Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-01-27T14:37:37Z No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Jonathan Cardoso Silva - 2014.pdf: 3627934 bytes, checksum: 4b576bd42a95d94493a78b1e54c2d64a (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-01-28T12:39:13Z (GMT) No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Jonathan Cardoso Silva - 2014.pdf: 3627934 bytes, checksum: 4b576bd42a95d94493a78b1e54c2d64a (MD5) / Made available in DSpace on 2015-01-28T12:39:13Z (GMT). No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Jonathan Cardoso Silva - 2014.pdf: 3627934 bytes, checksum: 4b576bd42a95d94493a78b1e54c2d64a (MD5) Previous issue date: 2014-02-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The operation planning of hydrothermal systems is a complex, dynamic, stochastic, nonlinear and interconnected problem. In this study, we consider that this problem must tackle two objectives simultaneously: minimize thermoelectric generation (by maximizing the use of hydroelectric plants) and maximize water reservoirs’ level of hydroelectric plants. This dissertation presents the application of some multiobjective meta-heuristics, using a set of eight actual plants from Brazilian interconnected system in three periods of medium-term planning. The algorithms used were of two types: those based on particle swarms (MOPSO , MOPSO-TVAC , SMPSO, MOPSO-CDR and MOPSO-DFR) and evolutionary algorithms (SPEA2 and MOEAD/DRA). The results from previous studies, made with single objective techniques, were inserted in the initial population of the algorithms and compared with those simulations with normal initialization. We observed that MOPSO-CDR outperformed the other algorithms in the test scenarios while, in some cases, MOPSO has also generated competitive results. / O problema do planejamento da operação de sistemas hidrotérmicos é complexo, dinâmico, estocástico, interconectado e não linear. Este problema é tratado de modo atender a dois objetivos simultaneamente: maximizar a geração elétrica nas usinas hidrelétricas (ou minimizar o custo com a complementação da geração por termelétricas) e maximizar o nível dos reservatórios de água das hidrelétricas. Este trabalho apresenta a aplicação de algumas meta-heurísticas multiobjetivo a este problema, utilizando um conjunto de oito usinas reais do Sistema Interligado Nacional em três períodos de planejamento de médio prazo. Os algoritmos utilizados foram de dois tipos: os baseadas em enxames de partículas (MOPSO, MOPSO-TVAC,SMPSO, MOPSO-CDR e MOPSO-DFR) e os algoritmos evolucionários (SPEA2 e MOEAD/DRA). Foram realizados testes com a inserção de resultados de estudos anteriores com técnicas de único objetivo na população inicial dos algoritmos e comparados com os testes com inicialização normal. Observou-se que o algoritmo MOPSO-CDR obtém os melhores resultados nos cenários de testes utilizados, competindo em alguns casos com os resultados do MOPSO.

The maintenance schedule of generators in power plants needs to match the electricity demand and needs to ensure the reliability of the power plant at a minimum cost of operation. In this study, a comparison is made between the modified generator maintenance scheduling model and the classic generator maintenance scheduling model using the reliability objective functions. Both models are applied to a 21-unit test system, and the results show that the modified generator maintenance scheduling model gives better and more reliable solutions than the regular generator maintenance scheduling model. The better results of the modified generator maintenance scheduling model are due the modified and additional constraints in the modified generator maintenance scheduling model. Due to the reliable results of the modified generator maintenance scheduling model, a robust model is formulated using the economic cost objective function. The model includes modified crew and maintenance window constraints, with some additional constraints such as the relationship constraints among the variables. To illustrate the robustness of the formulated GMS model, the maintenance of the Arnot power plant in South Africa is scheduled with open-loop and closed-loop controllers. Both controllers satisfy all the constraints but the closed-loop results are better than the open-loop results. AFRIKAANS : Die onderhoudskedule vir kragopwekkers (OSK) in kragstasies moet kan voorsien in die vraag na elektrisiteit en moet die betroubaarheid van die kragstasie teen ’n minimum operasiekoste verseker. In hierdie studie word die betroubaarheidsdoelwitfunksie gebruik om ’n gewysigde onderhoudskeduleringsmodel vir kragopwekkers te vergelyk met die konvensionele onderhoudskeduleringsmodel. Beide modelle word toegepas op 'n 21-eenheid-toetsstelsel, en die resultate toon dat die gewysigde model ’n beter en meer betroubare oplossing bied as die konvensionele model. Die beter resultate van die gewysigde model is die gevolg van die gewysigde en bykomende beperkings in die gewysigde model. As gevolg van die betroubare resultate van die gewysigde onderhoudskeduleringsmodel word die koste-ekonomie-doelwitfunksie gebruik om ’n robuuste model te formuleer. Die model sluit gewysigde bemanning- en onderhoudvensterbeperkings in, met ’n paar bykomende beperkings soos die verhoudingsbeperkings tussen die veranderlikes. Om die robuustheid van die geformuleerde OSK-model te illustreer word die instandhouding van die Arnot kragstasie in Suid-Afrika geskeduleer met oop- en geslotelus-beheerders. Beide beheerders voldoen aan al die beperkinge, maar die geslotelusresultate is beter as die ooplusresultate. / Dissertation (MSc)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / Unrestricted

