In this study, a method is developed for modelling and classifying behaviour of maritime vessels in a piracy situation. Prior knowledge is used to construct a probabilistic graphical model of maritime vessel behaviour. This model is a novel variant of a dynamic Bayesian network (DBN), that extends the switching linear dynamic system (SLDS) to accommodate contextual information. A generative model and a classifier model are developed. The purpose of the generative model is to generate simulated data by modelling the behaviour of fishing vessels, transport vessels and pirate vessels in a maritime piracy situation. The vessels move, interact and perform various activities on a predefined map. A novel methodology for evaluating and optimising the generative model is proposed. This methodology can easily be adapted to other applications. The model is evaluated by comparing simulation results with 2011 pirate attack reports. The classifier model classifies maritime vessels into predefined categories according to their behaviour. The classification is performed by inferring the class of a vessel as a fishing, transport or pirate vessel class. The classification method is evaluated by classifying the data generated by the generative model and comparing it to the true classes of the simulated vessels. / Thesis (PhD)--University of Pretoria, 2014. / tm2015 / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

A utilização de Redes Neurais Artificias para fins de identificação e controle de sistemas dinâmicos têm recebido atenção especial de muitos pesquisadores, principalmente no que se refere a sistemas não lineares. Neste trabalho é apresentado um estudo sobre a utilização de um tipo em particular de Rede Neural Artificial, uma Perceptron Multicamadas com Atraso de Tempo, na estimação de estados da etapa fermentativa do processo de Reichstein para produção de vitamina C. A aplicação de Redes Neurais Artificiais a este processo pode ser justificada pela existência de problemas associados à esta etapa, como variáveis de estado não mensuráveis e com incertezas de medida e não linearidade do processo fermentativo, além da dificuldade em se obter um modelo convencional que contemple todas as fases do processo. É estudado também a eficácia do algoritmo de Levenberg-Marquadt, na aceleração do treinamento da Rede Neural Artificial, além de uma comparação do desempenho de estimação de estados das Redes Neurais Artificiais estudadas com o filtro estendido de Kalman, baseado em um modelo não estruturado do processo fermentativo. A análise do desempenho das Redes Neurais Artificiais estudadas é avaliada em termos de uma figura de mérito baseada no erro médio quadrático sendo feitas considerações quanto ao tipo da função de ativação e o número de unidades da camada oculta. Os dados utilizados para treinamento e avaliação da Redes Neurais Artificiais foram obtidos de um conjunto de ensaios interpolados para o intervalo de amostragem desejado. / ldentification and Control of dynamic systems using Artificial Neural Networks has been widely investigated by many researchers in the last few years, with special attention to the application of these in nonlinear systems. ls this works, a study on the utilization of a particular type of Artificial Neural Networks, a Time Delay Multi Layer Perceptron, in the state estimation of the fermentative phase of the Reichstein process of the C vitamin production. The use of Artificial Neural Networks can be justified by the presence of problems, such as uncertain and unmeasurable state variables and process non-linearity, and by the fact that a conventional model that works on all phases of the fermentative processes is very difficult to obtain. The efficiency of the Levenberg Marquadt algorithm on the acceleration of the training process is also studied. Also, a comparison is performed between the studied Artificial Neural Networks and an extended Kalman filter based on a non-structured model for this fermentative process. The analysis of lhe Artificial Neural Networks is carried out using lhe mean square errors taking into consideration lhe activation function and the number of units presents in the hidden layer. A set of batch experimental runs, interpolated to the desired time interval, is used for training and validating the Artificial Neural Networks.

A utilização de Redes Neurais Artificias para fins de identificação e controle de sistemas dinâmicos têm recebido atenção especial de muitos pesquisadores, principalmente no que se refere a sistemas não lineares. Neste trabalho é apresentado um estudo sobre a utilização de um tipo em particular de Rede Neural Artificial, uma Perceptron Multicamadas com Atraso de Tempo, na estimação de estados da etapa fermentativa do processo de Reichstein para produção de vitamina C. A aplicação de Redes Neurais Artificiais a este processo pode ser justificada pela existência de problemas associados à esta etapa, como variáveis de estado não mensuráveis e com incertezas de medida e não linearidade do processo fermentativo, além da dificuldade em se obter um modelo convencional que contemple todas as fases do processo. É estudado também a eficácia do algoritmo de Levenberg-Marquadt, na aceleração do treinamento da Rede Neural Artificial, além de uma comparação do desempenho de estimação de estados das Redes Neurais Artificiais estudadas com o filtro estendido de Kalman, baseado em um modelo não estruturado do processo fermentativo. A análise do desempenho das Redes Neurais Artificiais estudadas é avaliada em termos de uma figura de mérito baseada no erro médio quadrático sendo feitas considerações quanto ao tipo da função de ativação e o número de unidades da camada oculta. Os dados utilizados para treinamento e avaliação da Redes Neurais Artificiais foram obtidos de um conjunto de ensaios interpolados para o intervalo de amostragem desejado. / ldentification and Control of dynamic systems using Artificial Neural Networks has been widely investigated by many researchers in the last few years, with special attention to the application of these in nonlinear systems. ls this works, a study on the utilization of a particular type of Artificial Neural Networks, a Time Delay Multi Layer Perceptron, in the state estimation of the fermentative phase of the Reichstein process of the C vitamin production. The use of Artificial Neural Networks can be justified by the presence of problems, such as uncertain and unmeasurable state variables and process non-linearity, and by the fact that a conventional model that works on all phases of the fermentative processes is very difficult to obtain. The efficiency of the Levenberg Marquadt algorithm on the acceleration of the training process is also studied. Also, a comparison is performed between the studied Artificial Neural Networks and an extended Kalman filter based on a non-structured model for this fermentative process. The analysis of lhe Artificial Neural Networks is carried out using lhe mean square errors taking into consideration lhe activation function and the number of units presents in the hidden layer. A set of batch experimental runs, interpolated to the desired time interval, is used for training and validating the Artificial Neural Networks.

