Campylobacter spp., principalmente as espécies C. coli e C. jejuni, são a causa mais comum de doença bacteriana veiculada por alimentos na Europa, Estados Unidos e alguns outros locais do mundo. No Brasil, há uma escassez de estudos de C. coli, o que dificulta avaliar a dimensão do envolvimento dessa bactéria como causadora de doença nos seres humanos e em animais, bem como, determinar o impacto de sua presença em alimentos e no meio ambiente. O objetivo desse trabalho foi caracterizar molecularmente linhagens de C. coli isoladas de origens diversas no Brasil pela pesquisa da presença de genes relacionados à virulência por PCR, perfil de sensibilidade a antimicrobianos e pela análise da similaridade genotípica por métodos de tipagem molecular. Adicionalmente, o Índice de Discriminação (D) de tais metodologias foi verificado. Foram estudadas 63 linhagens de C. coli, isoladas de humanos (12), animais (21), alimentos (10) e ambiente (20), entre os anos de 1995 e 2011, nos Estados do Rio de Janeiro, São Paulo e Minas Gerais. Todas as linhagens apresentaram os genes flaA, cadF e sodB. O gene cdtB foi detectado em 20 (31,7%) linhagens, o gene flhA foi detectado em 11 (17,5%) linhagens, o gene dnaJ foi encontrado em 10 (15,9%) linhagens, o gene pldA foi detectado em sete (11,1%) linhagens, o gene iamA foi detectado em três (4,8%) linhagens, os genes cdtC e docA foram encontrados em duas (3,2%) linhagens, os genes cdtA e crsA foram encontrados em uma (1,6%) linhagem e os genes ciaB, wlaN, virB11 e racR não foram detectados. Dentre as 63 linhagens estudadas, 42 foram susceptíveis a todos os antimicrobianos testados. Das 21 linhagens resistentes, 10 (15,9%) foram resistentes a tetraciclina e doxaciclina, seis (9,5%) foram resistentes a ciprofloxacina e uma (1,6 %) foi resistente a eritromicina. Somente quatro (6,3%) linhagens foram resistentes a pelo menos duas diferentes classes de antibióticos testados simultaneamente. O dendrograma de similaridade genética de Pulsed field gel electrophoresis (PFGE) agrupou as 63 linhagens estudadas em dois grupos principais denominados PFGE-A e PFGE-B com similaridade genômica de 44,9% entre eles. Entretanto, algumas linhagens isoladas de humanos, animais, ambiente e alimentos apresentaram uma alta similaridade genotípica acima de 80% entre elas e, foram agrupadas em sete subgrupos denominados PFGE-A1 a PFGE-A7. O dendrograma de similaridade genômica das sequências da SVR do gene flaA agrupou as linhagens ii estudadas em dois grupos principais designados SVR-A e SVR-B, com similaridade acima de 83,1 % entre eles. Ademais, o depósito das sequências da SVR do gene flaA no banco de dados online demonstrou que os alelos 30 e o 1647 foram os mais frequentemente encontrados e permitiu a comparação das linhagens estudadas com os alelos descritos no banco de dados. Sete alelos, dentre os 22 encontrados, não haviam sido previamente descritos. A análise do locus CRISPR por HRMA dividiu as linhagens de C. coli em quatro diferentes perfis de melting. O Multilocus sequence typing (MLST) foi utilizado para tipar 20 linhagens de C. coli e foram obtidos 18 STs diferentes dos quais apenas dois já haviam sido previamente descritos. O D das metodologias de PFGE, sequenciamento da SVR do gene flaA, análise do locus CRISPR por HRMA e MLST foi de 0,986, 0,916, 0,550 e 0,989, respectivamente. Pode- se concluir que o potencial patogênico das linhagens de C. coli não foi evidenciado o que pode estar relacionado ao fato da maioria dos estudos envolvendo patogênese terem sido realizados para a espécie C. jejuni. Algumas linhagens apresentaram-se resistentes aos antimicrobianos testados, o que é preocupante uma vez que tais linhagens podem disseminar genes de resistência a outras isoladas de diversas fontes. Os resultados gerados pelos métodos de tipagem molecular por PFGE e sequenciamento da pequena região variável (SVR) do gene flaA demonstraram uma alta similaridade genotípica entre algumas linhagens de C. coli, sugerindo que uma possível contaminação tenha ocorrido entre linhagens isoladas de fontes clínicas e não clínicas ao longo de 16 anos no Brasil. Ademais, a análise dos alelos da SVR do gene flaA nos permitiu concluir que os alelos prevalentes nas linhagens estudadas diferem daqueles encontrados nos países Europeus. Os dados obtidos por MLST sugerem que as linhagens estudadas possuem uma grande diversidade genética