Made available in DSpace on 2014-12-17T15:47:52Z (GMT). No. of bitstreams: 1 ThatianaCNS_DISSERT.pdf: 1637234 bytes, checksum: 8b38ce4a7a358efe654d9bb1c23c15bc (MD5) Previous issue date: 2010-03-02 / The distribution of petroleum products through pipeline networks is an important problem that arises in production planning of refineries. It consists in determining what will be done in each production stage given a time horizon, concerning the distribution of products from source nodes to demand nodes, passing through intermediate nodes. Constraints concerning storage limits, delivering time, sources availability, limits on sending or receiving, among others, have to be satisfied. This problem can be viewed as a biobjective problem that aims at minimizing the time needed to for transporting the set of packages through the network and the successive transmission of different products in the same pipe is called fragmentation. This work are developed three algorithms that are applied to this problem: the first algorithm is discrete and is based on Particle Swarm Optimization (PSO), with local search procedures and path-relinking proposed as velocity operators, the second and the third algorithms deal of two versions based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed algorithms are compared to other approaches for the same problem, in terms of the solution quality and computational time spent, so that the efficiency of the developed methods can be evaluated / A distribui??o de produtos de petr?leo atrav?s de redes de polidutos ? um importante problema que se coloca no planejamento de produ??o das refinarias. Consiste em determinar o que ser? feito em cada est?gio de produ??o dado um determinado horizonte de tempo, no que respeita ? distribui??o de produtos de n?s fonte ? procura de n?s, passando por n?s intermedi?rios. Restri??es relativas a limites de armazenamento, tempo de entrega, disponibilidade de fontes, limites de envio ou recebimento, entre outros, t?m de ser satisfeitas. Este problema pode ser visto como um problema biobjetivo, que visa minimizar o tempo necess?rio para transportar o conjunto de pacotes atrav?s da rede e o envio sucessivo de produtos diferentes no mesmo duto que ? chamado de fragmenta??o. Neste trabalho, s?o desenvolvidos tr?s algoritmos que s?o aplicados a esse problema: o primeiro algoritmo ? discreto e baseia-se na Otimiza??o por Nuvem de Part?culas (PSO), com procedimentos de busca local e path-relinking propostos como operadores de velocidade, o segundo e o terceiro algoritmos tratam de duas vers?es baseadas no Non-dominated Sorting Genetic Algorithm II (NSGA-II). Os algoritmos propostos s?o comparados a outras abordagens para o mesmo problema, em termos de qualidade de solu??o e tempo computacional despendido, a fim de se avaliar a efici?ncia dos m?todos desenvolvidos

Made available in DSpace on 2014-12-17T14:55:35Z (GMT). No. of bitstreams: 1 JacksonGS.pdf: 1963039 bytes, checksum: ed3464892d7bb73b5dcab563e42f0e01 (MD5) Previous issue date: 2009-09-28 / Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth / Segmenta??o de imagens ? um dos problemas de processamento de imagens que merece especial interesse da comunidade cient?fica. Neste trabalho, s?o estudado m?todos n?o-supervisionados para detec??o de algomerados (clustering) e reconhecimento de padr?es (pattern recognition) em segmenta??o de imagens m?dicas M?todos baseados em t?cnicas de computa??o natural t?m se mostrado bastante atrativos nestas tarefas e s?o estudados aqui como uma forma de verificar a sua aplicabilidade em segmenta??o de imagens m?dicas. Este trabalho trata de implementa os m?todos GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm) PSOKA (Algoritmo de clustering baseado em PSO (Particle Swarm Optimization) e K means) e PSOFCM (Algoritmo de clustering baseado em PSO e FCM (Fuzzy C Means)). Al?m disso, como forma de avaliar os resultados fornecidos pelos algoritmos s?o utilizados ?ndices de valida??o de clustering como forma de medida quantitativa Avalia??es visuais e qualitativas tamb?m s?o realizadas, principalmente utilizando dados do sistema BrainWeb, um gerador de imagens do c?rebro, como ground truth

