Grande parte do sucesso do Google provêm do algoritmo Page Rank, que avalia quantitativamente a importância de cada página na web. Esta ordenação é obtida através do vetor estacionário de uma matriz estocástica específica, utilizando o Método das Potências. A velocidade de convergência deste método será avaliada em detalhe, já que se trata de uma resposta imediata da pesquisa do usuário. Afim de entender as diferentes situações que o modelo pode enfrentar, diversas simulações são apresentadas neste trabalho. Em particular, estamos interessados nos fatores que influenciam a velocidade de convergência. Para tanto, o número de páginas total e de cada conjunto fechado, bem como o número de conjuntos fechados e de nós pendentes foram estudados. / Great part of Google\'s success comes from the Page Rank algorithm, wich quantitatively evaluates the importance of each page on the web. This sort is achieved through a specific stochastic matrix stationary vector, using the Power Method. The convergency speed of this method will be evaluated in details, since this is a imediate response for the user search. In order to understand the diferent situations the model can confront, several simulations are shown in this work. In particular, we are interested in the factors which influences the convergency speed. For that, the total and inside each closed set number of pages and also the closed sets and dangling nodes numbers were studied.

Grande parte do sucesso do Google provêm do algoritmo Page Rank, que avalia quantitativamente a importância de cada página na web. Esta ordenação é obtida através do vetor estacionário de uma matriz estocástica específica, utilizando o Método das Potências. A velocidade de convergência deste método será avaliada em detalhe, já que se trata de uma resposta imediata da pesquisa do usuário. Afim de entender as diferentes situações que o modelo pode enfrentar, diversas simulações são apresentadas neste trabalho. Em particular, estamos interessados nos fatores que influenciam a velocidade de convergência. Para tanto, o número de páginas total e de cada conjunto fechado, bem como o número de conjuntos fechados e de nós pendentes foram estudados. / Great part of Google\'s success comes from the Page Rank algorithm, wich quantitatively evaluates the importance of each page on the web. This sort is achieved through a specific stochastic matrix stationary vector, using the Power Method. The convergency speed of this method will be evaluated in details, since this is a imediate response for the user search. In order to understand the diferent situations the model can confront, several simulations are shown in this work. In particular, we are interested in the factors which influences the convergency speed. For that, the total and inside each closed set number of pages and also the closed sets and dangling nodes numbers were studied.

The topic of convergence in real GDP per capita has become a very sensitive issue, its results often depending on how the sample group, time period, estimation approach and theoretical concept are chosen. This dissertation presents a study and a convenient explanation of the Mankiw, Romer and Weil's (1992) augmentation of the Solow's (1956) neoclassical growth model and its subsequent empirical application to the EU27 over the period 1970-2010. The application is based on the convergence models designed by the Augmented Solow's model and studies convergence speed and patterns among the EU27 countries. The evidence indicates that the pace of convergence within the EU27 is much slower than what the model predicts. Nevertheless, the analysis shows that an increase in human capital has a stronger impact on per capita GDP and, by extension, on convergence than a similar increase in physical capital.

