The proliferation of renewables, electric vehicles, and power electronic devices calls for innovative approaches to learn, optimize, and plan the power system. The uncertain and volatile nature of the integrated components necessitates using swift and probabilistic solutions. Gaussian process regression is a machine learning paradigm that provides closed-form predictions with quantified uncertainties. The key property of Gaussian processes is the natural ability to integrate the sensitivity of the labels with respect to features, yielding improved accuracy. This dissertation tailors Gaussian process regression for three applications in power systems. First, a physics-informed approach is introduced to infer the grid dynamics using synchrophasor data with minimal network information. The suggested method is useful for a wide range of applications, including prediction, extrapolation, and anomaly detection. Further, the proposed framework accommodates heterogeneous noisy measurements with missing entries. Second, a learn-to-optimize scheme is presented using Gaussian process regression that predicts the optimal power flow minimizers given grid conditions. The main contribution is leveraging sensitivities to expedite learning and achieve data efficiency without compromising computational efficiency. Third, Bayesian optimization is applied to solve a bi-level minimization used for strategic investment in electricity markets. This method relies on modeling the cost of the outer problem as a Gaussian process and is applicable to non-convex and hard-to-evaluate objective functions. The designed algorithm shows significant improvement in speed while attaining a lower cost than existing methods. / Doctor of Philosophy / The proliferation of renewables, electric vehicles, and power electronic devices calls for innovative approaches to learn, optimize, and plan the power system. The uncertain and volatile nature of the integrated components necessitates using swift and probabilistic solutions. This dissertation focuses on three practically important problems stemming from the power system modernization. First, a novel approach is proposed that improves power system monitoring, which is the first and necessary step for the stable operation of the network. The suggested method applies to a wide range of applications and is adaptable to use heterogeneous and noisy measurements with missing entries. The second problem focuses on predicting the minimizers of an optimization task. Moreover, a computationally efficient framework is put forth to expedite this process. The third part of this dissertation identifies investment portfolios for electricity markets that yield maximum revenue and minimum cost.

Descritores de características locais de imagens utilizados na representação de objetos têm se tornado muito populares nos últimos anos. Tais descritores têm a capacidade de caracterizar o conteúdo da imagem em dados compactos e discriminativos. As informações extraídas dos descritores são representadas por meio de vetores de características e são utilizados em várias aplicações, tais como reconhecimento de faces, cenas complexas e texturas. Neste trabalho foi explorada a análise e modelagem de descritores locais para caracterização de imagens invariantes a escala, rotação, iluminação e mudanças de ponto de vista. Esta tese apresenta três novos descritores locais que contribuem com o avanço das pesquisas atuais na área de visão computacional, desenvolvendo novos modelos para a caracterização de imagens e reconhecimento de imagens. A primeira contribuição desta tese é referente ao desenvolvimento de um descritor de imagens baseado no mapeamento das diferenças de nível de cinza, chamado Center-Symmetric Local Mapped Pattern (CS-LMP). O descritor proposto mostrou-se robusto a mudanças de escala, rotação, iluminação e mudanças parciais de ponto de vista, e foi comparado aos descritores Center-Symmetric Local Binary Pattern (CS-LBP) e Scale-Invariant Feature Transform (SIFT). A segunda contribuição é uma modificação do descritor CS-LMP, e foi denominada Modified Center-Symmetric Local Mapped Pattern (MCS-LMP). O descritor inclui o cálculo do pixel central na modelagem matemática, caracterizando melhor o conteúdo da mesma. O descritor proposto apresentou resultados superiores aos descritores CS-LMP, SIFT e LIOP na avaliação de reconhecimento de cenas complexas. A terceira contribuição é o desenvolvimento de um descritor de imagens chamado Mean-Local Mapped Pattern (M-LMP) que captura de modo mais fiel pequenas transições dos pixels na imagem, resultando em um número maior de \"matches\" corretos do que os descritores CS-LBP e SIFT. Além disso, foram realizados experimentos para classificação de objetos usando as base de imagens Caltech e Pascal VOC2006, apresentando melhores resultados comparando aos outros descritores em questão. Tal descritor foi proposto com a observação de que o descritor LBP pode gerar ruídos utilizando apenas a comparação dos vizinhos