entre si e em comparação com as linhagens isoladas em diferentes locais do mundo. Finalmente, as técnicas de MLST e PFGE foram as mais eficientes e adequadas na genotipagem das linhagens de C. coli estudadas. / Campylobacter spp., mainly the C. coli and C. jejuni species, are the most common cause of bacterial disease conveyed by food in Europe, United States, and other places worldwide. In Brazil, there is a paucity of studies on C. coli, which makes it difficult to evaluate the involvement of this bacterium as a cause of diseases in humans and animals, as well as to determine the impact of its presence in food and the environment. The aim of this study was to molecularly characterize C. coli strains isolated from diverse origins in Brazil by searching for the presence of virulence-related genes by PCR, antimicrobial sensitivity profile, and analysis of the genotypic similarity by molecular typing methods. Addicionaly, the Discriminatory Index (D) of those methodologies was acessed. Sixty-three C. coli strains isolated from humans (12), animals (21), food (10), and the environment (20) between 1995 and 2011, in the States of Rio de Janeiro, São Paulo, and Minas Gerais were studied. All strains presented the flaA, cadF and sodB genes. The cdtB gene was detected in 20 (31.7%) strains; the flhA gene was detected in 11 (17.5%) strains; the dnaJ gene was detected in 10 (15.9%) strains; the pldA gene was detected in 7 (11.1%) strains ; the iamA gene was detected in three (4.8%) strains; the cdtC and docA genes were found in two (3.2%) strains; the cdtA and crsA were found in one (1.6%) strain and the ciaB, wlaN, virB11 and racR genes were not detected. Among the 63 strains studied, 42 were susceptible to all antimicrobials tested. Of the 21 resistant strains, 10 (15.9%) were resistante to tetracycline and doxaciclyne, six (9.5%) showed resistance to ciprofloxacin, and one (1.6%) was resistant to erythromycin. Only four (6.3%) strains were simultaneously resistant to at least two different classes of the antibiotics tested. The dendrogram of genetic similarity of Pulsed field gel electrophoresis (PFGE) grouped the 63 strains studied into two groups namely PFGE-A and PFGE-B with a genomic similarity of 44.9% among them. However, some strains isolated from humans, animals, the environment and food presented a high genotypic similarity above 80% and were subdivided into seven groups designated as PFGE-A1 to PFGE-A7. The dendrogram of genetic similarity of the SRV-flaA gene sequences grouped the strains studied into two groups namely SVR-A and SVR-B, with similarity above 83.1% among them. Besides, the deposit of the SVR sequences of the flaA gene in the online database showed that the alleles 30 and 1647 were the iv most frequently found and allowed the comparison between the strains studied with the alleles described in the database. Seven alleles, among the 22 found have never been described before. The CRISPR locus analysis divided the C. coli strains into four different melting profiles. The Multilocus sequence typing (MLST) was used to type 20 C. coli strains and revealed 18 different STs among which just two had been previously described. The D of PFGE, SVR- flaA sequence, HRMA of CRISPR locus analysis and MLST was 0.986, 0.916, 0.550 and 0.989, respectively. In conclusion, the pathogenic potential of the C. coli strains was not highlighted, which could be related to the fact that the majority of the pathogenicity studies were performed with C. jejuni species. Some strains showed resistance to the antibiotics tested what is a concern once those strains may spread the resistance genes to other strains isolated from different sources. The results obtained by PFGE and SVR-flaA sequence showed a high genomic similarity among some C. coli strains which may suggest that a possible contamination may have occurred among clinical and non-clinical sources during 16 years in Brazil. Furthermore, the analysis of SVR- flaA alleles allowed the conclusion that the prevalent alleles in the strains studied were different from those found in European countries. The data obtained by MLST suggests that the strains studied had a high genomic diversity among them and in comparison with strains isolated from different places worldwide. Finally, the MLST and PFGE technicques were the most efficient and adequate in genotyping the C. coli strains studied.