Made available in DSpace on 2016-08-17T16:54:33Z (GMT). No. of bitstreams: 1 Tese Cristiane Cristina.pdf: 4404224 bytes, checksum: a68e5757bedc2d3d341a5937f100fe1f (MD5) Previous issue date: 2013-07-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In adaptive filtering many adaptive filter are based on the mean square error method (MSE). These filters were developed to improve convergence spedd with a lower misadjustment. The least mean square (LMS) and the recursive least square (RLS) algorithms have been the hallmark of adaptive filtering. In this work we develop adaptive algorithms based on the even powers of the error inspired in the recursive lest square (RLS) algorithm. Namely recursive nom quadratic (RNQ) algorithm. The ideas is based on Widrow s least mean square fourth (LMF) algorithm. Fisrt we derive equations based on a singal even power of the error in order to obtain criterions that guarantee convergence. We also determine equations that measure the misadjustment and the time constant of the adaptive process of the RNQ algorithm. We work also, toward making the algorithm less sensitive to the size of the error in na alternative direction, by proposing a cost function which is a sum of the even powers of the error. This second approach bring the error explicitly to the RLS algorithm formulation by proposing a new cost function that preserves the measnsquare-error (MSE) solution, but allows for the exploitation of higher order moments of the error to speedup the converge of the algorithm. The main goal this work is to create form first principles (new cost functions ) a mechanism to include instantaneous error information in the RLS algorithm, make it track better, and allow for the design of the forgetting factor. As we will see the key aspecto of our approach is to include the error in the Kalman gain that effectively controls the speed of adaptation of the RLS algorithm. / Em filtragem adaptativa, vários filtros são baseados no método do erro quadrático médio (do inglês, MSE- mean squared error ) e muitos desses foram desenvolvidos para obter uma convergência rápida com um menos desajuste. Os algoritmos mínimos quadrático médio (do inglês, LMS- least mean square ) e mínimos quadrados recursivos (do inglês, RLS- recursive least square ) foram um marco em filtragem adaptativa. Nesse trabalho apresentamos o desenvolvimento de uma família de algoritmos adaptativos baseados nas potências pares do erro, inspirado na dedução do algoritmo RLS padrão. Chamaremos esses novos algoritmos de recursivo não-quadrático (RNQ). A ideia básica é baseada na função de custo apresentada por Widrow no algoritmo mínimo quarto médio ( do inglês, LMF least mean square fourth). Inicialmente derivamos equações baseados em uma potência par do erro para obter critérios que garantam a convergência. Determinamos também, equações que definem o desajuste e o tempo de aprendizagem do processo de adaptação do algoritmo RNQ baseado em potência para arbitrária. Trabalhamos também, no sentido de tornar o algoritmo menos sensível ao tamanho do erro numa direção alternativa, propondo uma função de custo baseado na soma das potências pares do erro. Essa segunda abordagem torna explícito o papel do erro na formulação do RLS ao propor uma nova função de custo que preserve a solução MSE, mas permite a utilização dos momentos de alta ordem do erro para aumentar a velocidade de convergência do algoritmo. O principal objetivo do nosso trabalho é criar a partir dos primeiros princípios (novas funções de custo) um mecanismo para incluir informações de erro instantâneo no algoritmo RLS e torná-lo um seguidor melhor. Assim, o aspecto-chave dessa nova abordagem é incluir o erro no ganho de Kalman que controla efetivamente a velocidade de adaptação do algoritmo de RLS.

Current control is an important topic in modern power electronics system. For voltage source inverters, current control loop ensures the waveform quality at steady state and the fast response at transient state. To improve the current control performance, quite a few nonlinear control strategies have been presented and one well-known strategy is the hysteresis current control. It achieves fast response without stability issue and it has high control precision. However, for voltage source inverter applications, hysteresis current control has a wide switching frequency range, which introduces additional switching loss and impacts the design of harmonic filter. Other nonlinear current control strategies include one-cycle control, non-linear carrier control, peak current control, charge control, and so on. However, these control strategies are just suitable for specific topologies and it cannot be directly used by voltage source inverters. The recently proposed parabolic current control solves the frequency variation problem of hysteresis current control by employing a pair of parabolic carriers as the control band. By the use of parabolic current control, approximate-constant switching frequency can be achieved. Due to the cycle-by-cycle control structure, it inherently has fast response speed and high precision. These advantages make it suitable for voltage source inverters, including stand-alone inverters, grid connected inverters, active power filters, and power factor correction applications. However, parabolic current control has some limitations, such as dead-time effects, only working as bipolar PWM, complex hardware implementation, non-ideal converging speed. These problems are respectively solved in this dissertation and solutions include dead-time compensation, the implementation on dual-carrier unipolar PWM, sensorless parabolic current control, single-step current control. With the proposed dead-time compensation strategy, current control precision is improved and stable duty-cycle range are extended. Dual-carrier PWM implementation of parabolic current control has smaller harmonic filter size and lower power loss. Sensorless parabolic current control decreases the cost of system and enhances the noise immunity capability. Single-step current control pushes the convergence speed to one switching operation with simple implementation. High switching frequency is allowed and power density can be improved. Detailed analysis, motivation and experimental verification of all these innovations are covered in this dissertation. In addition, the duality phenomenon exists in electrical circuits, such as Thevenin's theorem and Norton's theorem, capacitance and inductance. These associated pairs are called duals. The dual of parabolic current control is derived and named parabolic voltage control. Parabolic voltage control solves the audible noise problem of burst mode power converters and maintains high efficiency in the designed boost converter. / Ph. D. / Current control strategy is an important topic in power converter design. Good current control strategy helps to control the quality of input or output waveform of power conversion systems. This dissertation studied an attractive current control strategy named parabolic current control. Parabolic current control solves some drawbacks of conventional current control strategies with enhanced performance. However, it still has some application limitations. This dissertation proposed four new strategies to solve the application limitations of parabolic current control. Motivated by the duality phenomenon, a voltage control strategy named parabolic voltage control is also proposed, serving as the dual of parabolic current control. By the use of parabolic voltage control, audible noise in some power conversion systems can be eliminated and conversion efficiency can be ensured. All these new ideas in this dissertation are carefully derived in theory and verified by experimental test results.

