This thesis compares three variations of the Bayesian network as an aid for decision-making using uncertain information. After reviewing the basic theory underlying probabilistic graphical models and Bayesian estimation, the thesis presents a user-defined static Bayesian network, a static Bayesian network in which the parameter values are learned from data, and a dynamic Bayesian network with learning. As a basis for the comparison, these models are used to provide a prior assessment of the safety of flight of a small unmanned aircraft, taking into consideration the state of the aircraft and weather. The results of the analysis indicate that the dynamic Bayesian network is more effective than the static networks at predicting safety of flight. / Master of Science

Cette thèse de doctorat analyse les concepts impliqués dans la prise de décisions de groupes d'agents et applique ces concepts dans la création d'un cadre théorique et pratique qui permet la reconnaissance de comportements de groupes. Nous allons présenter une vue d'ensemble de la théorie de l'intention, étudiée dans le passé par quelques grands théoriciens comme Searle, Bratmann et Cohen, et nous allons montrer le lien avec des recherches plus récentes dans le domaine de la reconnaissance de comportements. Nous allons étudier les avantages et inconvénients des techniques les plus avancées dans ce domaine et nous allons créer un nouveau modèle qui représente et détecte les comportements de groupes. Ce nouveau modèle s'appelle Multiagent-Abstract Hidden Markov mEmory Model (M-AHMEM) et résulte de la fusion de modèles déjà existants, le but étant de créer une approche unifiée du problème. La plus grande partie de cette thèse est consacrée à la présentation détaillée du M-AHMEM et de l'algorithme responsable de la reconnaissance de comportements. Notre modèle sera testé sur deux applications différentes : l'analyse gesturale humaine et la fusion multimodale des données audio et vidéo. A travers ces deux applications, nous avançons l'argument qu'un ensemble de données constitué de plusieurs variables corrélées peut être analysé efficacement sous un cadre unifié de reconnaissance de comportements. Nous allons montrer que la corrélation entre les différentes variables peut être modélisée comme une coopération ayant lieu à l'intérieur d'une équipe et que la reconnaissance de comportements constitue une approche moderne de classification et de reconnaissance de patrons.

Stock Market is always being the most important role in modern capital market. And Stock Market is becoming one the most popular investment tools these days. Because of the Globalization of capital markets, the spreading of capital becomes faster and easier. The development of capital markets evoke the interesting of scholars and the field of stock market prediction attract scholars and researchers from different background. There are two approaches of predicting stock market - fundamental analysis and technical analysis. The purpose of my work was to predict three stock markets in the world, namely Taiwan Weighted Index (IDXWT), Hong Kong Hang Seng Index (HSI) and Dow Jones Industrial Average (DJI) using technical analysis and Dynamic Bayesian Network (DBN).This thesis is based on Wang¡¦s thesis [Wan05] ¡§Investment Decision Support with Dynamic Bayesian Networks¡¨. According to different characteristic of 3 stock markets, we divide 3 different markets into 3 experiments. For each market, we expect we can find the best indicators and trading signals. The first experiment involves Taiwan Weighted Index as our prediction target; the second one uses Hong Kong Hang Seng Index and the third experiment employs Dow Jones Industrial Average. As a result, Taiwan Stock market (both 15-day and 20-day Moving Average)can make higher returns than buy-and-hold, RSI_6 and KD. And we also have the same conclusion of Hang Seng Index and Dow Jones Industrial Average. The best return from 15-day MA and 20-day MA of Taiwan Stock market is 47.95% and 60.21%, respectively. Moreover, the best result of Hang Seng Index is 60.06% for 4 years and 25.83% for Dow Jones Industrial Average. All of the best results can make higher returns than each of their buy-and-hold, RSI_6 and KD. In the conclusion, we may say that this paper can provide a direction to investors while they are using these technical indicators to predict these particular stock markets.

