This thesis uses data of more than 180 banks from CEE region to identify the main determinants of long term credit ratings assigned to these banks in period between 2010 - 2012. This is done by employing two frequently used classification methods - Multiple Discriminant Analysis and Ordered Logit Model. The main contribution lies in including explanatory variables from various areas which have impact on financial health of examined banks. Apart from standard spheres of banks' performance such as capital adequacy, asset quality or profitability we investigate relevance of macroeconomic and qualitative factors as well. Although our results suggest that all mentioned areas are relevant for credit risk and hence rating assignment process the bank specific variables, both quantitative and qualitative, still play the key role.

Made available in DSpace on 2014-12-17T15:41:48Z (GMT). No. of bitstreams: 1 KyteriaSLF_DISSERT.pdf: 2575352 bytes, checksum: 9c601baf3a71a96816d5ae90fe0ecaea (MD5) Previous issue date: 2010-02-22 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Over exploitation of oil deposits on land onshore or offshore, there is simultaneous generation of waste water, known as produced water, which represents the largest waste stream in the production of crude oil. The relationship between the chemical composition of oil and water production and the conditions in which this process occurs or is favored are still poorly studied. The area chosen for the study has an important oil reserve and an important aquifer saturated with freshwater meteoric. The aim of this work is to study some chemical parameters in water produced for each reservoir zone of production in mature oil fields of A?u Formation, using the hydrochemical and statistical analysis to serve as a reference and be used as tools against the indicator ranges water producers in oil producing wells. Samples were collected from different wells in 6 different areas of production and were measured 50 parameters, which can be classified into three groups: anions, cations and physicochemical properties (considering only the parameters that generated values above detection limits in all samples). Through the characterization hydrochemistry observed an area of water and chlorinated sodium, chlorinated calcium or magnesium (mixed) in well water in different areas of A?u, by applying a statistical treatment, we obtained a discriminant function that distinguishes chemically production areas. Thus, it was possible to calculate the rate of correct classification of the function was 76.3%. To validate this model the accuracy rate was 86% / Ao longo da explora??o de petr?leo de jazidas em terra (onshore) ou no mar (offshore), existe gera??o concomitante de um efluente aquoso, denominado ?gua produzida, que representa a maior corrente de res?duo na produ??o do ?leo cru. A rela??o entre a composi??o qu?mica dos ?leos e das ?guas de produ??o, bem como as condi??es em que este processo ocorre ou ? favorecido ainda s?o pouco estudadas. A ?rea escolhida para o estudo possui uma importante reserva petrol?fera e um importante aqu?fero saturado em ?gua doce mete?rica. O objetivo desse trabalho ? estudar alguns par?metros qu?micos na ?gua produzida para cada zona-reservat?rio de produ??o em campos maduros de petr?leo da Forma??o A?u, utilizando a hidroqu?mica e a an?lise estat?stica para servir de refer?ncia e serem utilizados como ferramentas indicadoras frente a intervalos produtores de ?gua nos po?os produtores de petr?leo. Foram coletadas amostras de po?os distintos em 6 diferentes zonas de produ??o e foram medidos 50 par?metros, que podem ser classificados em tr?s grupos: ?nions, c?tions e propriedades f?sico-qu?micas (considerando-se apenas os par?metros que geraram valores acima dos limites de detec??o em todas as amostras). Atrav?s da caracteriza??o hidroqu?mica observou-se um dom?nio de ?guas cloretadas s?dicas e cloretadas c?lcicas ou magnesianas (mista) nas ?guas de po?os das diversas zonas na Forma??o A?u e, com a aplica??o de um tratamento estat?stico, obteve-se uma fun??o discriminante que distingue quimicamente as zonas de produ??o. Assim, foi poss?vel calcular a taxa de acerto de classifica??o da fun??o que foi de 76,3%. Para a valida??o desse modelo a taxa de acerto foi de 86%

