Este trabalho tem como objetivo o desenvolvimento de uma metodologia de monitoramento a partir da alocação de medidores voltada para a estimação de estado harmônico trifásica em sistemas de distribuição de energia elétrica desequilibrados. O algoritmo de estimação de estado harmônico desenvolvido tem como entrada os fasores de tensão e de corrente em pontos pré-definidos de medição sobre os alimentadores em análise. Para a alocação dos medidores, verificou-se a necessidade de a mesma ser realizada e direcionada para este problema, e um algoritmo de otimização em específico foi desenvolvido utilizando algoritmos genéticos. Para a estimação de estado harmônico, a técnica de Decomposição em Valores Singulares foi utilizada, por ser adequada a sistemas não completamente observáveis. Em relação às simulações, cargas não lineares (ou perturbadoras) foram conectadas ao longo dos alimentadores testes do IEEE de 13, 34 e 37 barras, considerando configuração trifásica assimétrica para as linhas e cargas desbalanceadas. Todas as simulações computacionais foram realizadas dispondo do programa DIgSILENT PowerFactory. Os resultados satisfatórios encontrados denotam que o desempenho do estimador desenvolvido é dependente dos pontos de medição pré-definidos a partir da alocação dos medidores realizada. Pela metodologia implementada e aplicada, o algoritmo de estimação de estado harmônico veio a corretamente calcular todas as variáveis de estado e, consequentemente, os sistemas testes em análise tornaram-se completamente observáveis para todas as fases e ordens harmônicas caracterizadas. / This research aims for the development of a monitoring methodology through the allo-cation of meters in order to perform a three-phase harmonic state estimation in unbalanced distribution systems. The harmonic state estimation algorithm developed has voltage and current phasors as inputs at predefined measurement points on the feeders about analysis. For an allocation of the meters, there was a need for it to be performed and directed to this problem, and a specific optimization algorithm was developed using Genetic Algorithms. For a harmonic state estimation, the Singular Value Decomposition technique was made, because it is suitable for systems that are not completely observable. Regarding the simulations, the non-linear (or disturbing) loads were connected along the test feeders of the IEEE of 13, 34 and 37 bus, considering the three-phase asymmetric configuration for lines and loads. All computational simulations were performed in the DIgSILENT PowerFactory software. The satisfactory results found note that the performance of the developed estimator depends on the pre-defined measurement points from the allocation of the realized meters. By the applied methodology, the harmonic state estimation algorithm came to correctly calculate all the state variables and, consequently, the test systems about analysis became fully observable for all phases and harmonic orders characterized.

Este trabalho descreve os métodos e os resultados encontrados a partir da implementação de métodos estáticos para análise da estabilidade de tensão em sistemas elétricos de potência. A determinação da margem de estabilidade de tensão foi feita através do cálculo do menor valor singular da matriz jacobiana associada às equações de fluxo de carga, comumente utilizado como índice estático de colapso de tensão. As não linearidades e descontinuidades relatadas nas referências estudadas e encontradas nos testes realizados, levaram-nos a propor o uso da razão entre o menor e o maior valores singulares da mesma matriz jacobiana, na expectativa de que este índice tivesse um comportamento menos instável do que o menor valor singular, o que não foi confirmado nos testes realizados. Identifica-se também as regiões do sistema elétrico mais afetadas pela instabilidade, o que é feito através da determinação da barra crítica do sistema e da classificação das barras de carga em ilhas de controle de tensão. A barra crítica é identificada através do cálculo do vetor tangente do sistema, conforme proposto nas referências citadas no trabalho. Como alternativa ao vetor tangente para a identificação da barra crítica, propôs-se usar o vetor singular à direita associado ao menor valor singular da matriz jacobiana. A comparação da capacidade de identificação da barra crítica por esses dois vetores mostrou uma clara vantagem do uso do vetor tangente. A rotina para identificação das ilhas de controle de tensão foi adaptada a partir de um método desenvolvido para a análise de coerência em barras de carga, e os resultados encontrados foram bastante satisfatórios. Os métodos implementados foram testados em diversas situações, com o objetivo de se analisar os efeitos dos modelos de carga ZIP com elevadas parcelas de impedância constante, dos limitadores de potência reativa dos geradores e da repartição do incremento da carga de potência ativa entre os geradores. / This work describes the methods and results got from the implementation of static methods for power systems voltage stability analisys. The power system voltage stability margin was predicted by the smallest load flow jacobian\'s singular value, commonly used as a prediction index to voltage stability. lt is investigated the use of ratio of the smallest single value by the biggest one as voltage colapse index, assuming that it\'s less unstable than the singular value itself, specialy near the collapse point. The results presented shown a clear advantage of using the smallest singular value instead of this singular value rate. The identification of the system\'s regions affected by the voltage drop is made by the tangent vector and by the voltage island identification method proposed on this work. Is compared the ability to identify system\'s critical bus by the tangent vector and right singular vetor of the smallest jacobian\'s singular value. In this case, tests results show the superiority of tangent vector. All the simulations presented are compared to allow the analysis of the voltage dependents load models (with high percentual of constant impedances), reactive limiters and generators load sharing efects over the smallest singular value, the rate of the smallest single value by the biggest one, voltage island classification and the critical bus identification.