This diploma thesis deals with the path planning by swarm intelligence. In the theoretical part it describes the best known methods of swarm intelligence (Ant Colony Optimization, Bee Swarm Optimization, Firefly Swarm Optimization and Particle Swarm Optimization) and their application for path planning. In the practical part particle swarm optimization is selected for the design and implementation of path planning in the C#.

In this thesis, we use evolutionary robotics techniques to automatically design and synthesise<p>behaviour for groups of simulated and real robots. Our contribution will be on<p>the design of non-trivial individual and collective behaviour; decisions about solitary or<p>social behaviour will be temporal and they will be interdependent with communicative<p>acts. In particular, we study time-based decision-making in a social context: how the<p>experiences of robots unfold in time and how these experiences influence their interaction<p>with the rest of the group. We propose three experiments based on non-trivial real-world<p>cooperative scenarios. First, we study social cooperative categorisation; signalling and<p>communication evolve in a task where the cooperation among robots is not a priori required.<p>The communication and categorisation skills of the robots are co-evolved from<p>scratch, and the emerging time-dependent individual and social behaviour are successfully<p>tested on real robots. Second, we show on real hardware evidence of the success of evolved<p>neuro-controllers when controlling two autonomous robots that have to grip each other<p>(autonomously self-assemble). Our experiment constitutes the first fully evolved approach<p>on such a task that requires sophisticated and fine sensory-motor coordination, and it<p>highlights the minimal conditions to achieve assembly in autonomous robots by reducing<p>the assumptions a priori made by the experimenter to a functional minimum. Third, we<p>present the first work in the literature to deal with the design of homogeneous control<p>mechanisms for morphologically heterogeneous robots, that is, robots that do not share<p>the same hardware characteristics. We show how artificial evolution designs individual<p>behaviours and communication protocols that allow the cooperation between robots of<p>different types, by using dynamical neural networks that specialise on-line, depending on<p>the nature of the morphology of each robot. The experiments briefly described above<p>contribute to the advancement of the state of the art in evolving neuro-controllers for<p>collective robotics both from an application-oriented, engineering point of view, as well as<p>from a more theoretical point of view. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Several of our technological breakthroughs are influenced by types of behavior and structures developed in the natural world, including the emulation of swarm in- telligence and the engineering of artificial synapses that function like the human mind. Much like these breakthroughs, this report examines emerging behaviors across swarms of non-communicating, adaptive units that evade obstacles while find- ing a path, to present a swarming algorithm premised on a class of local rule sets re- sulting in a Unmanned Aerial Vehicle (UAV) group navigating together as a unified swarm. Primarily, this method’s important quality is that its rules are local in nature. Thus, the exponential calculations which can be supposed with growing number of drones, their states, and potential tasks are remedied. To this extent, the study tests the algorithmic rules in experiments to replicate the desired behavior in a bounded virtual space filled with simulated units. Simultaneously, in the adaptation of natural flocking rules the study also introduces the rule sets for goal seeking and uncertainty evasion. In effect, the study succeeds in reaching and displaying the desired goals even as the units avoid unknown before flight obstacles and inter-unit collisions with- out the need for a global centralized command nor a leader based hierarchical system.