Cette thèse propose une réflexion autour de l'étude des marchés financiers sous le prisme des systèmes complexes.Tout d'abord une description mathématique est proposée pour représenter le processus de prises de décision des agents dès lors où celui-ci bien que représentant les intérêts individuels d'un agent, est également influencé par l'émergence d'un comportement collectif. La méthode est particulièrement applicable lorsque le système étudié est caractérisé par une dynamique non-linéaire. Une application du modèle est proposée au travers de l'implémentation d'un marché artificiel boursier avec une approche système multi-agents. Dans cette application la dynamique du marché est décrite à la fois aux niveaux microscopiques (comportement des agents) et macroscopique (formation du prix). Le processus de décision des agents est défini à partir d'un ensemble de règles comportementales reposant sur des principes de logique floue. La dynamique de la formation du prix repose sur une description déterministe à partir des règles d'appariement d'un carnet d'ordres central tel que sur NYSE-Euronext-Paris. Il est montré que le marché artificiel boursier tel qu'implémenté est capable de répliquer plusieurs faits stylisés des marchés financiers : queue de distribution des rendements plus épaisse que celle d'une loi normale et existence de grappes de volatilité (ou volatility clustering).Par la suite, à partir de simulations numériques il est proposé d'étudier trois grandes propriétés du système : sa capacité d'auto-organisation, de résilience et sa robustesse. Dans un premier temps une méthode est introduite pour qualifier le niveau d'auto-organisation du marché. Nous verrons que la capacité d'auto-organisation du système est maximisée quand les comportements des agents sont diversifiés. Ensuite, il est proposé d'étudier la réponse du système quand celui-ci est stressé via la simulation de chocs de marché. Dans les deux analyses, afin de mettre en évidence comment la dynamique globale du système émerge à partir des interactions et des comportements des agents des résultats numériques sont systématiquement apportés puis discutés.Nos résultats montrent notamment qu'un comportement collectif grégaire apparait à la suite d'un choc, et, entraîne une incapacité temporaire du système à s'auto-organiser. Finalement, au travers des simulations numériques il peut être également remarqué que le marché artificiel boursier implémenté est plus sensible à de faibles répétitions répétées qu'à un choc plus important mais unique. / This thesis suggests reflection in studying financial markets through complex systems prism.First, an original mathematic description for describing agents' decision-making process in case of problems affecting by both individual and collective behavior is introduced. The proposed method is particularly applicable when studied system is characterized by non-linear, path dependent and self-organizing interactions. An application to financial markets is proposed by designing a multi¬agent system based on the proposed formalization.In this application, we propose to implement a computational agent-based financial market in which the system is described in both a microscopie and macroscopic levels are proposed. The agents' decision-making process is based on fuzzy logic rules and the price dynamic is purely deten-ninistic according to the basis matching rules of a central order book as in NYSE-Euronext-Paris. We show that, while putting most parameters under evolutionary control, the computational agent- based system is able to replicate several stylized facts of financial time series (distributions of stocks returns showing a heavy tau l with positive excess kurtosis and volatility clustering phenomenon).Thereafter, with numerical simulations we propose to study three system's properties: self-organization, resilience and robustness. First a method is introduced to quantify the degree of selforganization which ernerges in the system and shows that the capacity of self-organization is maximized when the agents' behaviors are heterogeneous. Secondly, we propose to study the system's response when market shock is simulated. in both cases, numerical results are presentedI and analyzed, showing how the global market behavior emerges from specific individual behavior interactions.Our results notably show that the emergence of collective herding behavior when market shock occurs leads to a temporary disruption on the system self-organization. Finaily, numerical simulations highlight that our artificial financial market can be able to absorb strong mono-shock but be lead to the rupture by low but repeated perturbations.