The evolution of big data has led to financial time series becoming increasingly complex, noisy, non-stationary and nonlinear. Takens theorem can be used to analyze and forecast nonlinear time series, but even small amounts of noise can hopelessly corrupt a Takens approach. In contrast, Singular Spectrum Analysis is an excellent tool for both forecasting and noise reduction. Fortunately, it is possible to combine the Takens approach with Singular Spectrum analysis (SSA), and in fact, estimation of key parameters in Takens theorem is performed with Singular Spectrum Analysis. In this thesis, we combine the denoising abilities of SSA with the Takens theorem approach to make the manifold reconstruction outcomes of Takens theorem less sensitive to noise. In particular, in the course of performing the SSA on a noisy time series, we branch of into a Takens theorem approach. We apply this approach to a variety of noisy time series.

Recommendation systems are gaining more popularity because of the complexity of problems that they provide a solution to. There are many applications of recommendation systems everywhere around us. Implementation of these systems differs and there are two approaches that are most distinguished. First approach is a system without Machine Learning, while the other one includes Machine Learning. The second approach, used in this project, is based on Machine Learning collaborative filtering techniques. These techniques include numerous algorithms and data processing methods. This document describes a process that focuses on building a job recommendation system for a recruitment industry, starting from data acquisition to the final result. Data used in the project is collected from the Pitchler AB company, which provides an online recruitment platform. Result of this project is a machine learning based recommendation system used as an engine for the Pitchler AB IT recruitment platform.

設H是一個實數正交的矩陣，我們要求它的特徵值以及特徵向量。H可以表示成Schur參數的形式。根據Ammar，Gragg及Reichel的論文，我們把H的特徵問題轉換成兩個元素由Schur參數決定的二對角矩陣的奇異值(奇異向量)的問題。我們用這個方法寫成程式並且與CLAPACK的程式比較準確度及速度。最後列出一些數值的結果作為結論。 / Let H be an orthogonal Hessenberg matrix whose eigenvalues, and possibly eigenvectors, are to be determined. Then H can be represented in Schur parametric form [2]. Following Ammar, Gragg, and Reichel's paper [1], we compute the eigenproblem of H by finding the singular values (and vectors) of two bidiagonal matrices whose elements are explicitly known functions of the Schur parameters. We compare the accuracy and speed of our programs using the method described aboved with those in CLAPACK. Numerical results conclude this thesis.

<p>Flight Control Systems are often designed in linearization points over a flight envelope and it must be proven to clearance authorities that the system works for different parameter variations and failures all over this envelope. </p><p>In this thesis µ-analysis is tried as a complement for linear analysis in the frequency plane. Using this method stability can be guaranteed for all static parameter combinations modelled and linear criteria such as phase and gain margins and most unstable eigenvalue can be included in the analysis. A way of including bounds on the parameter variations using parameter dependent Lyapunov functions is also tried. </p><p>To perform µ-analysis the system must be described as a Linear Fractional Transformation (LFT). This is a way of reformulating a parameter dependent system description as an interconnection of a nominal linear time invariant system and a structured parameter block. </p><p>A linear and a rational approximation of the system are used to make LFTs. These methods are compared. Four algorithms for calculation of the upper and lower bounds of µ are evaluated. The methods are tried on VEGAS, a SAAB research aircraft model. </p><p>µ-analysis works quite well for linear clearance. The rational approximation LFT gives best results and can be cleared for the criteria mentioned above. A combination of the algorithms is used for best results. When the Lyapunov based method is used the size of the problem grows quite fast and, due to numerical problems, stability can only be guaranteed for a reduced model.</p>

As information continues to grow at a very fast pace, our ability to access this information effectively does not, and we are often realize how harder is getting to locate an object quickly and easily. The so-called personalization technology is one of the best solutions to this information overload problem: by automatically learning the user profile, personalized information services have the potential to offer users a more proactive and intelligent form of information access that is designed to assist us in finding interesting objects. Recommender systems, which have emerged as a solution to minimize the problem of information overload, provide us with recommendations of content suited to our needs. In order to provide recommendations as close as possible to a user&rsquo / s taste, personalized recommender systems require accurate user models of characteristics, preferences and needs. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users, But it suffers from several issues like data sparsity and cold start. In one-class collaborative filtering, a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples, the challenge is even greater, since these datasets are even sparser. In this thesis, we present a series of experiments conducted on a real-life customer purchase database from a major Turkish E-Commerce site. The sparsity problem is handled by the use of content-based technique combined with TFIDF weights, memory based collaborative filtering combined with different similarity measures and also hybrids approaches, and also model based collaborative filtering with the use of Singular Value Decomposition (SVD). Our study showed that the binary similarity measure and SVD outperform conventional measures in this OCCF dataset.