Indiana University-Purdue University Indianapolis (IUPUI) / As our population ages, treating bone and joint ailments is becoming increasingly important. Both osteoporosis, a bone disease characterized by a decreased density of mineral in bone, and osteoarthritis, a joint disease characterized by the degeneration of cartilage on the ends of bones, are major causes of decreased movement ability and increased pain. To combat these diseases, many treatments are offered, including drugs and exercise, and much biomedical research is being conducted. However, how can we get the most out of the research we perform and the treatment we do have? One approach is through computational analysis and mathematical modeling. In this thesis, quantitative methods of analysis are applied in different ways to two systems: osteoporosis and osteoarthritis. A mouse model simulating osteoporosis is treated with salubrinal and knee loading. The bone and cell data is used to formulate a system of differential equations to model the response of bone to each treatment. Using Particle Swarm Optimization, optimal treatment regimens are found, including a consideration of budgetary constraints. Additionally, an in vitro model of osteoarthritis in chondrocytes receives RNA silencing of Lrp5. Microarray analysis of gene expression is used to further elucidate the mode of regulation of ADAMTS5, an aggrecanase associated with cartilage degradation, by Lrp5, including the development of a mathematical model. The math model of osteoporosis reveals a quick response to salubrinal and a delayed but substantial response to knee loading. Consideration of cost effectiveness showed that as budgetary constraints increased, treatment did not start until later. The quantitative analysis of ADAMTS5 regulation suggested the involvement of IL1B and p38 MAPK. This research demonstrates the application of quantitative methods to further the usefulness of biomedical and biomolecular research into treatment and signaling pathways. Further work using these techniques can help uncover a bigger picture of osteoarthritis's mode of action and ideal treatment regimens for osteoporosis.

Indiana University-Purdue University Indianapolis (IUPUI) / A gradient free function optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized in parameter identification of the electrochemical model of a Lithium-Ion battery having a LiCoO2 chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24-hr over-discharged battery, and over-charged battery. It is important for a battery management system to have these parameters changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. In this work, PSO methodology has been used to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions. The identified battery models were validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. These identified conditions of the battery were then used to monitor condition of the battery that can aid the battery management system (BMS) in improving overall performance. An adaptive estimation technique, namely multiple model adaptive estimation (MMAE) method, was implemented for this purpose. In this estimation algorithm, all the identified models were simulated for a battery current input profile extracted from the hybrid pulse power characterization (HPPC) cycle simulation of a hybrid electric vehicle (HEV). A partial differential algebraic equation (PDAE) observer was utilized to obtain the estimated voltage, which was used to generate the residuals. Analysis of these residuals through MMAE provided the probability of matching the current battery operating condition to that of one of the identified models. Simulation results show that the proposed model based method offered an accurate and effective fault diagnosis of the battery conditions. This type of fault diagnosis, which is based on the models capturing true physics of the battery electrochemistry, can lead to a more accurate and robust battery fault diagnosis and help BMS take appropriate steps to prevent battery operation in any of the stated severe or abusive conditions.

This work deals with the issue of swarm intelligence as a subdiscipline of artificial intelligence. It describes biological background of the dilemma briefly and presents the principles of searching paths in ant colonies as well. There is also adduced combinatorial optimization and two selected tasks are defined in detail: Travelling Salesman Problem and Quadratic Assignment Problem. The main part of this work consists of description of swarm intelligence methods for solving mentioned problems and evaluation of experiments that were made on these methods. There were tested Ant System, Ant Colony System, Hybrid Ant System and Max-Min Ant System algorithm. Within the work there were also designed and tested my own method Genetic Ant System which enriches the basic Ant System i.a. with development of unit parameters based on genetical principles. The results of described methods were compared together with the ones of classical artificial intelligence within the frame of both solved problems.