This dissertation poses the question of whether there is a relationship between the speed of convergence of globalisation and the speed of convergence of GDP per capita. Firstly, the concepts of globalisation and real convergence and their relationship are thoroughly explained from both the theoretical and empirical point of view. And secondly, the answer to the question comes in the form of beta and sigma convergence analysis of this relationship. Thirdly, the analysis splits the countries into two groups (developed and developing countries) and finds interesting but ambiguous results in their comparison. Finally, in order to correctly interpret the results of absolute and conditional beta and sigma convergence, their theoretical and empirical overview is discussed in depth. The dissertation concludes by providing some answers to the initial question for every particular analysis. Namely, it shows that this relationship is indeed very ambiguous.

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Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-06-23T21:14:46Z No. of bitstreams: 1 JonathanQueiroz.pdf: 631286 bytes, checksum: 2cab2a7d6e496bf574ddef1f49a77440 (MD5) / Made available in DSpace on 2017-06-23T21:14:46Z (GMT). No. of bitstreams: 1 JonathanQueiroz.pdf: 631286 bytes, checksum: 2cab2a7d6e496bf574ddef1f49a77440 (MD5) Previous issue date: 2016-03-08 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ) / The discrete Riccati algebraic equation has played an increasingly important role in optimal control theory and adaptive ltering. For this reason, various techniques have been developed to solve the DARE, for example the approach based on self vectors or approaches related to invariant subspaces [1], which require mathematical rigor and precision. However, these approaches present a number of problems, among them the fact that they can not be implemented in real-time due to its high computational cost to estimate the solution of DARE in many systems, especially systems with higher order three. In order to overcomes this problem, we propose to solve the DARE using as an estimator based on the sum of potential error pairs. The estimator is similar to the Recursive Least Squares (RLS), but with a better performance in terms of convergence speed and estimation accuracy without a signi- cant increase in computational complexity. The estimator is called Recursive Least Non-Squares (RLNS). One other aspect in unraveling the general DARE is to ensure that DARE is numerically well conditioned. To perform the numerical conditioning of DARE, a matrix decomposition technique known as Moore-Penrose inverse or generalized inverse is used. The proposed method is evaluated in a multivariate system 6th order corresponding to the wind generator. The method is evaluated under the numerical stability point of view and speed of convergence. / A equação algébrica Riccati discreta (discrete algebraic Riccati equation (DARE)) tem desempenhado uma papel cada vez mais importante na teoria de controle ótimo. Por esse motivo, varias técnicas tem sido desenvolvidas para solucionar a DARE, por exemplo a abordagem baseada em auto vetores ou ainda abordagens relacionadas a subespaços invariantes, as quais requerem rigor e precisão matemáticas. No entanto, estas abordagens apresentam uma serie de problemas, dentre eles, o fato de não poderem ser implementadas em tempo real devido ao seu alto custo computacional para estimar a solução da DARE em diversos sistemas, sobretudo sistemas com ordem superior a três. Com o intuito de contorna este problema, propomos solucionar a DARE utilizando um estimador baseado na soma das potencias pares do erro. O estimador e similar ao Recursive least squares (RLS), mas com um desempenho melhor em termos de velocidade de convergência e precisão de estimativa, sem aumento significativo da complexidade computacional. O estimador é denominado Recursive Least Non-Squares (RLNS). Um outra aspecto para que possamos solucionar a DARE de forma geral, e garantir que a DARE seja numericamente bem condicionada. Para efetuar o condicionamento numérico da DARE, será utilizada uma técnica de decomposição matricial conhecida como inversa de Moore-Penrose ou inversa generalizada. A metodologia proposta e avaliada em um sistema multivariavel de 6th ordem correspondente ao gerador eólico.

In the modern generation, Electric Power has become one of the fundamental needs for humans to survive. This is due to the dependence of continuous availability of power. However, for electric power to be available to the society, it has to pass through a number of complex stages. Through each stage power quality problems are experienced on the grid. Under-voltages and over-voltages are the most common electric problems experienced on the grid, causing industries and business firms losses of Billions of dollars each year. Researchers from different regions are attracted by an idea that will overcome all the electrical issues experienced in the traditional grid using Artificial Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The research focused on Power Quality Optimization in Smart Grid based on improved Differential Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags and eliminate voltage surges or spikes, while maximizing the power quality. During Differential Evolution improvement research, elimination of stagnation, better and fast convergence speed were achieved based on modification of DE’s mutation schemes and parameter control selection. DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for DE algorithm by achieving excellent optimization results with regards to convergence speed and elimination of stagnation during simulations. The improved DE was used to optimize Power Quality in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR). Excellent convergence results of voltage swells and voltage sags minimization were achieved based on application of multi-objective parallel operation strategy during simulations. MATLAB was used to model the proposed solution and experimental simulations. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

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