The primary purpose of this research is to explore the relationships between Beer's viable system model (VSM) and mainstream functionalist organisation theory.The latter is taken to include the classical, behavioural and systems models of organisation. For completeness, we also consider organisation theory situated in the interpretive, radical humanist and radical structuralist paradigms of Burrell and Morgan's (1979) sociological grid. Models of mainstream organisation theory have been used extensively by organisation theorists in the structuring of organisations and the design of information systems. Little interest, however, has been paid by organisation theorists to Beer's VSM, which is also used by cyberneticians to structure organisations and design information systems. The problem is that both camps have developed in isolation from one another. Theorists in each camp advocate their own stance regardless what the other might have to offer to their thinking. This situation is a result of a gap between the two camps owing to lack of dialogue between them. The aim of this thesis is to attempt to bridge the gap between the two camps. It is the author's firm belief that this is best done by adopting a complementary approach to pinpoint domains of support each camp may offer to the other. The outcome of this approach is an enhanced model of organisation. Part One of the research begins by introducing the science of cybernetics. Its history, tools, techniques and concepts are then put in place. Building on cybernetic tools and techniques, Beer developed a model of any viable system. Beer's VSM is presented in Chapter 2. Part Two of the thesis is totally devoted to organisational theory. First, we take up models of the functionalist mainstream organisation theory. The approach adopted is first to elaborate on each model, then to contrast each with the VSM. Attention is then directed to organisation theory located in the alternative paradigms, that is, the interpretive, radical humanist and radical structuralist paradigms, respectively. Again, theory of organisation within the above mentioned paradigms is contrasted with the VSM. We mark the end of Part Two by presenting an enhanced model of organisation. This model is the outcome of the comparison which took place between the functionalist organisation theory and the VSM. The argument is that the likelihood of the classical model providing support to the VSM is slim. In fact, the former stands to gain much from the VSM, particularly from the notion of recursive structures which explains how control and communication systems must be designed and organised. The behavioural model, which takes the informal aspects of organisation as its core, appears to be a useful adjunct to the VSM, which concentrates primarily on the formal organisation. Again, the behavioural model stands to gain much from the insights offered by the VSM. At least, the view of openness to the environment would surely give the behavioural model a boost in the right direction. However, we focus our interest on the systems model of organisation, specifically, the notion of semiautonomous work groups encapsulated in the sociotechnical systems approach. By incorporating this notion into the VSM we can, it is hoped, enhance the VSM. Once again, the insights of the VSM, especially that of recursivity of its structure, is of immense significance. In Part Three, the enhanced model is put to the test. This is done by applying it to an existing pharmaceutical manufacturer. The model proves to be not only practical, but also powerful in highlighting domains requiring attention if the effectiveness and efficiency of the organisation in concern is to improve, which the VSM, on its own, cannot provide.

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Despite the number of approaches established for Multiple Criteria Optimisation Problems, few of them have been developed for the decision making process. This research work proposes a new methodology for the solution of optimisation problems that involve multiple criteria emphasising the Decision-Maker's (DM's) preferences model and the use of evolutionary computation techniques and fuzzy logic. The use of genetic algorithms (GAs) is of vital importance to the development of this research. The use of operations research (OR) techniques and decision analysis is also considered vital. The aim of this project is to provide a definition of hybrid approaches that combine the strengths of GA and decision analysis. For this reason four hybrid models are proposed: 1. The GA-SEMOPS. 2. The fuzzy multiobjective genetic optimiser. 3. The GA-PROTRADE. 4. The interactive procedure for multiple objective optimisation problems. The main characteristics of these approaches are that they handle the DM's preferences in an interactive way and their objective functions are formulated using goal levels and surrogate functions. In order to demonstrate that these models can be used in different optimisation problems they have been applied to different case studies covering examples from environmental systems to land and human resource allocation. Each model was studied in depth, comparing the results found with those available in literature. In the majority of the cases, it was found that they performed better than existing methods. The investigations carried out showed that the proposed hybrid models can be considered as a very powerful tool for the solution of a wide variety of optimisation problems in situations from business to science and engineering.

