in the scene. In addition to the presence of noise, occlusionsand missing data, such tasks are often hindered by the irregularity of the capturing conditions bothwithin the same dataset and from one data set to another. Given the complexity of the underlyingproblems, recent processing approaches attempt to exploit semantic knowledge for identifying andclassifying objects. In the present thesis, we propose a novel approach that makes use of intelligentknowledge management strategies for processing of 3D point clouds as well as identifying andclassifying objects in digitized scenes. Our approach extends the use of semantic knowledge to allstages of the processing, including the guidance of the individual data-driven processing algorithms.The complete solution consists in a multi-stage iterative concept based on three factors: the modeledknowledge, the package of algorithms, and a classification engine. The goal of the present work isto select and guide algorithms following an adaptive and intelligent strategy for detecting objects inpoint clouds. Experiments with two case studies demonstrate the applicability of our approach. Thestudies were carried out on scans of the waiting area of an airport and along the tracks of a railway.In both cases the goal was to detect and identify objects within a defined area. Results show that ourapproach succeeded in identifying the objects of interest while using various data types