This thesis presents an analysis of the performance of optimisation using Kriging surrogate models on low-carbon building design problems. Their performance is compared with established genetic algorithms operating without a surrogate on a range of different types of building-design problems. The advantages and disadvantages of a Kriging approach, and their particular relevance to low-carbon building design optimisation, are tested and discussed. Scenarios in which Kriging methods are most likely to be of use, and scenarios where, conversely, they may be dis- advantageous compared to other methods for reducing the computational cost of optimisation, such as parallel computing, are highlighted. Kriging is shown to be able, in some cases, to find designs of comparable performance in fewer main-model evaluations than a stand-alone genetic algorithm method. However, this improvement is not robust, and in several cases Kriging required many more main-model evaluations to find comparable designs, especially in the case of design problems with discrete variables, which are common in low-carbon building design. Furthermore, limitations regarding the extent to which Kriging optimisa- tions can be accelerated using parallel computing resources mean that, even in the scenarios in which Kriging showed the greatest advantage, a stand-alone genetic algorithm implemented in parallel would be likely to find comparable designs more quickly. In light of this it is recommended that, for most lowcarbon building design problems, a stand-alone genetic algorithm is the most suitable optimisation method. Two novel methods are developed to improve the performance of optimisation algorithms on low-carbon building design problems. The first takes advantage of variables whose impact can be quickly calculated without re-running an expensive dynamic simulation, in order to dramatically increase the number of designs that can be explored within a given computing budget. The second takes advantage of objectives that can be !Keywords To Be Included For Additional Search Power: Optimisation, optimization, Kriging, meta-models, metamodels, low-energy design ! "2 calculated without a dynamic simulation in order to filter out designs that do not meet constraints in those objectives and focus the use of computationally expensive dynamic simulations on feasible designs. Both of these methods show significant improvement over standard methods in terms of the quality of designs found within a given dynamic-simulation budget.

This thesis presents the concept of hybrid semantic-document models to aid information management when using standards for complex technical domains such as military data communication. These standards are traditionally text based documents for human interpretation, but prose sections can often be ambiguous and can lead to discrepancies and subsequent implementation problems. Many organisations produce semantic representations of the material to ensure common understanding and to exploit computer aided development. In developing these semantic representations, no relationship is maintained to the original prose. Maintaining relationships between the original prose and the semantic model has key benefits, including assessing conformance at a semantic level, and enabling original content authors to explicitly define their intentions, thus reducing ambiguity and facilitating computer aided functionality. Through the use of a case study method based on the military standard MIL-STD-6016C, a framework of relationships is proposed. These relationships can integrate with common document modelling techniques and provide the necessary functionality to allow semantic content to be mapped into document views. These relationships are then generalised for applicability to a wider context. Additionally, this framework is coupled with a templating approach which, for repeating sections, can improve consistency and further enhance quality. A reflective approach to model driven web rendering is presented and evaluated. This reflective approach uses self-inspection at runtime to read directly from the model, thus eliminating the need for any generative processes which result in data duplication across source used for different purpose.

In the 1980s, the evolution of engineering methods and techniques yielded the object-oriented approaches. Specifically, object orientation was established in software engineering, gradually relieving structured approaches. In other domains, e.g. systems engineering, object orientation is not well established. As a result, different domains employ different methods and techniques. This makes it difficult to exchange information between the domains, e.g. passing systems engineering information for further refinement to software engineering. This thesis presents a generic principle for bridging the gap between structured and object-oriented specification techniques. The principle enables interoperability of structured and object-oriented analysis and design tools through mutual information exchanges. Therefore, the concepts and elements of representative structured and object-oriented specification techniques are identified and analyzed. Then, a metamodel for each specification technique is created. From the meta-models, a common metamodel is synthesized. Finally, mappings between the meta-models and the common meta-model are created. Used in conjunction, the meta-models, the common meta-model and the mappings enable tool interoperability through transforming specification information under one meta-model via the common meta-model into a representation under another metamodel. Example transformations that illustrate the proposed principle using fragments of an aircraft’s landing gear specification are provided. The work presented in this thesis is based on the achievements of the SEDRES (ESPRIT 20496), SEDEX (NUTEK IPII-98-6292) and SEDRES-2 (IST 11953) projects. The projects strove for integrating different systems engineering tools in the forthcoming ISO-10303-233 (AP-233) standard for systems engineering design data. This thesis is an extension to the SEDRES / SEDEX and AP-233 achievements. It specifically focuses on integrating structured and modern UML based object-oriented specification techniques which was only performed schematically in the SEDRES / SEDEX and AP-233 work.