This thesis project is an experimental study on how to approach quantitative portfolio credit risk modelling in Venture Debt portfolios. Facing a lack of applicable default data from ArK and publicly available sets, as well as seeking to capture companies that fail to service debt obligations before defaulting per se, we present an approach to risk modeling based on trends in revenue. The main framework revolves around driving a Monte Carlo simulation with Copluas to predict future revenue scenarios across a portfolio of early-stage technology companies. Three models for a random Gaussian walk, a Linear Dynamic System and an Autoregressive Integrated Moving Average (ARIMA) time series are implemented and evaluated in terms of their portfolio Value-at-Risk influence. The model performance confirms that modeling portfolio risk in Venture Debt is challenging, especially due to lack of sufficient data and thus a heavy reliance on assumptions. However, the empirical results for Value-at-Risk and Expected Shortfall are in line with expectations. The evaluated portfolio is still in an early stage with a majority of assets not yet in their repayment period and consequently the spread of potential losses within one year is very tight. It should further be recognized that the scope in terms of explanatory variables for sales and model complexities has been narrowed and simplified for computational benefits, transparency and communicability. The main conclusion drawn is that alternative approaches to model Venture Debt risk is fully possible, and should improve in reliability and accuracy with more data feeding the model. For future research it is recommended to incorporate macroeconomic variables as well as similar company analysis to better capture macro, funding and sector conditions. Furthermore, it is suggested to extend the set of financial and operational explanatory variables for sales through machine learning or neural networks. / Detta examensarbete är en experimentell studie för kvantitativ modellering av kreditrisk i Venture Debt-portföljer. Givet en brist på tillgänlig konkurs-data från ArK samt från offentligt tillgängliga databaser i kombination med ambitionen att inkludera företag som misslyckas med skuldförpliktelser innan konkurs per se, presenterar vi en metod för riskmodellering baserad på trender i intäkter. Ramverket för modellen kretsar kring Monte Carlo-simulering med Copluas för att estimera framtida intäktsscenarier över en portfölj med tillväxtbolag inom tekniksektorn. Tre modeller för en random walk, ett linjärt dynamiskt system och ARIMA- tidsserier implementeras och utvärderas i termer av deras inflytande på portföljens Value-at- Risk. Modellens prestationer bekräftar att modellering av portföljrisk inom Venture Debt är utmanande, särskilt på grund av bristen på tillräckliga data och därmed ett stort beroende av antaganden. Dock är de empiriska resultaten för Value-at-Risk och Expected Shortfall i linje med förväntningarna. Den utvärderade portföljen är fortfarande i ett tidigt skede där en majoritet av tillgångarna fortfarande befinner sig i en amorteringsfri period och följaktligen är spridningen av potentiella förluster inom ett år mycket snäv. Det bör vidare tillkännages att omfattningen i termer av förklarande variabler för intäkter och modellkomplexitet har förenklats för beräkningsfördelar, transparens och kommunicerbarhet. Den främsta slutsatsen som dras är att alternativa metoder för att modellera risker inom Venture Debt är fullt möjliga och bör förbättras i tillförlitlighet och precision när mer data kan matas in i modellen. För framtida arbete rekommenderas det att inkorporera makroekonomiska variabler samt analys av liknande bolag för att bättre fånga makro-, finansierings- och sektorsförhållanden. Vidare föreslås det att utöka uppsättningen av finansiella och operationella förklarande variabler för intäkter genom maskininlärning eller neurala nätverk.

Deux thématiques importantes des technologies de la santé: la pratique médicale fondée sur des preuves probantes et l’évaluation des interventions en médecine sont fondées sur une approche positiviste et une conception mécaniste des organisations en santé. Dans ce mémoire, nous soulevons l’hypothèse selon laquelle les théories de la complexité et la systémique permettent une conceptualisation différente de ces deux aspects de la gouvernance clinique d’une unité de Soins Intensifs Chirurgicaux (SIC), qui est considérée comme un système adaptatif dynamique non linéaire qui nécessite une approche systémique de la cognition. L’étude de cas d’une unité de SIC, permet de démontrer par de nombreux exemples et des analyses de micro-situations, toutes les caractéristiques de la complexité des patients critiques et instables et de la structure organisationnelle des SIC. Après une critique épistémologique de l’Evidence-Based Medicine nous proposons une pratique fondée sur des raisonnements cliniques alliant l’abduction, l’herméneutique et la systémique aux SIC. En nous inspirant des travaux de Karl Weick, nous suggérons aussi de repenser l’évaluation des modes d’interventions cliniques en s’inspirant de la notion d’organisation de haute fiabilité pour mettre en place les conditions nécessaires à l’amélioration des pratiques aux SIC. / In Health Technology Assessment and Management, Evidence-Based Medicine and many tools available for clinical assessment reflect a positivistic and mechanistic approach to Health Care Organizations and scientific knowledge. We argue that the Complexity Theories and the Systemic decision-making process give a different insight on those two aspects of Clinical Governance in a Surgical Intensive Care Unit (SICU). In a case-study, we describe the nature of critically ill and unstable patients and the organizational structure of a SICU in a university based hospital. We demonstrate all the characteristics of complexity in that setting, through the use of many examples and micro-situational analysis. After an epistemological critical appraisal of EBM, we suggest that if a SICU is conceptualized as a dynamic non-linear adaptative system, then clinical knowledge and scientific thought processes must include hermeneutical, systemic and abductive types of reasoning. Finally, we draw upon Karl Weick’s work and suggest that a SICU must be considered as a High Reliability Organization in order to aim for improving patient care and create better conditions for quality and performance in this complex environment.

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