Submitted by Luciclea Silva (luci@ufpa.br) on 2018-12-11T14:50:03Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_Diagnosticofalhasincipientes.pdf: 5235661 bytes, checksum: 67b492c9d40682971d19271da4d4a96c (MD5) / Approved for entry into archive by Luciclea Silva (luci@ufpa.br) on 2018-12-11T14:50:33Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_Diagnosticofalhasincipientes.pdf: 5235661 bytes, checksum: 67b492c9d40682971d19271da4d4a96c (MD5) / Made available in DSpace on 2018-12-11T14:50:33Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_Diagnosticofalhasincipientes.pdf: 5235661 bytes, checksum: 67b492c9d40682971d19271da4d4a96c (MD5) Previous issue date: 2018-10-26 / Atualmente, a operação do sistema de transmissão de energia elétrica é sobrecarregada pela grande quantidade de informações oriundas dos mais diversos sistemas de monitoração, que devem analisar estas informações para manter o sistema em condições de operação aceitáveis de acordo com a normas do Setor Elétrico Brasileiro. Nesse contexto, este trabalho propõe um sistema de diagnóstico de falhas on-line em linhas de transmissão baseado na análise da monitoração da corrente de fuga para múltiplas falhas incipientes, que é composto de módulos que se adaptam de modo autônomo às melhorias que são executadas na LT. O trabalho desenvolvido aborda especificamente o módulo diagnóstico, no qual são extraídas as características do espectro harmônico da corrente de fuga com falha, e posteriormente, identifica a falha mais proeminente em um cenário multi eventos. Para extrair as características dos sinais de corrente de fuga com falhas foi utilizada a redundância analítica, que a partir de dados obtidos em laboratório e em campo, serviu para determinar o comportamento normal da LT, elaborar o modelo da LT em funcionamento normal e com a anomalia. De posse da corrente de fuga de funcionamento normal e com falha realiza-se a caracterização destes sinais, que empregam algoritmos adequados nas características levantadas no estado da arte sobre o tema e nos dados obtidos em campo e em laboratório. Após escolher o algoritmo de extração que possui melhor desempenho para múltiplas falhas, são propostos classificadores para determinar qual a falhas mais proeminente na LT. O projeto do classificador levou em consideração que o sistema precisa se adaptar às mudanças ocorridas na LT, incorporando o conhecimento sobre o sistema, uma vez que este é bastante dinâmico. / Atualmente, a operação do sistema de transmissão de energia elétrica é sobrecarregada pela grande quantidade de informações oriundas dos mais diversos sistemas de monitoração, que devem analisar estas informações para manter o sistema em condições de operação aceitáveis de acordo com a normas do Setor Elétrico Brasileiro. Nesse contexto, este trabalho propõe um sistema de diagnóstico de falhas on-line em linhas de transmissão baseado na análise da monitoração da corrente de fuga para múltiplas falhas incipientes, que é composto de módulos que se adaptam de modo autônomo às melhorias que são executadas na LT. O trabalho desenvolvido aborda especificamente o módulo diagnóstico, no qual são extraídas as características do espectro harmônico da corrente de fuga com falha, e posteriormente, identifica a falha mais proeminente em um cenário multi eventos. Para extrair as características dos sinais de corrente de fuga com falhas foi utilizada a redundância analítica, que a partir de dados obtidos em laboratório e em campo, serviu para determinar o comportamento normal da LT, elaborar o modelo da LT em funcionamento normal e com a anomalia. De posse da corrente de fuga de funcionamento normal e com falha realiza-se a caracterização destes sinais, que empregam algoritmos adequados nas características levantadas no estado da arte sobre o tema e nos dados obtidos em campo e em laboratório. Após escolher o algoritmo de extração que possui melhor desempenho para múltiplas falhas, são propostos classificadores para determinar qual a falhas mais proeminente na LT. O projeto do classificador levou em consideração que o sistema precisa se adaptar às mudanças ocorridas na LT, incorporando o conhecimento sobre o sistema, uma vez que este é bastante dinâmico.