com o pixel central. O descritor M-LMP insere em sua modelagem matemática o cálculo da média dos pixels da vizinhança, com o objetivo de evitar ruídos e deixar as características mais robustas. Os descritores foram desenvolvidos de tal forma que seja possível uma redução de dimensionalidade de maneira simples e sem a necessidade de aplicação de técnicas como o PCA. Os resultados desse trabalho mostraram que os descritores propostos foram robustos na descrição das imagens, quantificando a similaridade entre as imagens por meio da abordagem Bag-of-Features (BoF), e com isso, apresentando resultados computacionais relevantes para a área de pesquisa. / Local feature descriptors used in objects representation have become very popular in recent years. Such descriptors have the ability to characterize the image content in compact and discriminative data. The information extracted from descriptors is represented by feature vectors and is used in various applications such as face recognition, complex scenes and textures. In this work we explored the analysis and modeling of local descriptors to characterize invariant scale images, rotation, changes in illumination and viewpoint. This thesis presents three new local descriptors that contribute to the current research advancement in computer vision area, developing new models for the characterization of images and image recognition. The first contribution is the development of a descriptor based on the mapping of gray-level-differences, called Center-Symmetric Local Mapped Pattern (CS-LMP). The proposed descriptor showed to be invariant to scale change, rotation, illumination and partial changes of viewpoint and compared to the descriptors Center-Symmetric Local Binary Pattern (CS-LBP) and Scale-Invariant Feature Trans- form (SIFT). The second contribution is a modification of the CS-LMP descriptor, which we call Modified Center-Symmetric Local Mapped Pattern (MCS-LMP). The descriptor includes the central pixel in mathematical modeling to better characterize the image content. The proposed descriptor presented superior results to CS-LMP , SIFT and LIOP descriptors in evaluating recognition of complex scenes. The third proposal includes the development of an image descriptor called Mean-Local Mapped Pattern (M-LMP) capturing more accurately small transitions of pixels in the image, resulting in a greater number of \"matches\" correct than CS-LBP and SIFT descriptors. In addition, experiments for classifying objects have been achieved by using the images based Caltech and Pascal VOC2006, presenting better results compared to other descriptors in question. This descriptor was proposed with the observation that the LBP descriptor can gene- rate noise using only the comparison of the neighbors to the central pixel. The M-LMP descriptor inserts in their mathematical modeling the averaging of the pixels of the neighborhood, in order to avoid noise and leave the more robust features. The results of this thesis showed that the proposed descriptors were robust in the description of the images, quantifying the similarity between images using the Bag-of-Features approach (BoF), and thus, presenting relevant computational results for the research area.

Descritores de características locais de imagens utilizados na representação de objetos têm se tornado muito populares nos últimos anos. Tais descritores têm a capacidade de caracterizar o conteúdo da imagem em dados compactos e discriminativos. As informações extraídas dos descritores são representadas por meio de vetores de características e são utilizados em várias aplicações, tais como reconhecimento de faces, cenas complexas e texturas. Neste trabalho foi explorada a análise e modelagem de descritores locais para caracterização de imagens invariantes a escala, rotação, iluminação e mudanças de ponto de vista. Esta tese apresenta três novos descritores locais que contribuem com o avanço das pesquisas atuais na área de visão computacional, desenvolvendo novos modelos para a caracterização de imagens e reconhecimento de imagens. A primeira contribuição desta tese é referente ao desenvolvimento de um descritor de imagens baseado no mapeamento das diferenças de nível de cinza, chamado Center-Symmetric Local Mapped Pattern (CS-LMP). O descritor proposto mostrou-se robusto a mudanças de escala, rotação, iluminação e mudanças parciais de ponto de vista, e foi comparado aos descritores Center-Symmetric Local Binary Pattern (CS-LBP) e Scale-Invariant Feature Transform (SIFT). A segunda contribuição é uma modificação do descritor CS-LMP, e foi denominada Modified Center-Symmetric Local Mapped Pattern (MCS-LMP). O descritor inclui o cálculo do pixel central na modelagem matemática, caracterizando melhor o conteúdo da mesma. O descritor proposto apresentou resultados superiores aos descritores CS-LMP, SIFT e LIOP na avaliação de reconhecimento de cenas complexas. A terceira contribuição é o desenvolvimento de um descritor de imagens chamado Mean-Local Mapped