Made available in DSpace on 2014-12-17T15:42:17Z (GMT). No. of bitstreams: 1 KyteriaSLF_TESE.pdf: 2934284 bytes, checksum: 962e569af56d47cdfb0e3425d7b7a8b9 (MD5) Previous issue date: 2013-02-26 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This study investigates the chemical species produced water from the reservoir areas of oil production in the field of Monte Alegre (onshore production) with a proposal of developing a model applied to the identification of the water produced in different zones or groups of zones.Starting from the concentrations of anions and c?tions from water produced as input parameters in Linear Discriminate Analysis, it was possible to estimate and compare the model predictions respecting the particularities of their methods in order to ascertain which one would be most appropriate. The methods Resubstitution, Holdout Method and Lachenbruch were used for adjustment and general evaluation of the built models. Of the estimated models for Wells producing water for a single production area, the most suitable method was the "Holdout Method and had a hit rate of 90%. Discriminant functions (CV1, CV2 and CV3) estimated in this model were used to modeling new functions for samples ofartificial mixtures of produced water (producedin our laboratory) and samples of mixtures actualproduced water (water collected inwellsproducingmore thanonezone).The experiment with these mixtures was carried out according to a schedule experimental mixtures simplex type-centroid also was simulated in which the presence of water from steam injectionin these tanks fora part of amostras. Using graphs of two and three dimensions was possible to estimate the proportion of water in the production area / Esse estudo investiga as esp?cies qu?micas presentes na ?gua produzida proveniente das zonas reservat?rio de produ??o de petr?leo no campo de Monte Alegre (produ??o onshore ) com a proposta de desenvolver um modelo aplicado ? identifica??o da ?gua produzida nas diferentes zonas ou grupos de zonas. A partir das concentra??es de ?nions e c?tions da ?gua produzida como par?metros de entrada na An?lise Discriminante Linear, foi poss?velcomparar os modelos de previs?o, respeitando as particularidades dos m?todos aplicados, a fim de indagar qual deles seria o mais adequado. Os m?todos Resubstitui??o, "Holdout Method" e Lachenbruch foram utilizados para ajuste e avalia??o geral dos modelos constru?dos. Dos modelos estimados para po?os produzindo ?gua por uma ?nica zona de produ??o, o m?todo mais adequado foi o "Holdout Method que apresentou taxa de acerto de 90%. As fun??es discriminantes (CV1, CV2 e CV3) estimadas nesse modelo foram utilizadas para modelar novas fun??es para amostras de misturas artificiais de ?gua produzida (produzidas em laborat?rio) e amostras de misturas reais de ?gua produzida (coletadas em po?os produzindo ?gua por mais de uma zona). O experimento com as referidas misturas foi executado de acordo com um planejamento experimental para misturas do tipo simplex-centroid no qual tamb?m, foi simulada a presen?a de ?gua proveniente de inje??o de vapor nestes reservat?rios para uma parte das amostras. Utilizando gr?ficos de duas e tr?s dimens?es foi poss?vel estimar a propor??o de ?gua nas zona de produ??o

Cette thèse traite du développement d'algorithmes d'estimation en haute dimension. Ces algorithmes visent à résoudre des problèmes de discrimination et de classification, notamment, en incorporant un mécanisme de sélection des variables pertinentes. Les contributions de cette thèse se concrétisent par deux algorithmes, GLOSS pour la discrimination et Mix-GLOSS pour la classification. Tous les deux sont basés sur le résolution d'une régression régularisée de type "optimal scoring" avec une formulation quadratique de la pénalité group-Lasso qui encourage l'élimination des descripteurs non-significatifs. Les fondements théoriques montrant que la régression de type "optimal scoring" pénalisée avec un terme "group-Lasso" permet de résoudre un problème d'analyse discriminante linéaire ont été développés ici pour la première fois. L'adaptation de cette théorie pour la classification avec l'algorithme EM n'est pas nouvelle, mais elle n'a jamais été détaillée précisément pour les pénalités qui induisent la parcimonie. Cette thèse démontre solidement que l'utilisation d'une régression de type "optimal scoring" pénalisée avec un terme "group-Lasso" à l'intérieur d'une boucle EM est possible. Nos algorithmes ont été testés avec des bases de données réelles et artificielles en haute dimension avec des résultats probants en terme de parcimonie, et ce, sans compromettre la performance du classifieur. / This thesis deals with the development of estimation algorithms with embedded feature selection the context of high dimensional data, in the supervised and unsupervised frameworks. The contributions of this work are materialized by two algorithms, GLOSS for the supervised domain and Mix-GLOSS for unsupervised counterpart. Both algorithms are based on the resolution of optimal scoring regression regularized with a quadratic formulation of the group-Lasso penalty which encourages the removal of uninformative features. The theoretical foundations that prove that a group-Lasso penalized optimal scoring regression can be used to solve a linear discriminant analysis bave been firstly developed in this work. The theory that adapts this technique to the unsupervised domain by means of the EM algorithm is not new, but it has never been clearly exposed for a sparsity-inducing penalty. This thesis solidly demonstrates that the utilization of group-Lasso penalized optimal scoring regression inside an EM algorithm is possible. Our algorithms have been tested with real and artificial high dimensional databases with impressive resuits from the point of view of the parsimony without compromising prediction performances.