<p>Simulations are widely used for analysis and design of complex systems. Real-world complex systems are often too complex to be expressed with tractable mathematical formulations. Therefore simulations are often used instead of mathematical formulations because of their flexibility and ability to model real-world complex systems in some detail. Simulation models can often be complex and slow which lead to the development of simulation meta-models that are simpler and faster models of complex simulation models. Artificial neural networks (ANNs) have been studied for use as simulation meta-models with different results. This final year project further studies the use of ANNs as simulation meta-models by comparing the predictability of five different neural network architectures: feed-forward-, generalized feed-forward-, modular-, radial basis- and Elman artificial neural networks where the underlying simulation is of complex production system. The results where that all architectures gave acceptable results even though it can be said that Elman- and feed-forward ANNs performed the best of the tests conducted here. The difference in accuracy and generalization was considerably small.</p>

Simulations are widely used for analysis and design of complex systems. Real-world complex systems are often too complex to be expressed with tractable mathematical formulations. Therefore simulations are often used instead of mathematical formulations because of their flexibility and ability to model real-world complex systems in some detail. Simulation models can often be complex and slow which lead to the development of simulation meta-models that are simpler and faster models of complex simulation models. Artificial neural networks (ANNs) have been studied for use as simulation meta-models with different results. This final year project further studies the use of ANNs as simulation meta-models by comparing the predictability of five different neural network architectures: feed-forward-, generalized feed-forward-, modular-, radial basis- and Elman artificial neural networks where the underlying simulation is of complex production system. The results where that all architectures gave acceptable results even though it can be said that Elman- and feed-forward ANNs performed the best of the tests conducted here. The difference in accuracy and generalization was considerably small.

Este trabalho estuda o problema da construção de sistemas orientados a objetos com características adaptativas, tendo como principal objetivo simplificar o processo de construção. Para isso o trabalho utiliza como base teórica a Tecnologia Adaptativa e sua aplicação em diversos formalismos. O Modelo Adaptativo de Objetos foi utilizado como base de comparação de soluções para a construção de sistemas adaptativos. Nesta pesquisa são apresentadas aplicações e uma proposição para a construção e modelagem de sistemas adaptativos, através da extensão do conceito de objetos com características da tecnologia adaptativa. Através deste estudo avaliou-se o impacto da aplicação do dispositivo adaptativo em um formalismo com tipo. Os resultados obtidos no presente trabalho demonstram que a tecnologia adaptativa é propícia para linguagens orientadas a objetos e que os diagramas UML são capazes, com pequenas extensões, de representar o comportamento adaptativo adequadamente. / This study addresses the issue of implementing object-oriented software with adaptive characteristics, having as primary purpose simplify the implementing process. The key theoretical basis consisted in adaptive technology and its application in various formalisms. Adaptive Object Model has been taken as comparison basis to solutions to implement adaptive systems. This study describes applications and a proposition to implement and model adaptive systems, through the extension of object concept with adaptive technology characteristics. It also evaluates the impact of applying adaptive devices in formalism with types. The results obtained demonstrate that adaptive technology is suitable for object-oriented languages and that UML diagrams are capable of presenting adaptive behavior appropriately with a small number of extensions.

Este trabalho estuda o problema da construção de sistemas orientados a objetos com características adaptativas, tendo como principal objetivo simplificar o processo de construção. Para isso o trabalho utiliza como base teórica a Tecnologia Adaptativa e sua aplicação em diversos formalismos. O Modelo Adaptativo de Objetos foi utilizado como base de comparação de soluções para a construção de sistemas adaptativos. Nesta pesquisa são apresentadas aplicações e uma proposição para a construção e modelagem de sistemas adaptativos, através da extensão do conceito de objetos com características da tecnologia adaptativa. Através deste estudo avaliou-se o impacto da aplicação do dispositivo adaptativo em um formalismo com tipo. Os resultados obtidos no presente trabalho demonstram que a tecnologia adaptativa é propícia para linguagens orientadas a objetos e que os diagramas UML são capazes, com pequenas extensões, de representar o comportamento adaptativo adequadamente. / This study addresses the issue of implementing object-oriented software with adaptive characteristics, having as primary purpose simplify the implementing process. The key theoretical basis consisted in adaptive technology and its application in various formalisms. Adaptive Object Model has been taken as comparison basis to solutions to implement adaptive systems. This study describes applications and a proposition to implement and model adaptive systems, through the extension of object concept with adaptive technology characteristics. It also evaluates the impact of applying adaptive devices in formalism with types. The results obtained demonstrate that adaptive technology is suitable for object-oriented languages and that UML diagrams are capable of presenting adaptive behavior appropriately with a small number of extensions.