Metodologias para obtenção do tamanho amostral para estimar a concentração de organismos em água de lastro e verificar normas internacionais são desenvolvidas sob uma abordagem bayesiana. Consideramos os critérios da cobertura média, do tamanho médio e da minimização do custo total sob os modelos Poisson com distribuição a priori gama e binomial negativo com distribuição a priori Pearson Tipo VI. Além disso, consideramos um processo Dirichlet como distribuição a priori no modelo Poisson com o propósito de obter maior flexibilidade e robustez. Para fins de aplicação, implementamos rotinas computacionais usando a linguagem R. / Sample size methodologies for estimating the organism concentration in ballast water and for verifying international standards are developed under a Bayesian approach. We consider the criteria of average coverage, of average length and of total cost minimization under the Poisson model with a gamma prior distribution and the negative binomial model with a Pearson type VI prior distribution. Furthermore, we consider a Dirichlet process as a prior distribution in the Poisson model with the purpose to gain more flexibility and robustness. For practical applications, we implemented computational routines using the R language.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / Neste trabalho, à discutido o problema de reconhecimento de objetos utilizando imagens extraÃdas de um sensor industrial 3D. NÃs nos concentramos em 9 extratores de caracterÃsticas, dos quais 7 sÃo baseados nos momentos invariantes (Hu, Zernike, Legendre, Fourier-Mellin, Tchebichef, Bessel-Fourier e Gaussian-Hermite), um outro à baseado na Transformada de Hough e o Ãltimo na anÃlise de componentes independentes, e, 4 classificadores, Naive Bayes, k-Vizinhos mais PrÃximos, MÃquina de Vetor de Suporte e Rede Neural Artificial-Perceptron Multi-Camadas. Para a escolha do melhor extrator de caracterÃsticas, foram comparados os seus desempenhos de classificaÃÃo em termos de taxa de acerto e de tempo de extraÃÃo, atravÃs do classificador k-Vizinhos mais PrÃximos utilizando distÃncia euclidiana. O extrator de caracterÃsticas baseado nos momentos de Zernike obteve as melhores taxas de acerto, 98.00%, e tempo relativamente baixo de extraÃÃo de caracterÃsticas, 0.3910 segundos. Os dados gerados a partir deste, foram apresentados a diferentes heurÃsticas de classificaÃÃo. Dentre os classificadores testados, o classificador k-Vizinhos mais PrÃximos, obteve a melhor taxa mÃdia de acerto, 98.00% e, tempo mÃdio de classificaÃÃo relativamente baixo, 0.0040 segundos, tornando-se o classificador mais adequado para a aplicaÃÃo deste estudo. / In this work, the problem of recognition of objects using images extracted from a 3D industrial sensor is discussed. We focus in 9 feature extractors (where seven are based on invariant moments -Hu, Zernike, Legendre, Fourier-Mellin, Tchebichef, BesselâFourier and Gaussian-Hermite-, another is based on the Hough transform and the last one on independent component analysis), and 4 classifiers (Naive Bayes, k-Nearest Neighbor, Support Vector machines and Artificial Neural Network-Multi-Layer Perceptron). To choose the best feature extractor, their performance was compared in terms of classification accuracy rate and extraction time by the k-nearest neighbors classifier using euclidean distance. The feature extractor based on Zernike moments, got the best hit rates, 98.00 %, and relatively low time feature extraction, 0.3910 seconds. The data generated from this, were presented to different heuristic classification. Among the tested classifiers, the k-nearest neighbors classifier achieved the highest average hit rate, 98.00%, and average time of relatively low rank, 0.0040 seconds, thus making it the most suitable classifier for the implementation of this study.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2015-05-04T17:25:43Z No. of bitstreams: 1 Rodrigo Morais.pdf: 5083865 bytes, checksum: 69563cc7178422ac20ff08fe38ee97de (MD5) / Made available in DSpace on 2015-05-04T17:25:43Z (GMT). No. of bitstreams: 1 Rodrigo Morais.pdf: 5083865 bytes, checksum: 69563cc7178422ac20ff08fe38ee97de (MD5) Previous issue date: 2013 / Nenhuma / A área de Mineração de Opiniões e Análise de Sentimentos surgiu da necessidade de processamento automatizado de informações textuais referentes a opiniões postadas na web. Como principal motivação está o constante crescimento do volume desse tipo de informação, proporcionado pelas tecnologia trazidas pela Web 2.0, que torna inviável o acompanhamento e análise dessas opiniões úteis tanto para usuários com pretensão de compra de novos produtos quanto para empresas para a identificação de demanda de mercado. Atualmente, a maioria dos estudos em Mineração de Opiniões e Análise de Sentimentos que fazem o uso de mineração de dados se voltam para o desenvolvimentos de técnicas que procuram uma melhor representação