Recently computational topology had an important development in data analysis giving birth to the field of Topological Data Analysis. Persistent homology appears as a fundamental tool based on the topology of data that can be represented as points in metric space. In this work, we apply techniques of Topological Data Analysis, more precisely, we use persistent homology to calculate topological features more persistent in data. In this sense, the persistence diagrams are processed as feature vectors for applying Machine Learning algorithms. In order to classification, we used the following classifiers: Partial Least Squares-Discriminant Analysis, Support Vector Machine, and Naive Bayes. For regression, we used Support Vector Regression and KNeighbors. Finally, we will give a certain statistical approach to analyze the accuracy of each classifier and regressor. / Recentemente a topologia computacional teve um importante desenvolvimento na análise de dados dando origem ao campo da Análise Topológica de Dados. A homologia persistente aparece como uma ferramenta fundamental baseada na topologia de dados que possam ser representados como pontos num espaço métrico. Neste trabalho, aplicamos técnicas da Análise Topológica de Dados, mais precisamente, usamos homologia persistente para calcular características topológicas mais persistentes em dados. Nesse sentido, os diagramas de persistencia são processados como vetores de características para posteriormente aplicar algoritmos de Aprendizado de Máquina. Para classificação, foram utilizados os seguintes classificadores: Análise de Discriminantes de Minimos Quadrados Parciais, Máquina de Vetores de Suporte, e Naive Bayes. Para a regressão, usamos a Regressão de Vetores de Suporte e KNeighbors. Finalmente, daremos uma certa abordagem estatística para analisar a precisão de cada classificador e regressor.

Cette thèse porte sur le diagnostic et le pronostic pour l’aide à la maintenance de systèmes complexes. Elle présente deux approches de diagnostic/pronostic qui permettent de générer les indicateurs utiles pour l’optimisation de la stratégie de maintenance. Plus précisément, ces approches permettent d’évaluer l’état de santé et de prédire la durée de vie résiduelle du système. Les approches présentées visent en particulier à pallier le problème d’absence d’indicateurs de dégradation. Les développements sont fondés sur l’utilisation d’observateurs, de formalisme de Modèle de Markov Caché, des méthodes d’inférences statistiques et des méthodes de prédiction de séries temporelles à base d’apprentissage afin de caractériser et prédire les modes de fonctionnement du système. Les deux approches sont illustrées sur des exemples de dégradation d’un système de régulation de niveau d’eau, d’une machine asynchrone et d’une batterie Li-Ion. / The research presented in this thesis deals of diagnosis and prognosis of complex systems. It presents two approaches that generate useful indicators for optimizing maintenance strategies. Specifically, these approaches are used to assess the level of degradation and estimate the Remaining Useful Life of the system. The aim of these approaches is to overcome for the lack of degradation indicators. The developments are based on observers, Hidden Markov Model formalism, statistical inference methods and learning-based methods in order to characterize and predict the system operating modes. To illustrate the proposed failure diagnosis/prognosis approaches, a simulated tank level control system, an induction motor and a Li-Ion battery were used.