The dissertation presents the investigation and development of novel adaptive computational techniques that provide a high level of performance when searching complex high-dimensional design spaces characterised by heavy non-linear constraint requirements. The objective is to develop a set of adaptive search engines that will allow the successful negotiation of such spaces to provide the design engineer with feasible high performance solutions. Constraint optimisation currently presents a major problem to the engineering designer and many attempts to utilise adaptive search techniques whilst overcoming these problems are in evidence. The most widely used method (which is also the most general) is to incorporate the constraints in the objective function and then use methods for unconstrained search. The engineer must develop and adjust an appropriate penalty function. There is no general solution to this problem neither in classical numerical optimisation nor in evolutionary computation. Some recent theoretical evidence suggests that the problem can only be solved by incorporating a priori knowledge into the search engine. Therefore, it becomes obvious that there is a need to classify constrained optimisation problems according to the degree of available or utilised knowledge and to develop search techniques applicable at each stage. The contribution of this thesis is to provide such a view of constrained optimisation, starting from problems that handle the constraints on the representation level, going through problems that have explicitly defined constraints (i.e., an easily computed closed form like a solvable equation), and ending with heavily constrained problems with implicitly defined constraints (incorporated into a single simulation model). At each stage we develop applicable adaptive search techniques that optimally exploit the degree of available a priori knowledge thus providing excellent quality of results and high performance. The proposed techniques are tested using both well known test beds and real world engineering design problems provided by industry.

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The development of algorithms to efficiently determine an optimal path through a complex environment is a continuing area of research within Computer Science. When such environments can be represented as a graph, established graph search algorithms, such as Dijkstra’s shortest path and A*, can be used. However, many environments are constructed from a set of regions that do not conform to a discrete graph. The Weighted Region Problem was proposed to address the problem of finding the shortest path through a set of such regions, weighted with values representing the cost of traversing the region. Robust solutions to this problem are computationally expensive since finding shortest paths across a region requires expensive minimisation. Sampling approaches construct graphs by introducing extra points on region edges and connecting them with edges criss-crossing the region. Dijkstra or A* are then applied to compute shortest paths. The connectivity of these graphs is high and such techniques are thus not particularly well suited to environments where the weights and representation frequently change. The Field D* algorithm, by contrast, computes the shortest path across a grid of weighted square cells and has replanning capabilites that cater for environmental changes. However, representing an environment as a weighted grid (an image) is not space-efficient since high resolution is required to produce accurate paths through areas containing features sensitive to noise. In this work, we extend Field D* to weighted simplicial complexes – specifically – triangulations in 2D and tetrahedral meshes in 3D. Such representations offer benefits in terms of space over a weighted grid, since fewer triangles can represent polygonal objects with greater accuracy than a large number of grid cells. By exploiting these savings, we show that Triangulated Field D* can produce an equivalent path cost to grid-based Multi-resolution Field D*, using up to an order of magnitude fewer triangles over grid cells and visiting an order of magnitude fewer nodes. Finally, as a practical demonstration of the utility of our formulation, we show how Field D* can be used to approximate a distance field on the nodes of a simplicial complex, and how this distance field can be used to weight the simplicial complex to produce contour-following behaviour by shortest paths computed with Field D*.

This study presents new techniques for the analysis of data acquired by focal plane array Fourier transform infrared (FPA-FTIR) spectroscopy. FPA-FTIR spectrometers are capable of acquiring several orders of magnitude more data than conventional FTIR spectrometers, necessitating the use of novel data analysis techniques to exploit the information-rich nature of these infrared imaging systems. The techniques investigated in this study are demonstrated in the context of bacteria identification by FPA-FTIR spectroscopy. Initially, an examination is made of the image fidelity of three FPA-FTIR instruments and demonstrates the high degree of within-image and between-image variability that is encountered with this technology. This is followed by a description of the development of pixel filtration routines that allow for the extraction of the most representative data from the infrared images of non-uniform samples. A genetic algorithm (GA) approach is introduced for determining the relevancy of spectral features in relation to bacterial classification and is compared to other forms of classifier optimizations. A proof-of-concept study demonstrating the potential use of infrared imaging to detect bacterial samples originating from a mixed culture is then presented. Finally, an overall methodology involving the combination of these data analysis techniques and including additional approaches towards the development, maintenance, and validation of databases based on infrared imaging data is described. This methodology has been developed with an emphasis on accessibility by implementing the elements of an expert system which allows for this technology to be employed by a non-technical user. / Cette étude présente une nouvelle approche d'analyse de données spectrales résultant de l'utilisation de la spectroscopie infrarouge à transformée de Fourier couplée à un détecteur type «matrice à plan focal» (FPA-FTIR) à balayage rapide. Les spectromètres FPA-FTIR ont une capacité de capture de données de plusieurs ordres de grandeur supérieurs aux spectromètres traditionnels et nécessitent donc des techniques avancées d'analyse de données pour exploiter cette mine d'information que représente l'imagerie infrarouge. La spectroscopie FPA-FTIR a été utilisée dans cette étude pour l'identification des bactéries. L'étape initiale, celle de la comparaison de trois spectromètres FPA-FTIR sur les points de vue fidélité de l'image, tant image-image qu'entre images, a révélé de grandes variabilités qui sont propres à cette technologie. Cette étape est suivie du développement de routines de filtration de pixels permettant d'extraire les données caractéristiques de l'imagerie infrarouge des échantillons non-uniformes. Un algorithme génétique (GA) est ensuite introduit pour déterminer la pertinence des caractéristiques spectrales sur le plan de la classification bactérienne et a été comparé à d'autres formes de classification optimisée. Une étude de démonstration de la capacité de la technologie d'imagerie infrarouge pour la détection des échantillons de bactéries provenant de cultures mixtes s'en est suivie. Pour terminer, une méthodologie globale combinant ces techniques d'analyse de données et incluant d'autres étapes telles le développement, la mise à niveau et la validation des bases de données d'imagerie infrarouge a été présentée. Cette méthodologie met l'emphase sur le développement et l'implantation d'un système expert accessible d'utilisation à de non-experts.