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Os pequenos e mÃdios produtores rurais de oleaginosas para produÃÃo de biodiesel necessitam de polÃticas pÃblicas voltadas para o desenvolvimento integrado e inclusÃo social, para que nÃo ocorra concentraÃÃo da produÃÃo, como acontece no caso do etanol. Assim, o objetivo desta pesquisa foi identificar quais as principais variÃveis de decisÃo e os seus graus de importÃncia para a tomada de decisÃo sobre o cultivo de oleaginosas por pequenos e mÃdios agricultores, no Ãmbito da cadeia produtiva do biodiesel, a fim de elaborar proposiÃÃes para subsidiar polÃticas governamentais de suporte ao setor, com base em gargalos encontrados e com Ãnfase para os procedimentos logÃsticos. Quanto aos procedimentos, a metodologia foi do tipo bibliogrÃfica e survey. O universo desta pesquisa à composto pelos pequenos e mÃdios produtores rurais do Estado do Cearà e especialistas da Ãrea agrÃcola, tendo a amostra totalizado 162 observaÃÃes vÃlidas. Como instrumento de coleta de dados, utilizou-se um questionÃrio estruturado e adaptado à realidade do Estado do CearÃ, contendo uma lista de 54 variÃveis capazes de influenciar a tomada de decisÃo no cultivo de oleaginosas, elaborado por meio da identificaÃÃo, na literatura revisada, dos principais constructos relacionados ao cultivo de oleaginosas. No que se refere à abordagem do problema, o modelo elaborado nesta pesquisa foi quantitativo, utilizando-se a AnÃlise Discriminante e a AnÃlise Fatorial como tÃcnicas para identificaÃÃo de semelhanÃas entre os respondentes e anÃlise das principais variÃveis de decisÃo, bem como dos seus graus de importÃncia, para os atores enfocados. Dentre os principais resultados do estudo, a AnÃlise Discriminante mostrou que nÃo hà diferenÃa nas respostas dos grupos formados por pequenos e mÃdios agricultores por um lado, e especialistas, por outro. Desta forma, aplicou-se a AnÃlise Fatorial com todas as observaÃÃes coletadas, constatando-se que os respondentes atribuÃram alta importÃncia à tomada de decisÃo, no cultivo de oleaginosas, Ãs variÃveis relativas ao cooperativismo e/ou associativismo, aos assentamentos e apoio que estes recebem de ÃrgÃos governamentais, seja de forma tÃcnica ou de formaÃÃo de recursos humanos. Assim, por exemplo, polÃticas que dÃem suporte aos assentamentos sÃo essenciais, jà que a agricultura familiar se baseia na comunidade e na interaÃÃo desta com o ambiente em que està inserido. Percebe-se, tambÃm, que sÃo valorizadas as variÃveis de decisÃo que estÃo relacionadas com a infra-estrutura logÃstica e de serviÃos bÃsicos e crÃdito. Finalmente, algumas proposiÃÃes sÃo feitas, com base nos resultados gerais da pesquisa, para subsidiar polÃticas governamentais para o setor de produÃÃo do biodiesel no Estado do CearÃ. / Brazilian median and small biooil country producers working on the Biodiesel production chain normally need the aid of public policies to guide the sector to sustainable development with social inclusion. Those policies are necessary to avoid the concentration of production in few large producers, as it has happened with sugar cane ethanol supply chain in Brazil. So, the aim of the present research work is to identify which are the key-variables and their relative importance to support public policies aiming the increase in agricultural production in Brazilian median and small biooil country producersâ sector. Data are elicited from a sample of 162 small and median producers in CearÃ, a poor Brazilian county in its northeastern region, as well as from experts in Biodiesel agricultural input practices. A questionnaire was applied involving 54 variables considered important, according with literature research, in terms of their impacts in biooil inputs production. The methodology applied to face the study problem uses both discriminant and factorial analysis to identify similarities among respondents and to analyze the main key-variables in their decision taking process. Main results of the present work show that are no statistically significant differences in responses of both groups researched. Factorial analysis application revealed high importance, to the individuals sampled, of variables related to associative producersâ clusters as well as to the support they may have from governmental authorities, relating to technical aspects or to personnel training in the focused sector. Familiar agriculture structured systems are based in community practices and in interaction with their environment. They also use to valorize availability of logistics equipments, basic services (educational and health facilities) and credit. Finally, many actions are put forward to support governmental policies aiming the development of the Biodiesel production sector in the County of CearÃ.