Pattern (M-LMP) que captura de modo mais fiel pequenas transições dos pixels na imagem, resultando em um número maior de \"matches\" corretos do que os descritores CS-LBP e SIFT. Além disso, foram realizados experimentos para classificação de objetos usando as base de imagens Caltech e Pascal VOC2006, apresentando melhores resultados comparando aos outros descritores em questão. Tal descritor foi proposto com a observação de que o descritor LBP pode gerar ruídos utilizando apenas a comparação dos vizinhos com o pixel central. O descritor M-LMP insere em sua modelagem matemática o cálculo da média dos pixels da vizinhança, com o objetivo de evitar ruídos e deixar as características mais robustas. Os descritores foram desenvolvidos de tal forma que seja possível uma redução de dimensionalidade de maneira simples e sem a necessidade de aplicação de técnicas como o PCA. Os resultados desse trabalho mostraram que os descritores propostos foram robustos na descrição das imagens, quantificando a similaridade entre as imagens por meio da abordagem Bag-of-Features (BoF), e com isso, apresentando resultados computacionais relevantes para a área de pesquisa. / Local feature descriptors used in objects representation have become very popular in recent years. Such descriptors have the ability to characterize the image content in compact and discriminative data. The information extracted from descriptors is represented by feature vectors and is used in various applications such as face recognition, complex scenes and textures. In this work we explored the analysis and modeling of local descriptors to characterize invariant scale images, rotation, changes in illumination and viewpoint. This thesis presents three new local descriptors that contribute to the current research advancement in computer vision area, developing new models for the characterization of images and image recognition. The first contribution is the development of a descriptor based on the mapping of gray-level-differences, called Center-Symmetric Local Mapped Pattern (CS-LMP). The proposed descriptor showed to be invariant to scale change, rotation, illumination and partial changes of viewpoint and compared to the descriptors Center-Symmetric Local Binary Pattern (CS-LBP) and Scale-Invariant Feature Trans- form (SIFT). The second contribution is a modification of the CS-LMP descriptor, which we call Modified Center-Symmetric Local Mapped Pattern (MCS-LMP). The descriptor includes the central pixel in mathematical modeling to better characterize the image content. The proposed descriptor presented superior results to CS-LMP , SIFT and LIOP descriptors in evaluating recognition of complex scenes. The third proposal includes the development of an image descriptor called Mean-Local Mapped Pattern (M-LMP) capturing more accurately small transitions of pixels in the image, resulting in a greater number of \"matches\" correct than CS-LBP and SIFT descriptors. In addition, experiments for classifying objects have been achieved by using the images based Caltech and Pascal VOC2006, presenting better results compared to other descriptors in question. This descriptor was proposed with the observation that the LBP descriptor can gene- rate noise using only the comparison of the neighbors to the central pixel. The M-LMP descriptor inserts in their mathematical modeling the averaging of the pixels of the neighborhood, in order to avoid noise and leave the more robust features. The results of this thesis showed that the proposed descriptors were robust in the description of the images, quantifying the similarity between images using the Bag-of-Features approach (BoF), and thus, presenting relevant computational results for the research area.

<p>The aim of this study was to identify the strategies used to render the literary dialect in A State of Independence into the Swedish translation. In order to systematically study the translation solutions, a number of ‘coupled pairs’ consisting of source text ‘problems’ and target text ‘solutions’ were extracted from the original text and the translation. The ‘coupled pairs’ were then analysed in order to detect regularities in the translation solutions. The study showed that the major strategy used by the translator was the use of ‘eye-dialect’, i.e. non-standard spellings that simulate non-standard speech. Moreover, some passages in the translation had been standardised, whereas eye-dialectal spellings were found in other passages where the original did not contain any non-standard features. Finally, a comparative count of dialectally marked utterances in both texts was made. The count showed that the dialectal markers were in the majority in the translation, which might indicate that the translator has tried to compensate for the lack of equivalent target language features.</p>