In this thesis, we analyze a context-dependent movie recommendation system using a Hierarchical Bayesian Network. Unlike most other recommender systems which either do not consider context or do so using collaborative filtering, our approach is content-based. This allows users to individually interpret contexts or invent their own contexts and continue to get good recommendations. By using a Hierarchical Bayesian Network, we can provide context recommendations when users have only provided a small amount of information about their preferences per context. At the same time, our model has enough degrees of freedom to handle users with different preferences in different contexts. We show on a real data set that using a Bayesian Network to model contexts reduces the error on cross-validation over models that do not link contexts together or ignore context altogether. / Dans cette thèse, nous analysons un système de recommandations de films dépendant du contexte en utilisant un réseau Bayésien hiérarchique. Contrairement à la plupart des systèmes de recommendations qui, soit ne considère pas le contexte, soit le considère en utilisant le filtrage collaboratif, notre approche est basée sur le contenu. Ceci permet aux utilisateurs d'interpréter les contextes individuellement ou d'inventer leurs propres contextes tout en obtenant toujours de bonnes recommandations. En utilisant le rèseau Bayésien hiérarchique, nous pouvons fournir des recommendations en contexte quand les utilisateurs n'ont fourni que quelques informations par rapport à leurs préférences dans différents contextes. De plus, notre modèle a assez de degrés de liberté pour prendre en charge les utilisateurs avec des préférences différentes dans différents contextes. Nous démontrons sur un ensemble de données réel que l'utilisation d'un réseau Bayésien pour modéliser les contextes réduit l'erreur de validation croisée par rapport aux modèles qui ne lient pas les contextes ensemble ou qui ignore tout simplement le contexte.

We explore the use of co-training to improve the performance of image classification in the setting where multiple classifiers are used and several types of features are available. Features are assigned to classifiers in an optimal manner using hierarchical clustering with a distance metric based on conditional mutual information. The effect of increasing the number of classifiers is then evaluated by co-training using the assigned feature sets. Experimental results indicate that the feature assignments chosen by the clustering approach afford superior co-training performance in comparison to other logical assignment choices. The results also indicate that increasing the number of classifiers beyond two leads to improved performance provided that the classifiers are sufficiently independent, and are reasonable well balanced in terms of labeling ability.Additionally, we explore the effect that the initial training set selectionhas on co-training performance. We find that the quality of training imageshas a profound effect on performance and provide recommendations for howbest to select these images. / Nous explorons l'utilisation de la co-formation pour améliorer la performance de classification d'image dans un milieu où multiples classificateurs s'emploient et plusieurs types de caractéristiques sont disponibles. Les caractéristiques sont associés aux classificateurs d'une manière optimal en employant le groupage hiérarchique avec une mesure de distance basée sur l'information mutuelle conditionnelle. L'effet d'augmenter le nombre de classificateurs est alors evalué par la co-formation, en employant les ensembles de caractéristiques attribués. Les résultats de nos expériences indique que si on augmente le nombre de classificateurs au-delà de deux, la performance s'améliore pourvu que les caractéristiques soient suffisamment indépendantes et assez bien équilibrées en termes de compétence d'étiquetage. En plus, nous explorons l'effet de l'ensemble choisi pour l'entraînement initial sur la performance en co-formation. Nous trouvons que la qualité d'images dans l'entraînement a un effet profond sur la performance, et nous fournissons des recommandations sur comment sélectionner ces images pour le meilleur effet.