Submitted by Kamila Costa (kamilavasconceloscosta@gmail.com) on 2015-06-15T21:53:52Z No. of bitstreams: 1 Dissertação-Larissa M de Almeida.pdf: 5516102 bytes, checksum: e49d2bccd21168f811140c6accd54e8f (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-16T15:05:39Z (GMT) No. of bitstreams: 1 Dissertação-Larissa M de Almeida.pdf: 5516102 bytes, checksum: e49d2bccd21168f811140c6accd54e8f (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-16T15:07:25Z (GMT) No. of bitstreams: 1 Dissertação-Larissa M de Almeida.pdf: 5516102 bytes, checksum: e49d2bccd21168f811140c6accd54e8f (MD5) / Made available in DSpace on 2015-06-16T15:07:25Z (GMT). No. of bitstreams: 1 Dissertação-Larissa M de Almeida.pdf: 5516102 bytes, checksum: e49d2bccd21168f811140c6accd54e8f (MD5) Previous issue date: 2015-04-17 / Não Informada / About half the world's population lives in malaria risk areas. Moreover, given the globalization of travel, these diseases that were once considered exotic and mostly tropical are increasingly found in hospital emergency rooms around the world. And often when it comes to experience in tropical diseases, expert opinion most of the time is not available or not accessible in a timely manner. The task of an accurate and efficient diagnosis of malaria, essential in medical practice, can become complex. And the complexity of this process increases as patients have non-specific symptoms with a large amount of data and inaccurate information involved. In this approach, Uzoka and colleagues (2011a), from clinical information of 30 Nigerian patients with confirmed malaria, used the Analytic Hierarchy Process method (AHP) and Fuzzy methodology to conduct the evaluation of the severity of malaria. The results obtained were compared with the diagnosis of medical experts. This paper develops a new methodology to evaluate the severity of malaria and compare with the techniques used by Uzoka and colleagues (2011a). For this purpose the data set used is the same of that study. The technique used is the Artificial Neural Networks (ANN). Are evaluated three architectures with different numbers of neurons in the hidden layer, two training methodologies (leave-one-out and 10-fold cross-validation) and three stopping criteria, namely: the root mean square error, early stop and regularization. In the first phase, we use the full database. Subsequently, the feature extraction methods are used: in the second stage, the Principal Component Analysis (PCA) and in the third stage, the Linear Discriminant Analysis (LDA). The best result obtained in the three phases, it was with the full database, using the criterion of regularization associated with the leave-one-out method, of 83.3%. And the best result obtained in (Uzoka, Osuji and Obot, 2011) was with the fuzzy network which revealed 80% accuracy / Cerca de metade da população mundial vive em áreas de risco da malária. Além disso, dada a globalização das viagens, essas doenças que antes eram consideradas exóticas e principalmente tropicais são cada vez mais encontradas em salas de emergência de hospitais no mundo todo. E frequentemente quando se trata de experiência em doenças tropicais, a opinião de especialistas na maioria das vezes está indisponível ou não acessível em tempo hábil. A tarefa de chegar a um diagnóstico da malária preciso e eficaz, fundamental na prática médica, pode tornar-se complexa. E a complexidade desse processo aumenta à medida que os pacientes apresentam sintomas não específicos com uma grande quantidade de dados e informação imprecisa envolvida. Nesse sentido, Uzoka e colaboradores (2011a), a partir de informações clínicas de 30 pacientes nigerianos com diagnóstico confirmado de malária, utilizaram a metodologia Analytic Hierarchy Process (AHP) e metodologia Fuzzy para realizar a avaliação da gravidade da malária. Os resultados obtidos foram comparados com o diagnóstico de médicos especialistas. Esta dissertação desenvolve uma nova metodologia para avaliação da gravidade da malária e a compara com as técnicas utilizadas por Uzoka e colaboradores (2011a). Para tal o conjunto de dados utilizados é o mesmo do referido estudo. A técnica utilizada é a de Redes Neurais Artificiais (RNA). São avaliadas três arquiteturas com diferentes números de neurônios na camada escondida, duas metodologias de treinamento (leave-one-out e 10-fold cross-validation) e três critérios de parada, a saber: o erro médio quadrático, parada antecipada e regularização. Na primeira fase, é utilizado o banco de dados completo. Posteriormente, são utilizados os métodos de extração de características: na segunda fase, a Análise dos Componentes Principais (do inglês, Principal Component Analysis - PCA) e na terceira fase, a Análise Discriminante Linear (do inglês, Linear Discriminant Analysis – LDA). O melhor resultado obtido nas três fases, foi com o banco de dados completo, utilizando o critério de regularização, associado ao leave-one-out, de 83.3%. Já o melhor resultado obtido em (Uzoka, Osuji e Obot, 2011) foi com a rede fuzzy onde obteve 80% de acurácia.