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-06-01T10:57:51Z No. of bitstreams: 2 Dissertação - Matheus Rudolfo Diedrich Ullmann - 2016.pdf: 1264745 bytes, checksum: 65f8378224bd7fd700216a920f2da7a0 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-06-01T11:00:53Z (GMT) No. of bitstreams: 2 Dissertação - Matheus Rudolfo Diedrich Ullmann - 2016.pdf: 1264745 bytes, checksum: 65f8378224bd7fd700216a920f2da7a0 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-06-01T11:00:53Z (GMT). No. of bitstreams: 2 Dissertação - Matheus Rudolfo Diedrich Ullmann - 2016.pdf: 1264745 bytes, checksum: 65f8378224bd7fd700216a920f2da7a0 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2016-02-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The MassiveOpenOnlineCourses(MOOCs)areonlinecourseswithopenenrollment that involvingahugeamountofstudentsfromdifferentlocations,withdifferentback- grounds andinterests.Thelargenumberofstudentsimpliesahugeandunmanageable number ofinteractions.Thisfact,alongwiththedifferentinterestsofstudents,resulting in low-qualityinteractions.Duetothelargenumberofstudents,alsobecomesunviable composition manuallylearninggroups.DuetothesecharacteristicspresentinMOOCs, a methodforforminggroupswasdevelopedinthiswork,asanattempttoattendthedi- chotomy existsbetweenthecollective,whichinvolvestheformationofanonlinelearning community onamassivescale,andindividual,withdifferentinterests,priorknowledge and expectationsanddifferentleadershipprofiles.Fortheformationofgroups,anadapta- tion ofParticleSwarmOptimizationalgorithmwasproposedbasedonthreecriteria,kno- wledge level,interestsandleadershipprofiles,formingthengroupswithdifferentlevels of knowledge,similarinterestsanddistributedleadership,providingbetterinteractionand knowledgeconstruction.Werecreatedtwovariationsoftheproblem,withfivestudents and theothersix.Basedoncomputationaltests,thealgorithmdemonstratedthatableto attend thegroupingcriteriainasatisfactorycomputingtimeandismoreefficientthanthe model randomgroupsformation.Thetestsalsodemonstratedthatthealgorithmisrobust taking intoaccountthevariousdatasetsanditerationsvariations.Toevaluatethequality of interactionsandknowledgebuildingingroupsformedbythemethod,Acasestudy wasconducted;andfortheanalysisofthecollecteddiscourses,itwastakenasthebasis twomodelsofdiscourseanalysisfoundintheliterature.Theresultsofthecasestudy demonstrated thatthegroupsformedbytheproposedmethodachievedthebestresultsin the interactionsandknowledgeconstruction,whencomparedwithgroupsthatdonotuse it. / Os Massive OpenOnlineCourses (MOOCs) sãocursos online com inscriçõesabertas que envolvemumaenormequantidadedeestudantesdediferenteslocalidades,comdife- rentes backgrounds e interesses.Ograndenúmerodealunosimplicaemumaenormee não gerenciávelquantidadedeinterações.Estefato,juntamentecomosinteressesdife- rentes dosalunos,resultaeminteraçõesdebaixaqualidade.Devidoàgrandequantidade de alunos,tambémtorna-seinviávelacomposiçãodegruposdeaprendizagemdeforma manual. DevidoàessascaracterísticaspresentesnosMOOCs,ummétodoparaformação de gruposfoidesenvolvidonestetrabalho,comoumatentativaparaatenderadicoto- mia queexisteentreocoletivo,queenvolveaformaçãodeumacomunidade online de aprendizagem emumaescalamaciça,eoindividual,comdiferentesinteresses,conhe- cimentos prévioseexpectativasecomdiferentesperfisdeliderança.Paraaformação dos grupos,umaadaptaçãodoalgoritmo ParticleSwarmOptimization foi propostacom base emtrêscritérios,níveldeconhecimento,interesseseperfisdeliderança,formando então gruposcomníveisdeconhecimentodiferentes,interessessemelhanteseliderança distribuída,proporcionandoumamelhorinteraçãoeconstruçãodeconhecimento.Foram criadas duasvariaçõesdoproblema,umacomcincoalunoseoutracomseis.Combase em testescomputacionais,oalgoritmodemonstrouqueconsegueatenderoscritériosde agrupamento emumtempodecomputaçãosatisfatórioeémaiseficientequeomodelode formação degruposaleatório.Ostestesdemonstraramtambémqueoalgoritmoérobusto levandoemcontaosvariadosconjuntosdedadosevariaçõesdeiterações.Paraavaliara qualidade dasinteraçõeseaconstruçãodeconhecimentonosgruposformadospelomé- todo, umestudodecasofoirealizado;eparaaanálisedosdiscursoscoletados,tomou-se como basedoismodelosdeanálisedediscursopresentesnaliteratura.Oresultadodo estudo decasodemonstrouqueosgruposformadospelométodopropostoobtiveramos melhores resultadosnasinteraçõeseconstruçãodoconhecimento,quandocomparados com osgruposquenãooutilizaram.