do conhecimento e acabam utilizando técnicas de classificação comumente aplicadas, não explorando outras que apresentam bons resultados em outros problemas. Sendo assim, este trabalho tem como objetivo uma investigação empírica e comparativa da aplicação do modelo clássico de Redes Neurais Artificiais (RNAs), o multilayer perceptron , no problema de Mineração de Opiniões e Análise de Sentimentos. Para isso, bases de dados de opiniões são definidas e técnicas de representação de conhecimento textual são aplicadas sobre essas objetivando uma igual representação dos textos para os classificadores através de unigramas. A partir dessa reresentação, os classificadores Support Vector Machines (SVM), Naïve Bayes (NB) e RNAs são aplicados considerandos três diferentes contextos de base de dados: (i) bases de dados balanceadas, (ii) bases com diferentes níveis de desbalanceamento e (iii) bases em que a técnica para o tratamento do desbalanceamento undersampling randômico é aplicada. A investigação do contexto desbalanceado e de outros originados dele se mostra relevante uma vez que bases de opiniões disponíveis na web normalmente apresentam mais opiniões positivas do que negativas. Para a avaliação dos classificadores são utilizadas métricas tanto para a mensuração de desempenho de classificação quanto para a de tempo de execução. Os resultados obtidos sobre o contexto balanceado indicam que as RNAs conseguem superar significativamente os resultados dos demais classificadores e, apesar de apresentarem um grande custo computacional para treinamento, proporcionam tempos de classificação significantemente inferiores aos do classificador que apresentou os resultados de classificação mais próximos aos dos resultados das RNAs. Já para o contexto desbalanceado, as RNAs se mostram sensíveis ao aumento de ruído na representação dos dados e ao aumento do desbalanceamento, se destacando nestes experimentos, o classificador NB. Com a aplicação de undersampling as RNAs conseguem ser equivalentes aos demais classificadores apresentando resultados competitivos. Porém, podem não ser o classificador mais adequado de se adotar nesse contexto quando considerados os tempos de treinamento e classificação, e também a diferença pouco expressiva de acerto de classificação. / The area of Opinion Mining and Sentiment Analysis emerges from the need for automated processing of textual information about reviews posted in the web. The main motivation of this area is the constant volume growth of such information, provided by the technologies brought by Web 2.0, that makes impossible the monitoring and analysis of these reviews that are useful for users, who desire to purchase new products, and for companies to identify market demand as well. Currently, the most studies of Opinion Mining and Sentiment Analysis that make use of data mining aims to the development of techniques that seek a better knowledge representation and using classification techniques commonly applied and they not explore others classifiers that work well in other problems. Thus, this work aims a comparative empirical research of the ap-plication of the classical model of Artificial Neural Networks (ANN), the multilayer perceptron, in the Opinion Mining and Sentiment Analysis problem. For this, reviews datasets are defined and techniques for textual knowledge representation applied to these aiming an equal texts rep-resentation for the classifiers. From this representation, the classifiers Support Vector Machines (SVM), Naïve Bayes (NB) and ANN are applied considering three data context: (i) balanced datasets, (ii) datasets with different unbalanced ratio and (iii) datasets with the application of random undersampling technique for the unbalanced handling. The unbalanced context inves-tigation and of others originated from it becomes relevant once datasets available in the web ordinarily contain more positive opinions than negative. For the classifiers evaluation, metrics both for the classification perform and for run time are used. The results obtained in the bal-anced context indicate that ANN outperformed significantly the others classifiers and, although it has a large computation cost for the training fase, the ANN classifier provides classification time (real-time) significantly less than the classifier that obtained the results closer than ANN. For the unbalanced context, the ANN are sensitive to the growth of noise representation and the unbalanced growth while the NB classifier stood out. With the undersampling application, the ANN classifier is equivalent to the others classifiers attaining competitive results. However, it can not be the most appropriate classifier to this context when the training and classification time and its little advantage of classification accuracy are considered.

Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶ Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.