This thesis considers the application of singular value decomposition (SVD) and semi-nonnegative matrix factorization (SNMF) within feedback control systems, called the SVD System and SNMF System, to control numerous subsystems with a reduced number of control inputs. The subsystems are coupled using a row-column structure to allow mn subsystems to be controlled using m+n inputs. Past techniques for controlling systems in this row-column structure have focused on scheduling procedures that offer limited performance. The SVD and SNMF Systems permit simultaneous control of every subsystem, which increases the convergence rate by an order of magnitude compared with previous methods. In addition to closed loop control, open loop procedures using the SVD and SNMF are compared with previous scheduling procedures, demonstrating significant performance improvements. This thesis presents theoretical results for the controllability of systems using the row-column structure and for the stability and performance of the SVD and SNMF Systems. Practical challenges to the implementation of the SVD and SNMF Systems are also examined. Numerous simulation examples are provided, in particular, a dynamic simulation of a pin array device, called Digital Clay, and two physical demonstrations are used to assess the feasibility of the SVD and SNMF Systems for specific applications.

Flight Control Systems are often designed in linearization points over a flight envelope and it must be proven to clearance authorities that the system works for different parameter variations and failures all over this envelope. In this thesis µ-analysis is tried as a complement for linear analysis in the frequency plane. Using this method stability can be guaranteed for all static parameter combinations modelled and linear criteria such as phase and gain margins and most unstable eigenvalue can be included in the analysis. A way of including bounds on the parameter variations using parameter dependent Lyapunov functions is also tried. To perform µ-analysis the system must be described as a Linear Fractional Transformation (LFT). This is a way of reformulating a parameter dependent system description as an interconnection of a nominal linear time invariant system and a structured parameter block. A linear and a rational approximation of the system are used to make LFTs. These methods are compared. Four algorithms for calculation of the upper and lower bounds of µ are evaluated. The methods are tried on VEGAS, a SAAB research aircraft model. µ-analysis works quite well for linear clearance. The rational approximation LFT gives best results and can be cleared for the criteria mentioned above. A combination of the algorithms is used for best results. When the Lyapunov based method is used the size of the problem grows quite fast and, due to numerical problems, stability can only be guaranteed for a reduced model.

In der vorliegenden Arbeit wird ein neues Tensorformat vorgestellt und eingehend analysiert. Das hierarchische Format verwendet einen binären Baum, um den Tensorraum der Ordnung d mit einer geschachtelten Unterraumstruktur zu versehen. Der Speicheraufwand für diese Darstellung ist von der Größenordnung O(dnr + dr^3), wobei n den Speicheraufwand in den Ansatzräumen kennzeichnet und r ein Rangparameter ist, der durch die Dimensionen der geschachtelten Unterräume bestimmt wird. Das hierarchische Format umfasst verschiedene Standardformate zur Tensordarstellung wie das kanonische oder r-Term-Format und die Unterraum-/Tucker-Darstellung. Die in dieser Arbeit entwickelte zugehörige Arithmetik inklusive mehrerer Approximationsmethoden basiert auf stabilen Methoden der Linearen Algebra, insbesondere die Singulärwertzerlegung und die QR-Zerlegung sind von zentraler Bedeutung. Die rechnerische Komplexität ist hierbei O(dnr^2+dr^4). Die lineare Abhängigkeit von der Ordnung d des Tensorraumes ist hervorzuheben. Für die verschiedenen Approximationsmethoden, deren Effizienz und Effektivität für die Anwendbarkeit des neuen Formates entscheidend sind, werden qualitative und quantitative Fehlerabschätzungen gezeigt. Umfassende numerische Experimente mit einem Fokus auf den Approximationsmethoden bestätigen zum einen die theoretischen Resultate und belegen die Stärken der neuen Tensordarstellung, zeigen aber zum anderen auch weitere, eher überraschende positive Eigenschaften der mit FastHOSVD bezeichneten schnellsten Kürzungsmethode. / In this dissertation we present and a new format for the representation of tensors and analyse its properties. The hierarchical format uses a binary tree in order to define a hierarchical structure of nested subspaces in the tensor space of order d. The strorage requirements are O(dnr+dr^3) where n is determined by the storage requirements in the ansatz spaces and r is a rank parameter determined by the dimensions of the nested subspaces. The hierarchichal representation contains the standard representation like canonical or r-term representation and subspace or Tucker representation. The arithmetical operations that have been developed in this work, including several approximation methods, are based on stable Linear Alebra methods, especially the singular value decomposition (SVD) and the QR decomposition are of importance. The computational complexity is O(dnr^2+dr^4). The linear dependence from the order d of the tensor space is important. The approximation methods are one of the key ingredients for the applicability of the new format and we present qualitative and quantitative error estimates. Numerical experiments approve the theoretical results and show some additional, but unexpected positive aspects of the fastest method called FastHOSVD.