Campylobacter spp., principalmente as espécies C. coli e C. jejuni, são a causa mais comum de doença bacteriana veiculada por alimentos na Europa, Estados Unidos e alguns outros locais do mundo. No Brasil, há uma escassez de estudos de C. coli, o que dificulta avaliar a dimensão do envolvimento dessa bactéria como causadora de doença nos seres humanos e em animais, bem como, determinar o impacto de sua presença em alimentos e no meio ambiente. O objetivo desse trabalho foi caracterizar molecularmente linhagens de C. coli isoladas de origens diversas no Brasil pela pesquisa da presença de genes relacionados à virulência por PCR, perfil de sensibilidade a antimicrobianos e pela análise da similaridade genotípica por métodos de tipagem molecular. Adicionalmente, o Índice de Discriminação (D) de tais metodologias foi verificado. Foram estudadas 63 linhagens de C. coli, isoladas de humanos (12), animais (21), alimentos (10) e ambiente (20), entre os anos de 1995 e 2011, nos Estados do Rio de Janeiro, São Paulo e Minas Gerais. Todas as linhagens apresentaram os genes flaA, cadF e sodB. O gene cdtB foi detectado em 20 (31,7%) linhagens, o gene flhA foi detectado em 11 (17,5%) linhagens, o gene dnaJ foi encontrado em 10 (15,9%) linhagens, o gene pldA foi detectado em sete (11,1%) linhagens, o gene iamA foi detectado em três (4,8%) linhagens, os genes cdtC e docA foram encontrados em duas (3,2%) linhagens, os genes cdtA e crsA foram encontrados em uma (1,6%) linhagem e os genes ciaB, wlaN, virB11 e racR não foram detectados. Dentre as 63 linhagens estudadas, 42 foram susceptíveis a todos os antimicrobianos testados. Das 21 linhagens resistentes, 10 (15,9%) foram resistentes a tetraciclina e doxaciclina, seis (9,5%) foram resistentes a ciprofloxacina e uma (1,6 %) foi resistente a eritromicina. Somente quatro (6,3%) linhagens foram resistentes a pelo menos duas diferentes classes de antibióticos testados simultaneamente. O dendrograma de similaridade genética de Pulsed field gel electrophoresis (PFGE) agrupou as 63 linhagens estudadas em dois grupos principais denominados PFGE-A e PFGE-B com similaridade genômica de 44,9% entre eles. Entretanto, algumas linhagens isoladas de humanos, animais, ambiente e alimentos apresentaram uma alta similaridade genotípica acima de 80% entre elas e, foram agrupadas em sete subgrupos denominados PFGE-A1 a PFGE-A7. O dendrograma de similaridade genômica das sequências da SVR do gene flaA agrupou as linhagens ii estudadas em dois grupos principais designados SVR-A e SVR-B, com similaridade acima de 83,1 % entre eles. Ademais, o depósito das sequências da SVR do gene flaA no banco de dados online demonstrou que os alelos 30 e o 1647 foram os mais frequentemente encontrados e permitiu a comparação das linhagens estudadas com os alelos descritos no banco de dados. Sete alelos, dentre os 22 encontrados, não haviam sido previamente descritos. A análise do locus CRISPR por HRMA dividiu as linhagens de C. coli em quatro diferentes perfis de melting. O Multilocus sequence typing (MLST) foi utilizado para tipar 20 linhagens de C. coli e foram obtidos 18 STs diferentes dos quais apenas dois já haviam sido previamente descritos. O D das metodologias de PFGE, sequenciamento da SVR do gene flaA, análise do locus CRISPR por HRMA e MLST foi de 0,986, 0,916, 0,550 e 0,989, respectivamente. Pode- se concluir que o potencial patogênico das linhagens de C. coli não foi evidenciado o que pode estar relacionado ao fato da maioria dos estudos envolvendo patogênese terem sido realizados para a espécie C. jejuni. Algumas linhagens apresentaram-se resistentes aos antimicrobianos testados, o que é preocupante uma vez que tais linhagens podem disseminar genes de resistência a outras isoladas de diversas fontes. Os resultados gerados pelos métodos de tipagem molecular por PFGE e sequenciamento da pequena região variável (SVR) do gene flaA demonstraram uma alta similaridade genotípica entre algumas linhagens de C. coli, sugerindo que uma possível contaminação tenha ocorrido entre linhagens isoladas de fontes clínicas e não clínicas ao longo de 16 anos no Brasil. Ademais, a análise dos alelos da SVR do gene flaA nos permitiu concluir que os alelos prevalentes nas linhagens estudadas diferem daqueles encontrados nos países Europeus. Os dados obtidos por MLST sugerem que as linhagens estudadas possuem uma grande diversidade genética