Orientador: João Antonio Pereira / Banca: Luiz de Paula do Nascimento / Banca: Mário Francisco Mucheroni / Resumo: Este trabalho apresenta o estudo, a implementação e a aplicação de duas técnicas de identificação de parâmetros modais utilizando apenas as respostas da estrutura, denominadas: Identificação Estocástica de Subespaço (IES) e Decomposição no Domínio da Freqüência (DDF). A IES é baseada na Decomposição em Valores Singulares (DVS) da projeção ortogonal do espaço das linhas das saídas futuras no espaço das linhas das saídas passadas. Uma vez realizada a DVS da projeção ortogonal é possível obter o modelo de espaço de estado da estrutura e os parâmetros modais são estimados diretamente através da decomposição em autovalores e autovetores da matriz dinâmica. A DDF é baseada na DVS da matriz de densidade espectral de potência de saída nas linhas de freqüências correspondentes a região em torno de um modo. O primeiro vetor singular obtido para cada linha de freqüência contém as respectivas informações daquele modo e os correspondentes valores singulares levam a função densidade espectral de um sistema equivalente de um grau de liberdade (1GL), permitindo a obtenção dos parâmetros do respectivo modo. Os métodos são avaliados utilizando dados simulados e experimentais. Os resultados mostram que as técnicas implementadas são capazes de estimar os parâmetros modais de estruturas utilizando apenas as respostas. / Abstract: This work presents the study, implementation and application of the two techniques for the modal parameters identification using only response data: Stochastic Subspace Identification (SSI) and Frequency Domain Decomposition (FDD). The SSI is based on Singular Value Decomposition (SVD) of the orthogonal projection of the future output row space in the past output row space. After the completion of the SVD of the orthogonal projection, is possible to get the state space model of the structure and the modal parameters are estimated directly through the eigenvalues and eigenvectors decomposition of the dynamic matrix. The FDD is based on the SVD of the output power spectral density matrix in the frequencies lines around a mode. The first singular vector obtained for each frequency line contains the respective information about this mode and the corresponding spectral density function leads to an equivalent system of one degree of freedom (1 DOF), allowing the calculation of the parameters of the mode. The methods are evaluated using simulated and experimental data. The results show that the techniques implemented are capable to estimate the modal parameters of structures using only response data. / Mestre

Cette thèse doctorale aborde divers aspects méthodologiques de l’analyse de levés électromagnétiques aéroportés en domaine temporel (TDEM) pour une interprétation détaillée à finalités géologique et hydrogéologique. Ce travail s’est appuyé sur un levé réalisé dans la région de Courtenay (Nord-Est de la région Centre) caractérisée par un plateau de craie karstifié (karst des Trois Fontaines) recouvert par des argiles d’altération et des alluvions. Tout d’abord, une méthode de filtrage des données TDEM utilisant la Décomposition en Valeurs Singulières (SVD) a été développée. L’adaptation rigoureuse de cette technique aux mesures TDEM a permis de séparer avec succès les bruits, qui ont pu être cartographiés, et le « signal géologique », diminuant grandement le temps nécessaire à leur traitement. De plus, la méthode s’est avérée efficace pour obtenir, rapidement, des informations géologiques préliminaires sur la zone. Ensuite, une analyse croisée entre le modèle de résistivité obtenu en inversant les données filtrées et les forages disponibles a été effectuée. Celle-ci a mené à une amélioration de la connaissance géologique et hydrogéologique de la zone. Une figure d’ondulation, séparant deux dépôts de craie, et le réseau de failles en subsurface ont pu être imagés, apportant un cadre géologique au karst des Trois Fontaines. Enfin, une nouvelle méthode combinant l’information aux forages et les pentes issues du modèle de résistivité EM a permis d’obtenir un modèle d‟une précision inégalée du toit de la craie. L’ensemble de ces travaux fournit un cadre solide pour de futures études géo-environnementales utilisant des données TDEM aéroportées, et ce, même en zone anthropisée. / This PhD thesis addresses various methodological aspects of the analysis of airborne Time Domain ElectroMagnetic (TDEM) surveys for a detailed interpretation in geological and hydrogeological purposes. This work was based on a survey conducted in the region of Courtenay (north-east of the Région Centre, France) characterized by a plateau of karstified chalk (karst des Trois Fontaines) covered by weathering clays and alluvium. First, a TDEM data filtering method using the Singular Value Decomposition (SVD) was developed. The rigorous adaptation of this technique to TDEM data has successfully separated the noise, which was mapped, and the “geological signal”, greatly reducing the time required for processing. Furthermore, the method has proved to be effective to obtain quick preliminary geological information on the area. Then, a cross analysis between the resistivity model obtained by inverting the filtered data and the available boreholes was conducted. This has led to the improvement of the geological and hydrogeological knowledge of the area. An undulating feature, separating two chalk deposits, and a fault network were imaged in subsurface, providing a geological framework for the Trois Fontaines karst. Finally, a new 3D modelling method combining the information at boreholes and the slopes derived from the EM resistivity model yielded an accurate model of the top of the chalk. All of this work provides a solid framework for future geo-environmental studies using airborne TDEM data, even in anthropized area.

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.

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.

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.

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.

The Singular Value Decomposition (SVD) has many applications in image processing. The SVD can be used to restore a corrupted image by separating significant information from the noise in the image data set. This thesis outlines broad applications that address current problems in digital image processing. In conjunction with SVD filtering, image compression using the SVD is discussed, including the process of reconstructing or estimating a rank reduced matrix representing the compressed image. Numerical plots and error measurement calculations are used to compare results of the two SVD image restoration techniques, as well as SVD image compression. The filtering methods assume that the images have been degraded by the application of a blurring function and the addition of noise. Finally, we present numerical experiments for the SVD restoration and compression to evaluate our computation.

Darbe, remiantis informacijos paieškos teorija, bandoma sukurti tinklalapio navigavimo asociacijų analizės ir prognozavimo modelį. Šio modelio tikslas – simuliuoti potencialių tinklalapio vartotojų informacijos paieškos kelius turint apibrėžtą informacinį tikslą. Modelis kuriamas apjungiant LSA, SVD algoritmus ir koreliacijos koeficientų skaičiavimus. LSA algoritmas naudojamas kuriant semantines erdves, o koreliacijos koeficientų skaičiavimai naudojami statistikoje. Kartu jie leidžia tinklalapio navigavimo asociacijų analizės ir prognozavimo modeliui analizuoti žodžių semantinį panašumą. Darbo eigoje išskiriamos pagrindinės problemos, su kuriomis gali susidurti tinklalapio lankytojai sudarant tinklalapio navigavimo asociacijas – tai yra konkurencijos tarp nuorodų problema, klaidinančių nuorodų problema ir nesuprantamų nuorodų problema. Demonstruojama kaip sukurtas modelis atpažįsta ir analizuoja šias problemas. / In this document we develop a model for analyzing and predicting the scent of a web site, which is based on information foraging theory. The goal of this model is to simulate potential web page users and their information foraging paths having specific information needs. Model is being developed combining LSA, SVD algorithms and correlation values calculations. LSA algorithm is used for creating semantic spaces and correlation values are user in statistics. Together they provide possibility to analyze word‘s semantic similarity. Primary problems of web navigation are described in this document. These problems can occur for users while creating the scent of a web site. User can face with concurrency between links problem, wrong sense link problem and unfamiliar link problem. In this document we demonstrate how model recognizes and analyzes these problems.