Esta tesis tiene dos objetivos principales. El primero es proponer métodos multivariantes para el estudio de las desigualdades socioeconómicas en la mortalidad en áreas pequeñas. El segundo es estudiar estas desigualdades en la práctica en varias ciudades españolas. En consecuencia, se han realizado cuatro estudios diferentes: dos de ellos más metodológicos y los otros dos más aplicados al estudio de las desigualdades. El primer estudio metodológico propone usar Análisis Factorial Bayesiano para el cálculo de índices de privación. Además, en este estudio se concluye que ignorar la variabilidad en la estimación del índice puede conducir a un sesgo cuando las áreas se agrupan según cuantiles del índice. En el segundo estudio se ha reformulado el modelo SANOVA de modo que es posible introducir una covariable dentro de este modelo. Asimismo, dicha reformulación permite la descomposición de la varianza de los patrones estudiados como suma de varianzas de todas las componentes del modelo. Finalmente, los estudios restantes evidencian la existencia de desigualdades socioeconómicas en la mortalidad total y en la mortalidad por las principales causas específicas en once ciudades españolas. Además, para las enfermedades isquémicas del corazón estas desigualdades parecen aumentar ligeramente en el tiempo. / This thesis has two main objectives. The first is to propose multivariate methods for the study of socioeconomic inequalities in mortality in small areas. The second is to study socioeconomic inequalities in mortality in small areas of several Spanish cities. Four different studies were conducted to attain these objectives: two of them focussed on the methodological aspects and the other two being empirical studies focussed on the study of inequalities. The first methodological study proposes the Bayesian factor analysis to calculate a deprivation index. Additionally, this study concludes that ignoring the uncertainty obtained in the estimation of the index may result in a misclassification bias when the areas are grouped according to quantiles of the index. In the second methodological study the SANOVA model has been reformulated enabling the introduction of a covariate in the model. Also, this reformulation permits the decomposition of the variance of the studied patterns into the sum of variances of all the model components. Finally, the other studies show the existence of socioeconomic inequalities in total mortality and mortality by specific causes in eleven major Spanish cities. In addition, for ischemic heart disease these inequalities appear to increase slightly over time.

<p>Denna rapport tar en titt på phishing-problemet, något som många har råkat ut för med bland annat de falska Nordea eller eBay mejl som på senaste tiden har dykt upp i våra inkorgar, och ett eventuellt sätt att minska phishingens effekt. Fokus i rapporten ligger på klassificering av mejl och den huvudsakliga frågeställningen är: ”Är det, med hög träffsäkerhet, möjligt att med hjälp av ett klassificeringsverktyg sortera ut mejl som har med phishing att göra från övrig skräppost.” Det visade sig svårare än väntat att hitta phishing mejl att använda i klassificeringen. I de klassificeringar som genomfördes visade det sig att både metoden Naive Bayes och med Support Vector Machine kan hitta upp till 100 % av phishing mejlen. Rapporten pressenterar arbetsgången, teori om phishing och resultaten efter genomförda klassificeringstest.</p> / <p>This report takes a look at the phishing problem, something that many have come across with for example the fake Nordea or eBay e-mails that lately have shown up in our e-mail inboxes, and a possible way to reduce the effect of phishing. The focus in the report lies on classification of e-mails and the main question is: “Is it, with high accuracy, possible with a classification tool to sort phishing e-mails from other spam e-mails.” It was more difficult than expected to find phishing e-mails to use in the classification. The classifications that were made showed that it was possible to find up to 100 % of the phishing e-mails with both Naive Bayes and with Support Vector Machine. The report presents the work done, facts about phishing and the results of the classification tests made.</p>