entre si e em comparação com as linhagens isoladas em diferentes locais do mundo. Finalmente, as técnicas de MLST e PFGE foram as mais eficientes e adequadas na genotipagem das linhagens de C. coli estudadas. / Campylobacter spp., mainly the C. coli and C. jejuni species, are the most common cause of bacterial disease conveyed by food in Europe, United States, and other places worldwide. In Brazil, there is a paucity of studies on C. coli, which makes it difficult to evaluate the involvement of this bacterium as a cause of diseases in humans and animals, as well as to determine the impact of its presence in food and the environment. The aim of this study was to molecularly characterize C. coli strains isolated from diverse origins in Brazil by searching for the presence of virulence-related genes by PCR, antimicrobial sensitivity profile, and analysis of the genotypic similarity by molecular typing methods. Addicionaly, the Discriminatory Index (D) of those methodologies was acessed. Sixty-three C. coli strains isolated from humans (12), animals (21), food (10), and the environment (20) between 1995 and 2011, in the States of Rio de Janeiro, São Paulo, and Minas Gerais were studied. All strains presented the flaA, cadF and sodB genes. The cdtB gene was detected in 20 (31.7%) strains; the flhA gene was detected in 11 (17.5%) strains; the dnaJ gene was detected in 10 (15.9%) strains; the pldA gene was detected in 7 (11.1%) strains ; the iamA gene was detected in three (4.8%) strains; the cdtC and docA genes were found in two (3.2%) strains; the cdtA and crsA were found in one (1.6%) strain and the ciaB, wlaN, virB11 and racR genes were not detected. Among the 63 strains studied, 42 were susceptible to all antimicrobials tested. Of the 21 resistant strains, 10 (15.9%) were resistante to tetracycline and doxaciclyne, six (9.5%) showed resistance to ciprofloxacin, and one (1.6%) was resistant to erythromycin. Only four (6.3%) strains were simultaneously resistant to at least two different classes of the antibiotics tested. The dendrogram of genetic similarity of Pulsed field gel electrophoresis (PFGE) grouped the 63 strains studied into two groups namely PFGE-A and PFGE-B with a genomic similarity of 44.9% among them. However, some strains isolated from humans, animals, the environment and food presented a high genotypic similarity above 80% and were subdivided into seven groups designated as PFGE-A1 to PFGE-A7. The dendrogram of genetic similarity of the SRV-flaA gene sequences grouped the strains studied into two groups namely SVR-A and SVR-B, with similarity above 83.1% among them. Besides, the deposit of the SVR sequences of the flaA gene in the online database showed that the alleles 30 and 1647 were the iv most frequently found and allowed the comparison between the strains studied with the alleles described in the database. Seven alleles, among the 22 found have never been described before. The CRISPR locus analysis divided the C. coli strains into four different melting profiles. The Multilocus sequence typing (MLST) was used to type 20 C. coli strains and revealed 18 different STs among which just two had been previously described. The D of PFGE, SVR- flaA sequence, HRMA of CRISPR locus analysis and MLST was 0.986, 0.916, 0.550 and 0.989, respectively. In conclusion, the pathogenic potential of the C. coli strains was not highlighted, which could be related to the fact that the majority of the pathogenicity studies were performed with C. jejuni species. Some strains showed resistance to the antibiotics tested what is a concern once those strains may spread the resistance genes to other strains isolated from different sources. The results obtained by PFGE and SVR-flaA sequence showed a high genomic similarity among some C. coli strains which may suggest that a possible contamination may have occurred among clinical and non-clinical sources during 16 years in Brazil. Furthermore, the analysis of SVR- flaA alleles allowed the conclusion that the prevalent alleles in the strains studied were different from those found in European countries. The data obtained by MLST suggests that the strains studied had a high genomic diversity among them and in comparison with strains isolated from different places worldwide. Finally, the MLST and PFGE technicques were the most efficient and adequate in genotyping the C. coli strains studied.