Nailfold capillary microscopy is simple, non-invasive, no injuries and easy to observe human`s microcirculation and micro blood stream directly. Due to these advantages, it plays a significant role in diseases diagnoses, treatments and prognosis. The observation of microcirculation focuses on hand, foot naildfold, conjunctival, lingual surface and lips. Nailfold microcirculation is usually performed on the ring finger. However, when measuring the speed of blood flow, difficulty to stabilize the region of interest (ROI) is often encountered. This problem becomes more serious when the magnification of microscope increases. Fixture to stabilize finger will inevitably affect the speed of blood flow under observation. The Laser Doppler blood flow velocimetry method, is expensive, only can be used in bigger capillary or to measure the average flow velocity of lager observed area, lacking of diversified morphological features of capillary, it¡¦s precision is worse than microscopy image capture method, and because of the regular contraction and relaxation of arterioles it can only measure the local blood flow velocity, cannot describe whole details of capillary blood flow velocity, some important information of microcirculation will be ignored easily. This thesis employs computer vision technique to operate displacement compensation of microscopy image sequence to stabilize observed area and extract area of capillary. Then the morphological and hemodynamic pathology features will be derived and analyzed to evaluate the status of a person¡¦s health. Not only morphological features, e.g., length, density and color, but also hemodynamic features, e.g., blood flow velocity will be measured to assess the microcirculation in end capillary. The most significant characteristic of this project is to combine three-dimensional models reconstruction technology of computer graphic to reconstruct three-dimensional capillary models and perform the three-dimensional dynamic blood flow visualization. Thus, the capillary blood flow can be adjusted and observed in the desired orientation, magnification and viewpoint. A variety of pathologically significant features of nailfold microcirculation will be extracted in the project proposed. These features can be classified into morphological and hemodynamic features. The morphological features extracted include the number, width/height, density, arteriolar limb caliber, curved segment caliber, venular limb caliber, blood color, tortuosity, and width of the curved segment of capillaries. On the other hand, hemodynamic features including velocity, direction of blood flow will also be extracted. By integrating both morphological and hemodynamic features, the status of a person¡¦s health can be evaluated by the doctor. The novel system proposed is not only easy to operate, low-cost but also has the great potential to be utilized clinically.

A number of researchers have shown that ear recognition is a viable alternative to more common biometrics such as fingerprint, face and iris because the ear is relatively stable over time, the ear is non-invasive to capture, the ear is expressionless, and both the ear’s geometry and shape have significant variation among individuals. Researchers have used different approaches to enhance ear recognition. Some researchers have improved upon existing algorithms, some have developed algorithms from scratch to assist with recognizing individuals by ears, and some researchers have taken algorithms tried and tested for another purpose, i.e., face recognition, and applied them to ear recognition. These approaches have resulted in a number of state-of-the-art effective methods to identify individuals by ears. However, most ear recognition research has been done using ear images that were captured in an ideal setting: ear images have near perfect lighting for image quality, ears are in the same position for each subject, and ears are without earrings, hair occlusions, or anything else that could block viewing of the entire ear. In order for ear recognition to be practical, current approaches must be improved. Ear recognition must move beyond ideal settings and demonstrate effectiveness in an unconstrained environment reflective of real world conditions. Ear recognition approaches must be scalable to handle large groups of people. And, ear recognition should demonstrate effectiveness across a diverse population. This dissertation advances ear recognition from ideal settings to real world settings. We devised an ear recognition framework that outperformed state-of-the-art recognition approaches using the most challenging sets of publicly available ear images and the most voluminous set of unconstrained ear images that we are aware of. We developed a Convolutional Neural Network-based solution for ear normalization and description, we designed a two-stage landmark detector, and we fused learned and handcrafted descriptors. Using our framework, we identified some individuals that are wearing earrings and that have other occlusions, such as hair. The results suggest that our framework can be a gateway for identification of individuals in real world conditions.

