Simulation is an important tool for prediction and assessment of the behavior of complex systems and situations. The importance of simulation has increased tremendously during the last few decades, mainly because the rapid pace of development in the field of electronics has turned the computer from a costly and obscure piece of equipment to a cheap ubiquitous tool which is now an integral part of our daily lives. While such technological improvements make it easier to analyze well-understood deterministic systems, increase in speed and storage capacity alone are not enough when simulating situations where human beings and their behavior are an integral part of the system being studied. The problem with simulation of intelligent entities is that intelligence is still not well understood and it seems that the field of Artificial Intelligence (AI) has a long way to go before we get computers to think like humans. Behavior-based agent modeling has been proposed in mid-80's as one of the alternatives to the classical AI approach. While used mainly for the control of specialized robotic vehicles with very specific sensory capabilities and limited intelligence, we believe that a behavior-based approach to modeling generic autonomous agents in complex environments can provide promising results. To this end, we are investigating a behavior-based model for controlling groups of collaborating and competing agents in a geographic terrain. In this thesis, we are focusing on scenarios of military nature, where agents can move within the environment and adversaries can eliminate each other through use of weapons. Different aspects of agent behavior like navigation to a goal or staying in group formation, are implemented by distinct behavior modules and the final observed behavior for each agent is an emergent property of the combination of simple behaviors and their interaction with the environment. Our experiments show that while such an approach is quite efficient in terms of computational power, it has some major drawbacks. One of the problems is that reactive behavior-based navigation algorithms are not well suited for environments with complex mobility constraints where they tend to perform much worse than proper path planning. This problem represents an important research question, especially when it is considered that most of the modern military conflicts and operations occur in urban environments. One of the contributions of this thesis is a novel approach to reactive navigation where goals and terrain information are fused based on the idea of transforming a terrain with obstacles into a virtual obstacle-free terrain. Experimental results show that our approach can successfully combine the low run-time computational complexity of reactive methods with the high success rates of classical path planning. Another interesting research problem is how to deal with the unpredictable nature of emergent behavior. It is not uncommon to have situations where an outcome diverges significantly from the intended behavior of the agents due to highly complex nonlinear interactions with other agents or the environment itself. Chances of devising a formal way to predict and avoid such abnormalities are slim at best, mostly because such complex systems tend to be be chaotic in nature. Instead, we focus on detection of deviations through tracking group behavior which is a key component of the total situation awareness capability required by modern technology-oriented and network-centric warfare. We have designed a simple and efficient clustering algorithm for tracking of groups of agent suitable for both spatial and behavioral domain. We also show how to detect certain events of interest based on a temporal analysis of the evolution of discovered clusters.

In a dynamically monitored environment the analyst team need timely and accurate information to conduct proactive action over complex situations. Typically, there are thousands of reported activities in a real time operation, therefore steps are taken to direct the analyst’s attention to the most important activity. The data fusion community have introduced the information fusion model, with multiple situational assessments. Each process lends itself to ranking the most important activities into a predetermined order. Unfortunately, the capability of a real time system can be hindered by the knowledge limitation problem, particularly when the underlying system is processing multiple sensor information. Consequently, the situational awareness domains may not rank the identified situation as perfect, as desired by the decision-making resources. This thesis presents advanced research carried out to evaluate the ranking capability of information from the situational awareness domains: perception, comprehension and projection. The Ranking Capability Score (RCS) has been designed for evaluating the prioritisation process. The enhanced (RCS) has been designed for addressing the knowledge representation problem in the user system relation under a situational assessment where the proposed number of tracking activities are dynamically shifted. Finally, the Scheduling Capability Score was designed for evaluating the scheduling capability of the situational awareness system. The proposed performance metrics have been successful in fulfilling their objectives. Furthermore, they have been validated and evaluated using an analytical approach, through conducting a rigorous analysis of the prioritisation and scheduling processes, despite any constraints related to a domain-specific configuration.

In this thesis, an extension of the existing localization system of the ABSOLUT project is presented, with the aim of making it more resistant to GNSS errors. This enhanced system is based on the integration of a LiDAR sensor. Initially, a 3D map of the traversed route is created using the LiDAR sensor. This process employs an existing factor graph-based SLAM algorithm, which is made more stable and accurate through the inclusion of a surveyed elevation profile of the environment, the integration of vehicle odometry sensors, and bias estimates of the IMU. The generated map is used during the drive to determine the vehicle's position by comparing it with the currently captured point clouds. This procedure relies on a newly developed Error-State Kalman Filter that fuses LiDAR odometry with absolute LiDAR position estimates. To optimally use the pose estimation from the various sensor systems, an approach is proposed that adaptively combines the estimates based on the environment. The performance of the developed system is evaluated using real driving data.