This thesis looks at how accurately one can model accents in jazz solos, more specifically the sound level. Further understanding the structure of jazz solos can give a way of pedagogically presenting differences within music styles and even between performers. Some studies have tried to model perceived accents in different music styles. In other words, model how listeners perceive some tones as somehow accentuated and more important than others. Other studies have looked at how the sound level correlates to other attributes of the tone. But to our knowledge, no other studies have been made modeling actual accents within jazz solos, nor have other studies had such a big amount of training data. The training data used is a set of 456 solos from the Weimar Jazz Database. This is a database containing tone data and metadata from monophonic solos performed with multiple instruments. The features used for the training algorithms are features obtained from the software Director Musices created at the Royal Institute of Technology in Sweden; features obtained from the software "melfeature" created at the University of Music Franz Liszt Weimar in Germany; and features built upon tone data or solo metadata from the Weimar Jazz Database. A comparison between these is made. Three learning algorithms are used, Multiple Linear Regression (MLR), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost). The first two are simpler regression models while the last is an award-winning tree boosting algorithm. The tests resulted in eXtreme Gradient Boosting (XGBoost) having the highest accuracy when combining all the available features minus some features that were removed since they did not improve the accuracy. The accuracy was around 27% with a high standard deviation. This tells that there was quite some difference when predicting the different solos, some had an accuracy of about 67% while others did not predict one tone correctly in the entire solo. But as a general model, the accuracy is too low for actual practical use. Either the methods were not the optimal ones or jazz solos differ too much to find a general pattern. / Detta examensarbete undersöker hur väl man kan modellera accenter i jazz-solos, mer specifikt ljudnivån. En bredare förståelse för strukturen i jazzsolos kan ge ett sätt att pedagogiskt presentera skillnaderna mellan olika musikstilar och även mellan olika artister. Andra studier har försökt modellera uppfattade accenter inom olika musik-stilar. Det vill säga, modellera hur åhörare upplever vissa toner som accentuerade och viktigare än andra. Andra studier har undersökt hur ljudnivån är korrelerad till andra attribut hos tonen. Men såvitt vi vet, så finns det inga andra studier som modellerar faktiska accenter inom jazzsolos, eller som haft samma stora mängd träningsdata. Träningsdatan som använts är ett set av 456 solos tagna från Weimar Jazz Database. Databasen innehåller data på toner och metadata från monofoniska solos genomförda med olika instrument. Särdragen som använts för tränings-algoritmerna är särdrag erhållna från mjukvaran Director Musices skapad på Kungliga Tekniska Högskolan i Sverige; särdrag erhållna från mjukvaran ”melfeature” skapad på University of Music Franz Liszt Weimar i Tyskland; och särdrag skapade utifrån datat i Weimar Jazz Database. En jämförelse mellan dessa har också gjorts. Tre inlärningsalgoritmer har använts, Multiple Linear Regression (MLR), Support Vector Regression (SVR), och eXtreme Gradient Boosting (XGBoost). De första två är enklare regressionsalgoritmer, medan den senare är en prisbelönt trädförstärkningsalgoritm. Testen resulterade i att eXtreme Gradient Boosting (XGBoost) skapade en modell med högst noggrannhet givet alla tillgängliga särdrag som träningsdata minus vissa särdrag som tagits bort då de inte förbättrar noggrannheten. Den erhållna noggrannheten låg på runt 27% med en hög standardavvikelse. Detta pekar på att det finns stora skillnader mellan att förutsäga ljudnivån mellan de olika solin. Vissa solin gav en noggrannhet på runt 67% medan andra erhöll inte en endaste ljudnivå korrekt i hela solot. Men som en generell modell är noggrannheten för låg för att användas i praktiken. Antingen är de valda metoderna inte de bästa, eller så är jazzsolin för olika för att hitta ett generellt mönster som går att förutsäga.