Tato diplomová práce je zaměřena na vývoj simulačního prostředí v Matlab/Simulink zvoleného letadla ve známém letovém režimu. Pozice a orientace letadla pohybujícího se ve vzduchu je popsána pohybovými rovnicemi se šesti stup\v{n}i volnosti. Soustava translačních, rotačních a kinematických rovnic tvoří soustavu devíti nelineárních diferenciálních rovnic prvního řádu. Tyto rovnice lze linearizovat okolo nějakého rovnovážného stavu, který budeme nazývat letovým režimem. Součástí simulačního prostředí je řídící systém letadla založený na PID regulaci. Základem je návrh autopilota, který řídí úhel podélného sklonu a úhel příčného náklonu. Součástí návrhu jsou takzvané „flight director\textquotedblright \phantom{s}m\'dy jako udržení výšky, volba kursu, regulace vertikální rychlosti, změna výšky, zachycení požadované výšky a navigační m\'{o}d založený na nelineárním navigačním zákonu. Optimalizace regulátorů za použití PSO algoritmu a Pareto optimalitě je využita pro nastavení parametrů PID regulátoru. Simulační prostředí je vizualizováno v softwaru FlightGear.

In microwave frequency band, the planar technology is mainly used to fabricate electronic circuits. Propagation of surface waves belongs to the significant problem of this technology. Surface waves can cause unwanted coupling among particular parts of the structure and can degrade its parameters. The problem can be solved using an electromagnetic band gap structure (EBG). These periodic structures are able to suppress surface waves in different frequency bands. This thesis is focused on the modeling of these structures in the program COMSOL Multiphysics.

The thesis is aimed to analyze parameters of antennas, and work out the computer, which can be used to the modeling of the radiation of antennas of the VUT 100 aircraft. First, used antennas are divided according to the operation frequencies, and the polarization. Second, a MATLAB program is developed and described. The program computes radiation patterns at different frequencies for the different location of antennas on the VUT aircraft. Finally, the MATLAB optimization program is develop and described. The program changes the position of antennas so that the requirements of aircraft producer can be met.

Planar antennas are used in several technical applications. The family of planar antennas contains microstrip antennas, which are very popular due to the low weight, low profile, simple manufacturing and easy mass production. Lower gain and excitation of surface waves are disadvantages of microstrip antennas. The propagation of surface waves can be efficiently suppressed if the conventional substrate is replaced by an electromagnetic bandgap (EBG) substrate. Microstrip antennas on EBG substrates have been presented in an open literature for several years. Nevertheless, no published work is devoted to the design of EBG substrates, which can suppress surface waves at several frequencies those cannot be covered by a single bandgap. In order to reach optimum parameters of designed antennas, selected global optimization methods are applied (genetic algorithms, particle swarm optimization, ant colony optimization).

The doctoral thesis deals with spectrum sensing and subsequent use of the frequency spectrum by multicarrier communication system, which parameters are set on the basis of the optimization technique. Adaptation settings can be made with respect to several requirements as well as state and occupancy of individual communication channels. The system, which is characterized above is often referred as cognitive radio. Equipments operating on cognitive radio principles will be widely used in the near future, because of frequency spectrum limitation. One of the main contributions of the work is the novel usage of the Kolmogorov – Smirnov statistical test as an alternative detection of primary user signal presence. The new fitness function for Particle Swarm Optimization (PSO) has been introduced and the Error Vector Magnitude (EVM) parameter has been used in the adaptive greedy algorithm and PSO optimization. The dissertation thesis also incorporates information about the reliability of the frequency spectrum sensing in the modified greedy algorithm. The proposed methods are verified by the simulations and the frequency domain energy detection is implemented on the development board with FPGA.

This paper deals with various approaches to solving optimization tasks. In prolog some examples from real life that show the application of optimization methods are given. Then term optimization task is defined and introducing of term fitness function which is common to all optimization methods follows. After that approaches by particle swarm optimization, ant colony optimization, simulated annealing, genetic algorithms and reinforcement learning are theoretically discussed. For testing we are using two discrete (multiple knapsack problem and set cover problem) and two continuous tasks (searching for global minimum of Ackley's and Rastrigin's function) which are presented in next chapter. Description of implementation details follows. For example description of solution representation or how current solutions are changed. Finally, results of measurements are presented. They show optimal settings for parameters of given optimization methods considering test tasks. In the end are given test tasks, which will be used for finding optimal settings of given approaches.

The diploma thesis comprises of a general approach used to predict the time series, their categorization, basic characteristics and basic statistical methods for their prediction. Neural networks are also mentioned and their categorization with regards to the suitability for prediction of time series. A program for the prediction of the progress of multiple time series in stock market is designed and implemented, and it's based on a model of flexible neuron tree, whose structure is optimized using immune programming and parameters using a modified version of simulated annealing or particle swarm optimization. Firstly, the program is tested on its ability to predict simple time series and then on its ability to predict multiple time series.