Denna studie är ett kognitionsvetenskapligt examensarbete som syftar på att skapa en modell som skapar semantiska representationer utifrån ett mer biologiskt plausibelt tillvägagångssätt jämfört med traditionella metoder. Denna modell kan ses som ett första steg i utredningen av ansatsen som följer. Studien utreder antagandet om Self Organizing Maps kan användas för att skapa semantiska representationer ur stora mängder text utifrån ett distribuerat inspirerat tillvägagångssätt. Resultatet visar på ett potentiellt fungerande system, men som behöver utredas vidare i framtida studier för verifiering av högre grad.

The Time Dependent Kernel Density Estimation (TDKDE) developed by Harvey & Oryshchenko (2012) is a kernel density estimation adjusted by the Exponentially Weighted Moving Average (EWMA) weighting scheme. The Maximum Likelihood Estimation (MLE) procedure for estimating the parameters proposed by Harvey & Oryshchenko (2012) is easy to apply but has two inherent problems. In this study, we evaluate the performances of the probability density estimation in terms of the uniformity of Probability Integral Transforms (PITs) on various kernel functions combined with different preset numbers. Furthermore, we develop a new estimation algorithm which can be conducted using Artificial Neural Networks to eliminate the inherent problems with the MLE method and to improve the estimation performance as well. Based on the new estimation algorithm, we develop the TDKDE-based Random Forests time series classification algorithm which is significantly superior to the commonly used statistical feature-based Random Forests method as well as the Ker- nel Density Estimation (KDE)-based Random Forests approach. Furthermore, the proposed TDKDE-based Self-organizing Map (SOM) clustering algorithm is demonstrated to be superior to the widely used Discrete-Wavelet- Transform (DWT)-based SOM method in terms of the Adjusted Rand Index (ARI).

This thesis introduces an orienting mechanism and control system for the purpose of eye tonometry. The design is based on a 3RRR spherical parallel manipulator architecture. The end-effector is mounted with a triad of force sensing elements. Presented in this paper is a unique approach to force control based on an artificial neural network. The mechanism generates movements to collect data about its tactile environment ultimately generating a path to the force sensors' equilibrium point.

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Magister Philosophiae - MPhil / This study explored the learning experiences of black working class women in the context of the National Skills Development Strategy. The research focused firstly, on how the National Skills Development Strategy facilitated women's learning and secondly, what has helped and hindered their learning and how their learning experiences related to the literature on women's learning. / South Africa

The dissertation looks at the city as an emergent product of the lower-level activities of the city components. City components refer to the smaller elements which make up the fabric of a city like buildings, roads, inhabitants, cars etc. Lower-level activities refer to the interaction between these components, and define the consequential feedback into the city as a whole. An understanding of these aspects of emergence allows for the identification of tools and guidelines which, in turn, forms the basis for design and building performance criteria. / Dissertation (MArch(Prof))--University of Pretoria, 2008. / Architecture / unrestricted

Condition monitoring of systems and detection of changes in the systems are of significant importance for an automated system, whether it is for production, transport, amusement, or any other application. Although condition monitoring is already widely used in machinery, the need for it is growing, especially as systems become increasingly autonomous and self-contained. One of the toughest tasks concerning embedded condition monitoring is to extract the useful information and conclusions from the often large amount of measured data. The use of self-organising maps, SOMs, for embedded condition monitoring is of interest for the component manufacturer who lacks information about how the component is to be used by the system integrator, or in what applications and load cases. At the same time, there is also a potential interest on the part of the system builders. Although they know how the system is designed and will be used, it is still hard to identify all possible failure modes. A component does not break at all locations or in all functions simultaneously, but rather in one, more stressed, location. Where is this location? Here, the collection of as much data as possible from the system and then processing it with the aid of SOMs allows the system integrators to create a map of the load on the system in its operating conditions. This gives the system integrators a better chance to decide where to improve the system. Automating monitoring and analysis means not only being able to collect prodigious amounts of measured data, but also being able to interpret the data and transform it into useful information, e.g. conclusions about the state of the system. However, as will be argued in this thesis, drawing the conclusions is one thing, being able to interpret the conclusions is another, not least concerning the credibility of the conclusions drawn. This has proven to be particularly true for simple mechanical systems like pneumatics in the manufacturing industry.

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles.

The task of this thesis was focused on comparison selected evolutionary algorithms for their success and computing needs. The paper discussed the basic principles and concepts of evolutionary algorithms used for optimization problems. Author programmed selected evolutionary algorithms and subsequently tasted on various test functions with exactly the given input conditions. Finally the algorithms were compared and evaluated the results obtained for different settings.

Práce se zabývá popisem nového stochastického vícekriteriálního optimalizačního algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukázáno, že algoritmus je schopen řešit nejrůznější typy optimalizačních úloh (s jakýmkoli počtem kritérií, s i bez omezujících podmínek, se spojitým i diskrétním stavovým prostorem). Výsledky algoritmu jsou srovnány s dalšími běžně používanými metodami pro vícekriteriální optimalizaci na velké sadě testovacích úloh. Uvedli jsme novou techniku pro výpočet metriky rozprostření (spread) založené na hledání minimální kostry grafu (Minimum Spanning Tree) pro problémy mající více než dvě kritéria. Doporučené hodnoty pro parametry řídící běh algoritmu byly určeny na základě výsledků jejich citlivostní analýzy. Algoritmus MOSOMA je dále úspěšně použit pro řešení různých návrhových úloh z oblasti elektromagnetismu (návrh Yagi-Uda antény a dielektrických filtrů, adaptivní řízení vyzařovaného svazku v časové oblasti…).