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Axlebox bearings are one of the most critical components of a rail vehicle with regard to safety. An axlebox bearing that breaks during operation can be dangerous for the passengers and expensive for the operator. In-service failure of axlebox bearings has been the cause of many catastrophic accidents. Thus, it is of utmost importance to predict bearing failures as early as possible. This will increase reliability and safety of the vehicle as well as reduce the vehicle maintenance cost. Monitoring of roller bearings is an active research eld, and many methods have been proposed by other researchers. Many of these methods employ complex algorithms to make the most use of the given measurements. The algorithms often lack interpretability and have high computational costs, making them dicult to employ in an on-board system. This thesis proposes an interpretable and transparent algorithm that predicts bearing damages with high accuracy. Meanwhile, it tries to retain interpretability as much as possible. The algorithm is based on Empirical Mode Decomposition (EMD) and Singular Value Decomposition (SVD). These two techniques extract essential and meaningful information from the axlebox accelerations. The algorithm is benchmarked on two benchmark datasets, and the results are compared to the respective literature. Then the algorithm is employed on the railway axlebox acceleration measurements that were taken on an axlebox test bench available at SWJTU. The proposed algorithm can be extended to incorporate additional measurements of dierent types, e.g. sound or temperature measurements. The incorporation of other types of measurements will improve the performance of the algorithm even further. / Axelbox lager är en av de viktigaste komponenterna i ett järnvägsfordon när det berör säkerheten. Ett axelbox lager som havererar under drift kan vara farligt for passagerarna och även dyrt för operatören. Driftfel av lagren har varit orsaken till många katastrofala olyckor. Därför är det av yttersta vikt att förutsäga lagerfel så tidigt som möjligt. Detta ökar fordonets tillförlitlighet och säkerhet samt minskar underhållskostnaderna. Mycket forskning har utförts inom övervakning av rullager. Många metoder använder komplexa algoritmer för att maximalt utnyttja matningarna. Algoritmerna saknar ofta tolkbarhet och har höga beräkningskostnader, vilket gör dem svåra att använda i ett integrerat system. Denna avhandling kombinerar era metoder för databehandling och maskininlärning till en algoritm som kan förutsäga lagerskador med hög precision, samtidigt som tolkningsförmågan bibehalls. Bland andra välkända metoder sa använder algoritmen Empirical Mode Decomposition (EMD) och Singular Value Decomposition (SVD) för att extrahera väsentlig information for vibrationsmätningarna. Algoritmen testas sedan med tre olika vibrationsdatamängder, varav en mättes specikt med tanke på simulering av axelbox lager. Ett annat mål med algoritmen är att göra den tillämpad för ytterligare mätningar. Det bör vara möjligt att inkludera mätningar av olika slag, dvs ljud- eller temperaturmätningar, och därigenom förbättra resultaten. Detta skulle minska implementeringskostnaden avsevärt eftersom befintliga sensorer används för detta ändamål. I händelsen av att de föreslagna metoderna inte fungerar med nya mätningar är det även möjligt att integrera ytterligare funktioner i algoritmen.