Στην παρούσα μεταπτυχιακή διπλωματική εργασία χρησιμοποιούμε έννοιες και εργαλεία της Θεωρίας Παιγνίων με σκοπό να μελετήσουμε την απόδοση μηχανισμών κατανομής διαιρέσιμων πόρων εστιάζοντας κυρίως στον μηχανισμό αναλογικής κατανομής. Σύμφωνα με αυτόν τον μηχανισμό, ένα σύνολο χρηστών ανταγωνίζονται για ένα διαιρέσιμο πόρο -- όπως το εύρος ζώνης ενός τηλεπικοινωνιακού καναλιού -- υποβάλλοντας προσφορές. Ο μηχανισμός κατανέμει σε κάθε χρήστη ένα μέρος του πόρου το οποίο είναι ανάλογο της προσφοράς του και συλλέγει ένα ποσό ίσο με την προσφορά αυτή ως πληρωμή. Οι χρήστες στοχεύουν στη μεγιστοποίηση της ωφέλειας τους και συμπεριφέρονται στρατηγικά αλλάζοντας τις προσφορές τους με σκοπό να το πετύχουν. Έτσι, ο μηχανισμός ορίζει ένα παιχνίδι αναλογικής κατανομής. Παρουσιάζουμε γνωστά αποτελέσματα από τη σχετική βιβλιογραφία καθώς και νέα βελτιωμένα φράγματα για το κόστος της αναρχίας ως προς το κοινωνικό όφελος για συσχετιζόμενες ισορροπίες στο μοντέλο πλήρους πληροφόρησης και για ισορροπίες κατά Bayes-Nash στο μοντέλο ελλιπούς πληροφόρησης. Πιο συγκεκριμένα, παρουσιάζουμε ένα κάτω φράγμα 1/2 για το κόστος της αναρχίας ως προς τις προαναφερθείσες έννοιες ισορροπίας, βελτιώνοντας σημαντικά το προηγούμενο καλύτερο κάτω φράγμα 26.8% που πρόσφατα απέδειξαν οι Syrgkanis και Tardos (STOC 2013). Επίσης, μελετάμε για πρώτη φορά τη περίπτωση όπου οι χρήστες διαθέτουν περιορισμένους προϋπολογισμούς και παρουσιάζουμε ένα κάτω φράγμα περίπου 36% και ένα άνω φράγμα 50% για το κόστος της αναρχίας χρησιμοποιώντας ως αντικειμενική συνάρτηση το αποτελεσματικό όφελος το οποίο λαμβάνει υπόψη προϋπολογισμούς. / In this thesis, we use notions and techniques from Game Theory in order to analyze the performance of divisible resource allocation mechanisms focusing mainly on the proportional allocation mechanism. According to this mechanism, a set of users are competing for a divisible resource -- such as bandwidth of a communication link -- by submitting bids. The mechanism allocates to each user a fraction of the resource that is proportional to the user's bid and collects an amount equal to the bid as payment. Users aim to maximize their individual utility and act strategically in order to achieve their goal. Hence, the mechanism defines a proportional allocation game. We cover previously known results from the related literature and present new bounds on the price of anarchy with respect to the social welfare over coarse-correlated and Bayes-Nash equilibria in the full and incomplete information settings, respectively. In particular, we prove a lower bound of $1/2$ for the price of anarchy over both equilibrium concepts, significantly improving the previously best known lower bound, presented by Syrgkanis and Tardos (STOC 2013). Furthermore, we study for the first time the scenario where users have budget constraints and present lower bounds on the price of anarchy using the effective welfare (which takes budgets into account) as an objective function.

No description available.

Nowadays, satellite-based localization is a well-established technical solution to support several navigation tasks in daily life. Besides the application inside of portable devices, satellite-based positioning is used for in-vehicle navigation systems as well. Moreover, due to its global coverage and the availability of inexpensive receiver hardware it is an appealing technology for numerous applications in the area of Intelligent Transportation Systems (ITSs). However, it has to be admitted that most of the aforementioned examples either rely on modest accuracy requirements or are not sensitive to temporary integrity violations. Although technical concepts of Advanced Driver Assistance Systems (ADASs) based on Global Navigation Satellite Systems (GNSSs) have been successfully demonstrated under open sky conditions, practice reveals that such systems suffer from degraded satellite signal quality when put into urban areas. Thus, the main research objective of this thesis is to provide a reliable vehicle positioning concept which can be used in urban areas without the aforementioned limitations. Therefore, an integrated probabilistic approach which preforms fault detection & exclusion, localization and multi-sensor data fusion within one unified Bayesian framework is proposed. From an algorithmic perspective, the presented concept is based on a probabilistic data association technique with explicit handling of outlier measurements as present in urban areas. By that approach, the accuracy, integrity and availability are improved at the same time, that is, a consistent positioning solution is provided. In addition, a comprehensive and in-depth analysis of typical errors in urban areas within the pseudorange domain is performed. Based on this analysis, probabilistic models are proposed and later on used to facilitate the positioning algorithm. Moreover, the presented concept clearly targets towards mass-market applications based on low-cost receivers and hence aims to replace costly sensors by smart algorithms. The benefits of these theoretical contributions are implemented and demonstrated on the example of a real-time vehicle positioning prototype as used inside of the European research project GAlileo Interactive driviNg (GAIN). This work describes all necessary parts of this system including GNSS signal processing, fault detection and multi-sensor data fusion within one processing chain. Finally, the performance and benefits of the proposed concept are examined and validated both with simulated and comprehensive real-world sensor data from numerous test drives.