Depuis les années 80, Les matériaux composites stratifié verre résine (SVR) ont été utilisés pour la construction des tuyaux et des réservoirs dans l'industrie chimique, y compris pour le stockage d’acides. Ce matériau composite présente une résistance supérieure à la corrosion. Cependant, des auteurs ont observé des ruptures accidentelles de réservoirs (horizontaux et verticaux) contenant des acides (chlorhydrique et sulfurique). Ces ruptures sont attribuées au mécanisme de corrosion sous contrainte (CSC). La corrosion des fibres de verre dans une solution acide est moins connue mais reste très importante. Ce mécanisme de corrosion, appelée désalcalinisation de la fibre peut provoquer la fissuration de la fibre de verre.Des essais de corrosion avec de l’acide chlorhydrique (37%) ont été effectués sur éprouvette SVR. Ces essais de corrosion ont été suivis par émission acoustique. Les observations au microscope électroniques à balayage (MEB) et les analyses physico-chimiques confirment la corrosion de fibres de verre dans une solution de HCl. L’utilisation de la micro-tomographie nous montre que cette technique permet d’avoir une information sur la profondeur d’attaque du matériau.Une approche statistique est utilisée pour caractériser les paramètres de la salve d’émission acoustique afin de les séparer. Le Clustering est fait en utilisant la méthode des k-moyennes. Trois classes d’émission acoustique distinctes ont ainsi été identifiées. L’analyse croisée de l’émission acoustique et des observations ont permis de relier les classes observées aux conséquences de la corrosion du SVR. / Since the 1980, Glass Reinforced Plastic (GRP) has been used for construction of pipes and tanks in the chemical industry, including the storage of mineral acids. This composite material offers superior and cost effective corrosion resistance. However, authors found accidental breakage of tanks (horizontal and vertical) containing mineral acids (hydrochloric and sulphuric). These failures are attributed to environmental stress-corrosion cracking (ESCC) mechanism. The corrosion of glass fibers in mineral acid solution is less known but very important. The mechanism of the corrosion, called leaching, is thought to induce tensile stresses in the surface of the glass. These stresses could be large enough to cause cracking of the fiber glass.Corrosion tests have been performed on GRP specimen. Aggressive environments used are hydrochloric acid (37%) This environment is known to react with E-glass. Corrosion tests have been monitored by acoustic emission.SEM observations and physicochemical analysis confirm the corrosion of glass fibers in HCl solution. The use of micro - tomography allows to have information on the depth of degradation of the material.Statistical approaches are used to characterize hit’s parameters. Clustering is made by using k-mean’s method. Three distinct acoustic emission classes are identified. Thanks to SEM observations and acoustic emission results, clusters can be assigned to the appearance of minor defects in the material.

No description available.

Accurate demand forecasting is a key capability for a manufacturing organization, more so, a semiconductor manufacturer. Many crucial decisions are based on demand forecasts. The semiconductor industry is characterized by very short product lifecycles (10 to 24 months) and extremely uncertain demand. The pace at which both the manufacturing technology and the product design changes, induce change in manufacturing throughput and potential demand. Well known methods like exponential smoothing, moving average, weighted moving average, ARMA, ARIMA, econometric methods and neural networks have been used in industry with varying degrees of success. We propose a novel forecasting technique which is based on Support Vector Regression (SVR). Specifically, we formulate ν-SVR models for semiconductor product demand data. We propose a 3-phased input vector modeling approach to comprehend demand characteristics learnt while building a standard ARIMA model on the data. Forecasting Experimentations are done for different semiconductor product demand data like 32 & 64 bit CPU products, 32bit Micro controller units, DSP for cellular products, NAND and NOR Flash Products. Demand data was provided by SRC(Semiconductor Research Consortium) Member Companies. Demand data was actual sales recorded at every month. Model performance is judged based on different performance metrics used in extant literature. Results of experimentation show that compared to other demand forecasting techniques ν-SVR can significantly reduce both mean absolute percentage errors and normalized mean-squared errors of forecasts. ν-SVR with our 3-phased input vector modeling approach performs better than standard ARIMA and simple ν-SVR models in most of the cases.

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised classification tasks. In this thesis, we propose new algorithms for solving semi-supervised binary classification problem using GP regression (GPR) models. The algorithms are closely related to semi-supervised classification based on support vector regression (SVR) and maximum margin clustering. The proposed algorithms are simple and easy to implement. Also, the hyper-parameters are estimated without resorting to expensive cross-validation technique. The algorithm based on sparse GPR model gives a sparse solution directly unlike the SVR based algorithm. Use of sparse GPR model helps in making the proposed algorithm scalable. The results of experiments on synthetic and real-world datasets demonstrate the efficacy of proposed sparse GP based algorithm for semi-supervised classification.

There is a demand for decision support tools that can model the electricity markets and allows to forecast the hourly electricity price. Many different ap- proach such as artificial neural network or support vector regression are used in the literature. This thesis provides comparison of several different estima- tors under one settings using available data from Czech electricity market. The resulting comparison of over 5000 different estimators led to a selection of several best performing models. The role of historical weather data (temper- ature, dew point and humidity) is also assesed within the comparison and it was found that while the inclusion of weather data might lead to overfitting, it is beneficial under the right circumstances. The best performing approach was the Lasso regression estimated using modified Lars. 1