Submitted by Kamila Costa (kamilavasconceloscosta@gmail.com) on 2015-06-18T20:16:38Z No. of bitstreams: 1 Dissertação-Carina Figueiredo Coelho.pdf: 3096964 bytes, checksum: 57c06ccd1fdc732a7cf9a50381d3806b (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-07-06T15:29:34Z (GMT) No. of bitstreams: 1 Dissertação-Carina Figueiredo Coelho.pdf: 3096964 bytes, checksum: 57c06ccd1fdc732a7cf9a50381d3806b (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-07-06T15:27:26Z (GMT) No. of bitstreams: 1 Dissertação-Carina Figueiredo Coelho.pdf: 3096964 bytes, checksum: 57c06ccd1fdc732a7cf9a50381d3806b (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-07-06T15:33:36Z (GMT) No. of bitstreams: 1 Dissertação-Carina Figueiredo Coelho.pdf: 3096964 bytes, checksum: 57c06ccd1fdc732a7cf9a50381d3806b (MD5) / Made available in DSpace on 2015-07-06T15:33:36Z (GMT). No. of bitstreams: 1 Dissertação-Carina Figueiredo Coelho.pdf: 3096964 bytes, checksum: 57c06ccd1fdc732a7cf9a50381d3806b (MD5) Previous issue date: 2013-06-28 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / We investigated use of finite mixture models with skew normal independent distributions to model the conditional distributions in discriminat analysis, particularly the skew normal and skew t. To evaluate this model, we developed a simulation study and applications with real data sets, analyzing error rates associated with the classifiers obtained with these mixture models. Problems were simulated with different structures and separations for the classes distributions employing different training set sizes. The results of the study suggest that the models evaluated are able to adjust to different problems studied, from the simplest to the most complex in terms of modeling the observations for classification purposes. With real data, where then shapes distributions of the class is unknown, the models showed reasonable error rates when compared to other classifiers. As a limitation for the analized sets of data was observed that modeling by finite mixtures requires large samples per class when the dimension of the feature vector is relatively high. / Investigamos o emprego de misturas finitas de densidades na família normal assimétrica independente, em particular a normal assimétrica e a t assimétrica, para modelar as distribuições condicionais do vetor de características em Análise Discriminante (AD). O objetivo é obter modelos capazes de modelar dados com estruturas mais complexas onde, por exemplo, temos assimetria e multimodalidade, o quemuitas vezes ocorrem em problemas reais de AD. Para avaliar esta modelagem, desenvolvemos um estudo de simulação e aplicações em dados reais, analisando a taxa de erro (TE) associadas aos classificadores obtidos com estes modelos de misturas. Foram simulados problemas com diferentes estruturas, relativas à separação e distribuição das classes e o tamanho do conjunto de treinamento. Os resultados do estudo sugerem que os modelos avaliados são capazes de se ajustar aos diferentes problemas estudados, desde os mais simples aos mais complexos, em termos de modelagem das observações para fins de classificação. Com os dados reais, situações onde desconhecemos as formas das distribuições nas classes, os modelos apresentaram TE’s razoáveis quando comparados a outros classificadores. Como uma limitação, para os conjuntos de dados analisados, foi observado que a modelagem por misturas finitas necessita de amostras grandes por classe em situações onde a dimensão do vetor de características é relativamente alta.

Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are the two popular independent feature extraction algorithms. Both of them extract features by projecting the parameter vectors into a new feature space through a linear transformation matrix. But they optimize the transformation matrix with different intentions. PCA optimizes the transformation matrix by finding the largest variations in the original feature space. LDA pursues the largest ratio of between-class variation and within-class variation when projecting the original feature space to a subspace. The drawback of independent feature extraction algorithms is that their optimization criteria are different from the classifier’s minimum classification error criterion, which may cause inconsistency between feature extraction and the classification stages of a pattern recognizer and consequently, degrade the performance of classifiers. A direct way to overcome this problem is to conduct feature extraction and classification jointly with a consistent criterion. Minimum classification Error (MCE) training algorithm provides such an integrated framework. MCE algorithm was first proposed for optimizing classifiers. It is a type of discriminative learning algorithm but achieves minimum classification error directly. The flexibility of the framework of MCE algorithm makes it convenient to conduct feature extraction and classification jointly. Conventional feature extraction and pattern classification algorithms, LDA, PCA, MCE training algorithm, minimum distance classifier, likelihood classifier and Bayesian classifier, are linear algorithms. The advantage of linear algorithms is their simplicity and ability to reduce feature dimensionalities. However, they have the limitation that the decision boundaries generated are linear and have little computational flexibility. SVM is a recently developed integrated pattern classification algorithm with non-linear formulation. It is based on the idea that the classification that a.ords dot-products can be computed efficiently in higher dimensional feature spaces. The classes which are not linearly separable in the original parametric space can be linearly separated in the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. However, SVM is a highly integrated and closed pattern classification system. It is very difficult to adopt feature extraction into SVM’s framework. Thus SVM is unable to conduct feature extraction tasks. This thesis investigates LDA and PCA for feature extraction and dimensionality reduction and proposes the application of MCE training algorithms for joint feature extraction and classification tasks. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithms in joint feature and classification tasks. SVM, as a non-linear pattern classification system is also investigated in this thesis. A reduced-dimensional SVM (RDSVM) is proposed to enable SVM to conduct feature extraction and classification jointly. All of the investigated and proposed algorithms are tested and compared firstly on a number of small databases, such as Deterding Vowels Database, Fisher’s IRIS database and German’s GLASS database. Then they are tested in a large-scale speech recognition experiment based on TIMIT database.

由於近年來汽機車的成長率大增，□=>許多重大營建工程陸續開工，導致 空氣污染日益嚴重，所以研究板橋地區一氧化氮濃度的預測模式。在本篇 論文中，我們首先應用集群分析將一氧化氮依濃度區分成數個集群，而後 運用區別分析診斷集群分析的結果是否合宜，最後找出集群內觀察值數目 最多的那個集群，然後將多變量時間序列中經過差分一次後的自我相關模 式應用在上面。目的是要尋求更精確的污染濃度預測值，以提供環保單位 一些訊息以作參考。

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia encountered in clinical practice and is associated with an increased mortality and morbidity. Identification of the sources of AF has been a goal of researchers for over 20 years. Current treatment procedures such as Cardio version, Radio Frequency Ablation, and multiple drugs have reduced the incidence of AF. Nevertheless, the success rate of these treatments is only 35-40% of the AF patients as they have limited effect in maintaining the patient in normal sinus rhythm. The problem stems from the fact that there are no methods developed to analyze the electrical activity generated by the cardiac cells during AF and to detect the aberrant atrial tissue that triggers it. In clinical practice, the sources triggering AF are generally expected to be at one of the four pulmonary veins in the left atrium. Classifying the signals originated from four pulmonary veins in left atrium has been the mainstay of signal analysis in this thesis which ultimately leads to correctly locating the source triggering AF. Unlike many of the current researchers where they use ECG signals for AF signal analysis, we collect intra cardiac signals along with ECG signals for AF analysis. AF Signal collected from catheters placed inside the heart gives us a better understanding of AF characteristics compared to the ECG. . In recent years, mechanisms leading to AF induction have begun to be explored but the current state of research and diagnosis of AF is mainly about the inspection of 12 lead ECG, QRS subtraction methods, spectral analysis to find the fibrillation rate and limited to establishment of its presence or absence. The main goal of this thesis research is to develop methodology and algorithm for finding the source of AF. Pattern recognition techniques were used to classify the AF signals originated from the four pulmonary veins. The classification of AF signals recorded by a stationary intra-cardiac catheter was done based on dominant frequency, frequency distribution and normalized power. Principal Component Analysis was used to reduce the dimensionality and further, Linear Discriminant Analysis was used as a classification technique. An algorithm has been developed and tested during recorded periods of AF with promising results.