Oil/gas and petrochemical plants are complicated and dynamic in nature. Dynamic characteristics include ageing of equipment/components, season changes, stochastic processes, operator response times, inspection and testing time intervals, sequential dependencies of equipment/components and timing of safety system operations, all of which are time dependent criteria that can influence dynamic processes. The conventional risk assessment methodologies can quantify dynamic changes in processes with limited capacity. Therefore, it is important to develop method that can address time-dependent effects. The primary objective of this study is to propose a risk assessment methodology for dynamic systems. In this study, a new technique for dynamic operational risk assessment is developed based on the Bayesian networks, a structure optimal suitable to organize cause-effect relations. The Bayesian network graphically describes the dependencies of variables and the dynamic Bayesian network capture change of variables over time. This study proposes to develop dynamic fault tree for a chemical process system/sub-system and then to map it in Bayesian network so that the developed method can capture dynamic operational changes in process due to sequential dependency of one equipment/component on others. The developed Bayesian network is then extended to the dynamic Bayesian network to demonstrate dynamic operational risk assessment. A case study on a holdup tank problem is provided to illustrate the application of the method. A dryout scenario in the tank is quantified. It has been observed that the developed method is able to provide updated probability different equipment/component failure with time incorporating the sequential dependencies of event occurrence. Another objective of this study is to show parallelism of Bayesian network with other available risk assessment methods such as event tree, HAZOP, FMEA. In this research, an event tree mapping procedure in Bayesian network is described. A case study on a chemical reactor system is provided to illustrate the mapping procedure and to identify factors that have significant influence on an event occurrence. Therefore, this study provides a method for dynamic operational risk assessment capable of providing updated probability of event occurrences considering sequential dependencies with time and a model for mapping event tree in Bayesian network.

Tragically, traffic accidents involving drivers, motorcyclists and pedestrians result in thousands of fatalities worldwide each year. For this reason, making improvements to road safety and saving people's lives is an international priority. In recent years, this aim has been supported by Intelligent Transport Systems, offering safety systems and providing an intelligent driving environment. The development of wireless communications and mobile ad hoc networks has led to improvements in intelligent transportation systems heightening these systems' safety. Vehicular ad hoc Networks comprise an important technology; included within intelligent transportation systems, they use dedicated short-range communications to assist vehicles to communicate with one another, or with those roadside units in range. This form of communication can reduce road accidents and provide a safer driving environment. A major challenge has been to design an ideal system to filter relevant contextual information from the surrounding environment, taking into consideration the contributory factors necessary to predict the likelihood of a crash with different levels of severity. Designing an accurate and effective pre-crash system to avoid front and back crashes or mitigate their severity is the most important goal of intelligent transportation systems, as it can save people's lives. Furthermore, in order to improve crash prediction, context-aware systems can be used to collect and analyse contextual information regarding contributory factors. The crash likelihood in this study is considered to operate within an uncertain context, and is defined according to the dynamic interaction between the driver, the vehicle and the environment, meaning it is affected by contributory factors and develops over time. As a crash likelihood is considered to be an uncertain context and develops over time, any usable technology must overcome this uncertainty in order to accurately predict crashes. This thesis presents a context-aware pre-crash collision prediction system, which captures information from the surrounding environment, the driver and other vehicles on the road. It utilises a Dynamic Bayesian Network as a reasoning model to predict crash likelihood and severity level, whether any crash will be fatal, serious, or slight. This is achieved by combining the above mentioned information and performing probabilistic reasoning over time. The thesis introduces novel context aware on-board unit architecture for crash prediction. The architecture is divided into three phases: the physical, the thinking and the application phase; these which represent the three main subsystems of a context-aware system: sensing, reasoning and acting. In the thinking phase, a novel Dynamic Bayesian Network framework is introduced to predict crash likelihood. The framework is able to perform probabilistic reasoning to predict uncertainty, in order to accurately predict a crash. It divides crash severity levels according to the UK department for transport, into fatal, serious and slight. GeNIe version 2.0 software was used to implement and verify the Dynamic Bayesian Network model. This model has been verified using both syntactical and real data provided by the UK department for transport in order to demonstrate the prediction accuracy of the proposed model and to demonstrate the importance of including a large amount of contextual information in the prediction process. The evaluation of the proposed system delivered high-fidelity results, when predicting crashes and their severity. This was judged by inputting different sensor readings and performing several experiments. The findings of this study has helped to predict the probability of a crash at different severity levels, accounting for factors that may be involved in causing a crash, thereby representing a valuable step towards creating a safer traffic network.

No description available.

Stock market plays an important role in the modern capital market. As a result, the prediction of financial assets attracts people in different areas. Moreover, it is commonly accepted that stock price movement generally follows a major trend. As a result, forecasting the market trend becomes an important mission for a prediction method. Accordingly, we will predict the long term trend rather than the movement of near future or change in a trading day as the target of our predicting approach. Although there are various kinds of analyses for trend prediction, most of them use clear cuts or certain thresholds to classify the trends. Users (or investors) are not informed with the degrees of confidence associated with the recommendation or the trading signal. Therefore, in this research, we would like to study an approach that could offer the confidence of the trend analysis by providing the probabilities of each possible state given its historical data through Dynamic Bayesian Network. We will incorporate the well-known principles of Dow¡¦s Theory to better model the trend of stock movements. Through the results of our experiment, we may say that the financial performance of the proposed model is able to defeat the buy and hold trading strategy when the time scope covers the entire cycle of a trend. It also means that for the long term investors, our approach has high potential to win the excess return. At the same time, the trading frequency and correspondently trading costs can be reduced significantly.