Submitted by Kleber Silva (kleberbs@ufba.br) on 2017-05-30T21:30:31Z No. of bitstreams: 1 TESE - Victor Sarinho.pdf: 2891194 bytes, checksum: 012f03f26406a40700502e1e0ff70b96 (MD5) / Approved for entry into archive by Vanessa Reis (vanessa.jamile@ufba.br) on 2017-06-01T12:38:02Z (GMT) No. of bitstreams: 1 TESE - Victor Sarinho.pdf: 2891194 bytes, checksum: 012f03f26406a40700502e1e0ff70b96 (MD5) / Made available in DSpace on 2017-06-01T12:38:02Z (GMT). No. of bitstreams: 1 TESE - Victor Sarinho.pdf: 2891194 bytes, checksum: 012f03f26406a40700502e1e0ff70b96 (MD5) / Modelagem de Features é uma abordagem popular que descreve a comunalidade e variabilidade de famílias de softwares em termos de features. Variabilidade em Software Product Lines (SPLs) é geralmente descrita usando features, e instâncias de SPLs também são definidas pela seleção ou configuração de features requeridas. Entretanto, abordagens diversas e complexas de SPLs têm sido obtidas com o uso de features. Diferentes abordagens de modelagem de features também têm sido propostas nos últimos anos, abrindo novas perspectivas em aspectos de variabilidade a serem gerenciados. A técnica de Modelagem de Features Orientada a Objetos (OOFM) foi proposta com o objetivo de cobrir tais aspectos de variabilidade, bem como fornecer uma solução padronizada de produção de SPLs baseadas em features. OOFM se baseia em abordagens OO representativas de features, operações comuns identificadas de manipulação de features, e recursos OO de programação e de herança existentes. Sua formalização é baseada em expressões OCL definidas e Perfil UML modelado. Ferramentas de suporte incrementam a sua usabilidade e compatibilidade com relação a técnicas de modelagem de features existentes. Finalmente, OOFM Framework e OOFM Process desenvolvidos garantem a produção padronizada de SPLs e de sistemas concretos com base na OOFM. Foram realizadas importantes avaliações entre a proposta OOFM e técnicas de modelagem de features existentes. Como resultado, OOFM se apresentou como uma solução integrada de aspectos de variabilidade que permite a análise e o projeto da variabilidade de sistemas diversos em termos de features.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O município de Mucajaí (área foco deste estudo) está situado na região centro-oeste do Estado de Roraima. A análise da paisagem do município de Mucajaí (objeto desse estudo) teve como enfoque os aspectos geológicos e geomorfológicos e o seu potencial para o geoturismo. Para o desenvolvimento da pesquisa, fez-se necessário o levantamento de material bibliográfico, cartográfico e de sensores remotos, bem como atividades de campo com intuito de identificar e descrever os compartimentos geomorfológicos que posteriormente foram cartografados. Como resultado obteve-se a descrição da paisagem no seu contexto geológico e geomorfológico em que identificou-se os modelados de denudação e acumulação. A compartimentação estudada revelou as seguintes unidades: Relevo em Crista Estruturado, essa unidade representa um relevo de erosão diferencial, recoberto por vegetação do tipo Floresta Ombrófila Densa; sobressaíram-se feições como serras, cachoeiras; na Superfície Somital Convexa, distribui-se de forma pontual e isolada por toda superfície aplainada maciços que ocorrem isoladamente, denominados relevos residuais além do Relevo Convexo Estruturado. As áreas rebaixadas são representadas pelas Superfícies Aplainadas e Aluvionares. A paisagem da área estudada revela um grande potencial para a atividade geoturística, a exemplo de serras e morros alinhados e as belas cachoeiras. Portanto, o potencial paisagístico repleto de belezas cênicas naturais favorecem à grandes possibilidades de implantação da atividade geoturística, a partir das iniciativas existentes no município e de um planejamento direcionado a valorização do potencial para sua efetiva operacionalização. / The county of Mucajaí ( focus area of this study ) is located in the central- west region of the state of Roraima . The analysis of the landscape of the county of Mucajaí (subject of this study ) focuses on the geological and geomorphological aspects and its potential for geotourism. To develop the research, it was necessary lifting bibliographic, cartographic and remote sensing equipment, as well as field activities aiming to identify and describe the geomorphological compartments that were subsequently mapped. As a result, we obtained a description of the landscape in its geological and geomorphological context in which we identified the modeled denudation and accumulation. The partitioning study revealed the following units : Relief on Structured Crest, this unit represents a relief of differential erosion, covered by vegetation of Dense Ombrophilous Forest; stood out features such as mountains, waterfalls ; in the Somital Convex Surface, is distributed in a defined and isolated manner throughout massive flat surface that occurs singly , called residual relief beyond relief Structured Convex. The recessed areas are represented by Planed Surfaces and Alluvial . The landscape of the studied area reveals a great potential for geotourism activity, like mountains and hills lined and beautiful waterfalls. Therefore, the potential landscape full of natural scenic beauty favoring a large potential for the deployment of geotourism activity, from existing initiatives in the county and planning aimed at exploiting the potential for its effective operationalization.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22800号 / 情博第730号 / 新制||情||125(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 中澤 篤志, 教授 西野 恒, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

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