Industrial software systems are becoming more complex with a large number of interacting parts distributed over networks. Due to the inherent complexity in the problem domains, most such systems are modified over time to incorporate emerging requirements, making incremental development a suitable approach for building complex systems. In domain specific systems it is the domain experts as end users who identify improvements that better suit their needs. Examples include meteorologists who use weather modeling software, engineers who use control systems and business analysts in business process modeling. Most domain experts are not fluent in systems programming and changes are realised through software engineers. This process hinders the evolution of the system, making it time consuming and costly. We hypothesise that if domain experts are empowered to make some of the system changes, it would greatly ease the evolutionary process, thereby making the systems more effective. Agent Oriented Software Engineering (AOSE) is seen as a natural fit for modeling and implementing distributed complex systems. With concepts such as goals and plans, agent systems support easy extension of functionality that facilitates incremental development. Further agents provide an intuitive metaphor that works at a higher level of abstraction compared to the object oriented model. However agent programming is not at a level accessible to domain experts to capitalise on its intuitiveness and appropriateness in building complex systems. We propose a model driven development approach for domain experts that uses visual modeling and automated code generation to simplify the development and evolution of agent systems. Our approach is called the Component Agent Framework for domain-Experts (CAFnE), which builds upon the concepts from Model Driven Development and the Prometheus agent software engineering methodology. CAFnE enables domain experts to work with a graphical representation of the system , which is easier to understand and work with than textual code. The model of the system, updated by domain experts, is then transformed to executable code using a transformation function. CAFnE is supported by a proof-of-concept toolkit that implements the visual modeling, model driven development and code generation. We used the CAFnE toolkit in a user study where five domain experts (weather forecasters) with no prior experience in agent programming were asked to make changes to an existing weather alerting system. Participants were able to rapidly become familiar with CAFnE concepts, comprehend the system's design, make design changes and implement them using the CAFnE toolkit.

Information demand is a part of comprehensive business logistics which encompass logistics of information. The demand for information has provided a unifying framework for different needs on enterprise modeling. Hence, the problems organizations faces relating to flow and distribution has lead to the development of various framework for analyzing information demand and this is guided by a set of rules, methods and even a unified representation. This thesis work defines a specification for enterprise Information Demand Context model using XPDL as the language of construct. The paper gives reasons why XPDL was preferred for such a representation and show how mapping is carried out from the constructs of notations to its associated XPDL specifications, so that when we are defining a representation we are as well defining its meta model. The resulting specification is presented in such a way that it should be able to give a flexible, logical and more defined structure.

Meta-Modelle sind Rechenmodelle, die das Verhalten technischer Systeme näherungsweise beschreiben oder nachbilden. Sie werden aus Beobachtungen von Simulationsmodellen der technischen Systeme abgeleitet. Es handelt sich also um Modelle von Modellen, um Meta-Modelle. Meta-Modelle unterscheiden sich grundsätzlich von analytischen Ansätzen zur Systembeschreibung. Während analytische Ansätze in ihrer mathematischen Struktur die tatsächlichen Gegebenheiten des betrachteten Systems wiedergeben, sind Meta-Modelle stets Näherungen. Der Vorteil von Meta-Modellen liegt in ihrer einfachen Form. Sie sind leicht zu bilden und anzuwenden. Ihr Nachteil ist die nur annähernde und u.U. unvollständige Beschreibung des Systemverhaltens. Im folgenden wird die Bildung von Meta-Modellen anhand eines Bediensystems dargestellt. Zuerst werden die Möglichkeiten einer analytischen Beschreibung bewertet. Danach werden zwei unterschiedliche Meta-Modelle, Polynome und neuronale Netze, vorgestellt. Möglichkeiten und Grenzen dieser Formen der Darstellung des Systemverhaltens werden diskutiert. Abschließend werden praktische Einsatzfelder von Meta-Modellen in der Materialflußplanung und -simulation aufgezeigt.