It is important to manage iterative projects in a way to maximize quality and minimize cost. To achieve high quality, accurate project estimates are of high importance. It is challenging to predict the effort that is required to perform test activities in an iterative development. If testers put extra effort in testing then schedule might be delayed, however, if testers spend less effort then quality could be affected. Currently there is no model for test effort prediction in iterative development to overcome such challenges. This paper introduces and validates a dynamic Bayesian network to predict test effort in iterative software development. In this research work, the proposed framework is evaluated in a number of ways: First, the framework behavior is observed by considering different parameters and performing initial validation. Then secondly, the framework is validated by incorporating data from two industrial projects. The accuracy of the results has been verified through different prediction accuracy measurements and statistical tests. The results from the verification confirmed that the framework has the ability to predict test effort in iterative projects accurately.

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

L'analyse de fiabilité fait partie intégrante de la conception et du fonctionnement du système, en particulier pour les systèmes exécutant des applications critiques. Des travaux récents ont montré l'intérêt d'utiliser les réseaux bayésiens dans le domaine de la fiabilité, pour modélisation la dégradation d'un système. Les modèles graphiques de durée sont un cas particulier des réseaux bayésiens, qui permettent de s'affranchir de la propriété markovienne des réseaux bayésiens dynamiques. Ils s'adaptent aux systèmes dont le temps de séjour dans chaque état n'est pas nécessairement distribué exponentiellement, comme c'est le cas dans la plupart des applications industrielles. Des travaux antérieurs ont toutefois montré des limitations à ces modèles en terme de capacité de stockage et de temps de calcul, en raison du caractère discret de la variable temps de séjour. Une solution pourrait consister à considérer une variable de durée continue. Selon les avis d'experts, les variables de temps de séjour suivent une distribution de Weibull dans de nombreux systèmes. L'objectif de la thèse est d'intégrer des variables de temps de séjour suivant une distribution de Weibull dans un modèle de durée graphique en proposant une nouvelle approche. Après une présentation des réseaux bayésiens, et plus particulièrement des modèles graphiques de durée et leur limitation, ce rapport s'attache à présenter le nouveau modèle permettant la modélisation du processus de dégradation. Ce nouveau modèle est appelé modèle graphique de durée hybride Weibull. Un algorithme original permettant l'inférence dans un tel réseau a été mis en place. L'étape suivante a été la validation de l'approche. Ne disposant pas de données, il a été nécessaire de simuler des séquences d'états du système. Différentes bases de données ainsi construites ont permis d'apprendre d'un part un modèle graphique de durée, et d'autre part un modèle graphique de durée hybride-Weibull, afin de les comparer, que ce soit en terme de qualité d’apprentissage, de qualité d’inférence, de temps de calcul, et de capacité de stockage / Reliability analysis is an integral part of system design and operation, especially for systems running critical applications. Recent works have shown the interest of using Bayesian Networks in the field of reliability, for modeling the degradation of a system. The Graphical Duration Models are a specific case of Bayesian Networks, which make it possible to overcome the Markovian property of dynamic Bayesian Networks. They adapt to systems whose sojourn-time in each state is not necessarily exponentially distributed, which is the case for most industrial applications. Previous works, however, have shown limitations in these models in terms of storage capacity and computing time, due to the discrete nature of the sojourn time variable. A solution might be to allow the sojourn time variable to be continuous. According to expert opinion, sojourn time variables follow a Weibull distribution in many systems. The goal of this thesis is to integrate sojour time variables following a Weibull distribution in a Graphical Duration Model by proposing a new approach. After a presentation of the Bayesian networks, and more particularly graphical duration models, and their limitations, this report focus on presenting the new model allowing the modeling of the degradation process. This new model is called Weibull Hybrid Graphical Duration Model. An original algorithm allowing inference in such a network has been deployed. Various so built databases allowed to learn on one hand a Graphical Duration Model, and on an other hand a Graphical Duration Model Hybrid - Weibull, in order to compare them, in term of learning quality, of inference quality, of compute time, and of storage space