Este trabajo analiza la idoneidad de dos de los principales métodos de asignación de tareas en entornos con restricciones temporales. Se pondrá de manifiesto que ambos tipos de mecanismos presentan carencias para tratar tareas con deadlines, especialmente cuando los robots han de formar coaliciones. Uno de los aspectos a los que esta tesis dedica mayor atención es la predicción del tiempo de ejecución, que depende, entre otros factores, de la interferencia física entre robots. Este fenómeno no se ha tenido en cuenta en los mecanismos actuales de asignación basados en subastas. Así, esta tesis presenta el primer mecanismo de subastas para la creación de coaliciones que tiene en cuenta la interferencia entre robots. Para ello, se ha desarrollado un modelo de predicción del tiempo de ejecución y un nuevo paradigma llamado subasta doble. Además, se han propuesto nuevos mecanismos basados en swarm

ANP / Este trabalho propõe o uso de Mapas Cognitivos Fuzzy Dinâmicos (DFCM, do inglês Dynamic Fuzzy Cognitive Maps), uma evolução dos Mapas Cognitivos Fuzzy (FCM), para o desenvolvimento de sistemas autônomos para tomada de decisões. O FCM representa o conhecimento de forma simbólica, através de conceitos e relações causais dispostas em um grafo. Na sua versão clássica, os FCMs são usados no desenvolvimento de modelos estáticos, sendo inapropriados para o desenvolvimento de modelos temporais ou dinâmicos devido à ocorrência simultânea de todas as causalidades em uma estrutura fixa dos grafos, i.e., os conceitos e suas relações causais são invariantes no tempo. O DFCM utiliza o mesmo formalismo matemático do FCM através de grafos, acrescentando funcionalidades, como por exemplo, a capacidade de auto adaptação através de algoritmos de aprendizagem de máquina e a possibilidade de inclusão de novos tipos de conceitos e relações causais ao modelo FCM clássico. A partir dessas inclusões, é possível construir modelos DFCM para tomada de decisões dinâmicas, as quais são necessárias no desenvolvimento de ferramentas inteligentes em áreas de conhecimento correlatas à engenharia, de modo especifico a construção de modelos aplicados em Robótica Autônoma. Em especial, para as áreas de Robótica de Enxame e Vida artificial, como abordados nesta pesquisa. O sistema autônomo desenvolvido neste trabalho aborda problemas com diferentes objetivos (como desviar de obstáculos, coletar alvos ou alimentos, explorar o ambiente), hierarquizando as ações necessárias para atingi-los, através do uso de uma arquitetura para o planejamento, inspirada no modelo clássico de Subsunção de Brooks, e uma máquina de estados para o gerenciamento das ações. Conceitos de aprendizagem de máquina, em especial Aprendizagem por Reforço, são empregadas no DFCM para a adaptação dinâmica das relações de casualidade, possibilitando o controlador a lidar com eventos não modelados a priori. A validação do controlador DFCM proposto é realizada por meio de experimentos simulados através de aplicações nas áreas supracitadas. / This dissertation proposes the use of Dynamic Fuzzy Cognitive Maps (DFCM), an evolution of Fuzzy Cognitive Maps (FCM), for the development of autonomous system to decision-taking. The FCM represents knowledge in a symbolic way, through concepts and causal relationships disposed in a graph. In its standard form, the FCMs are limited to the development of static models, in other words, classical FCMs are inappropriate for development of temporal or dynamic models due to the simultaneous occurrence of all causalities in a permanent structure, i.e., the concepts and the causal relationships are time-invariant. The DFCM uses the same mathematical formalism of the FCM, adding features to its predecessor, such as self-adaptation by means of machine learning algorithms and the possibility of inclusion of new types of concepts and causal relationships into the classical FCM model. From these inclusions, it is possible to develop DFCM models for dynamic decision-making problems, which are needed to the development of intelligent tools in engineering and other correlated areas, specifically, the construction of autonomous systems applied in Autonomous Robotic. In particular, to the areas of Swarm Robotics and Artificial Life, as approached in this research. The developed autonomous system deals with multi-objective problems (such as deviate from obstacle, collect target or feed, explore the environment), hierarchizing the actions needed to reach them, through the use of an architecture for planning, inspired by the Brook’s classical Subsumption model, and a state machine for the management of the actions. Learning machine algorithms, in particular Reinforcement Learning, are implemented in the DFCM to dynamically tune the causalities, enabling the controller to handle not modelled event a priori. The proposed DFCM model is validated by means of simulated experiments applied in the aforementioned areas.