Intra-dialytic hypotension (IDH) is the most common complication in haemodialysis (HD) treatment and has been linked with increased mortality in HD patients. Despite various approaches towards understanding the underlying physiological mechanisms giving rise to IDH, the causes of IDH are poorly understood. Heart Rate Variability (HRV) has previously been suggested as a predictive measure of IDH. In contrast to conventional spectral HRV measures in which the frequency bands are defined by fixed limits, a new spectral measure of HRV is introduced in which the breathing rate is used to identify and measure the physiologically-relevant peaks of the frequency spectrum. The ratio of peaks leading up to the IDH event was assessed as a possible measure for IDH prediction. Changes in the proposed measure correlate well with the magnitude of abrupt changes in blood pressure in patients with autonomic dysfunction, but there is no such correlation in patients without autonomic dysfunction. At present, routine clinical vital sign monitoring beyond simple weight and blood pressure measurements at the start and end of each session has not established itself in clinical practice. To investigate the benefits of continuous vital sign monitoring in HD patients with regard to detecting and predicting IDH, different population-based and patient-specific models of normality were devised and tested on data from an observational study at the Oxford Renal Unit in which vital signs were recorded during HD sessions. Patient-specific models of normality performed better in distinguishing between IDH and non-IDH data, primarily due to the wide range of vital sign data included as part of the training data in the population-based models. Further, a patient-specific data fusion model was constructed using Parzen windows to estimate a probability density function from the training data consisting of vital signs from IDH-free sessions. Although the model was constructed using four vital sign inputs, novelty detection was found to be primarily driven by blood pressure decreases.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: This thesis describes the development of a distributed control system for SSL soccer robots. The project continues on work done to develop a robotics research platform at Stellenbosch University. The wireless communication system is implemented using Player middleware. This enables high level programming of the robot drivers and communication clients, resulting in an easily modifiable system. The system is developed to be used as either a centralised or decentralised control system. The software of the robot’s motor controller unit is updated to ensure optimal movement. Slippage of the robot’s wheels restricts the robot’s movement capabilities. Trajectory tracking software is developed to ensure that the robot follows the desired trajectory while operating within its physical limits. The distributed control architecture reduces the robots dependency on the wireless network and the off-field computer. The robots are given some autonomy by integrating the navigation and control on the robot self. Kalman filters are designed to estimate the robots translational and rotational velocities. The Kalman filters fuse vision data from an overhead vision system with inertial measurements of an on-board IMU. This ensures reliable and accurate position, orientation and velocity information on the robot. Test results show an improvement in the controller performance as a result of the proposed system. / AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die ontwikkeling van ’n verspreidebeheerstelsel vir SSL sokker robotte. Die projek gaan voort op vorige werk wat gedoen is om ’n robotika navorsingsplatform aan die Universiteit van Stellenbosch te ontwikkel. Die kommunikasiestelsel is geïmplementeer met behulp van Player middelware. Dit stel die robotbeheerders en kommunikasiekliënte in staat om in hoë vlak tale geprogrameer te word. Dit lei tot ’n maklik veranderbare stelsel. Die stelsel is so ontwikkel dat dit gebruik kan word as óf ’n gesentraliseerde of verspreidebeheerstelsel. Die sagteware van die motorbeheer eenheid is opgedateer om optimale robot beweging te verseker. As die robot se wiele gly beperk dit die robot se bewegingsvermoëns. Trajekvolgings sagteware is ontwikkel om te verseker dat die robot die gewenste pad volg, terwyl dit binne sy fisiese operasionele grense bly. Die verspreibeheerargitektuur verminder die robot se afhanklikheid op die kommunikasienetwerk en die sentrale rekenaar. Die robot is ’n mate van outonomie gegee deur die integrasie van die navigasie en beheer op die robot self te doen. Kalman filters is ontwerp om die robot se translasie en rotasie snelhede te beraam. Die Kalman filters kombineer visuele data van ’n oorhoofse visiestelsel met inertia metings van ’n IMU op die robot. Dit verseker betroubare en akkurate posisie, oriëntasie en snelheids inligting. Toetsresultate toon ’n verbetering in die beheervermoë as ’n gevolg van die voorgestelde stelsel.

Cette thèse se situe dans le cadre de l'interprétation d'images satellite à haute résolution, et concerne plus spécifiquement la mise à jour de bases de données cartographiques grâce à des images optique et radar à haute résolution. Cette étude présente une chaîne de traitement générique pour la création ou la mise à jour de bases de données représentant les routes ou les bâtiments en milieu urbain. En fonction des données disponibles, différents scénarios sont envisagés. Le traitement est effectué en deux étapes. D'abord nous cherchons les objets qui doivent être retirés de la base de données. La seconde étape consiste à rechercher dans les images de nouveaux objets à ajouter dans la base de données. Pour réaliser ces deux étapes, des descripteurs sont construits dans le but de caractériser les objets d'intérêt dans les images d'entrée. L'inclusion ou élimination des objets dans la base de données est basée sur un score obtenu après fusion des descripteurs dans le cadre de la théorie de Dempster-Shafer. Les résultats présentés dans cette thèse illustrent l'intérêt d'une fusion multi-capteurs. De plus l'intégration aisée de nouveaux descripteurs permet à la chaîne d'être améliorable et adaptable à d'autres objets. / This work takes place in the framework of high resolution remote sensing image analysis. It focuses on the issue of cartographic database creation or updating with optical and SAR images. The goal of this work is to build a generic processing chain to update or create a cartographic database representing roads and buildings in built-up areas. According to available data, various scenarios are foreseen. The proposed processing chain is composed of two steps. First, if a database is available, the presence of each database object is checked in the images. The second step consist of looking for new objects that should be included in the database. To determine if an object should be present in the updated database, relevant features are extracted from images in the neighborhood of the considered object. Those features are based on caracteristics of roads and buildings in SAR and optical images. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of the Dempster-Shafer evidence theory. Results highlight the interest of multi sensor fusion. Moreover the chosen framework allows the easy integration of new features in the processing chain.