The objective of the present thesis research is to apply state-of-the-art experimental and data analysis techniques to the study of gas-liquid pipe flows, with a focus on conditions occurring in header-feeder systems of nuclear reactors under different accident scenarios. Novel experimental techniques have been proposed for the identification of the flow regime and measurement of the flow rates of both phases in gas-liquid flows. These techniques were automated, non-intrusive and economical, which ensured that their use would be feasible in industrial as well as laboratory settings. Measurements of differential pressure and the gas and liquid flow rates were collected in vertical upwards air-water flow at near-atmospheric pressure. It was demonstrated that the probability density function of the normalized differential pressure was indicative of the flow regime and using non-linear dimensionality reduction (the Elastic Maps Algorithm), it was possible to automate the process of identifying the flow regime from the differential pressure signal. The relationship between the probability density function and the power spectral density of normalized differential pressure with the gas and liquid flow rates in air-water pipe flow was also established and a machine learning algorithm (using Independent Component Analysis and Artificial Neural Networks) was proposed for the estimation of the phase flow rates from these properties. The proposed methods were adapted for use with single and dual conductivity wire-mesh sensors in vertical upwards and downwards air--water flows. A thorough evaluation of the performance and measurement uncertainty of wire-mesh sensors in gas-liquid flows was also performed. Lastly, measurements of the flow distribution in feeder tubes supplied with air-water mixtures by a simplified header model were collected and correlated to the observed flow patterns in the header.

La prospection géoélectrique est largement utilisée au Maroc pour des reconnaissances hydrogéologique. Le but de ce travail et de proposer de nouvelles techniques d’interprétation des sondages électriques verticaux en un temps réduit, et aussi de bien exploiter une base de données de sondages électriques, par l’établissement entre autre des images 2D horizontales et verticales de l’estimation de la distribution des résistivités électriques apparentes (modélisation géostatistique, inversion, etc.). Dans le but de caractériser électriquement le secteur d’étude (nord-est du Bassin de Saïss), une analyse statistique des résistivités apparentes de sondages électriques verticaux a été réalisée. Cette simple analyse descriptive est suivie par une étude statistique multidirectionnelle : analyse en composantes principales (ACP) et par une classification hiérarchique ascendante (CHA). (...) Les résultats des analyses statistiques et géostatistiques complétés par les inversions des sondages moyens pas classe, ont mis en évidence la fiabilité de ces techniques pour l’interprétation d’un nombre important de sondages électriques au lieu de la méthode ordinaire qui se base sur l’inversion des sondages un par un et les corréler ultérieurement pour construire la structure globale du domaine étudié. Avec les techniques utilisées, dans le cadre de ce travail, des résultats très satisfaisants en un temps plus réduit sont obtenus. Les profils étudiés et inversés à l’aide du logiciel RES2Dinv montrent tous les trois grandes structures définies auparavant (Résistant-Conductrice-Résistant), par contre on note des variations intra formations. De plus, l’organisation spatiale des formations permet de confirmer l’existence de failles cohérentes avec la structure en horst et graben du bassin. / The Geoelectric prospection is usually used in Morocco for hydrogeological recognition. The purpose of this work is to propose new techniques for interpreting vertical electric soundings in a reduced time, and also to fully exploit a database of stored electrical soundings by the establishment, amongst other things, of the horizontal and vertical 2D images, estimating the distribution of apparent electrical resistivity (geostatistic modeling, inversion, etc.). In order to characterize electrically the study area (north-east of the Saïss Basin), a statistical analysis of apparent resistivity of vertical electric soundings was performed. This simple descriptive analysis is followed by a statistical analysis (principal component analysis PCA and ascending hierarchical classification HAC.) (...)The results of statistical analysis and geostatistical supplemented by inversion of the average electric sounding per class, highlighted the reliability of these techniques to the interpretation of a large number of electrical soundings instead of the usual method which is based on the inversion of the electrical sounding one by one and correlate them later, to build the global structure of the area studied. With the techniques used in this work, very satisfactory results in a more reduced time, for interpreting vertical electric soundings, are obtained. VIThe studied profiles and inverted using the software RES2Dinv show all three structures defined previously (Resistant – Conductive - resistant), on the other hand, there are variations within the same formation. In addition, the spatial organization of the formation makes it possible to confirm the existence of faults coherent with the structure in horst and graben basin.

L'évolution des technologies actuelles permet de traiter un grand nombre d'expériences (ou de simulations) et d'envisager un nombre important de paramètres. Cette situation conduit à des matrices de grande, voire très grande, dimension et nécessite le développement de nouveaux outils pour évaluer et visualiser ces données et, le cas échéant, en réduire la dimension. L'évaluation de la qualité de l'information apportée par l'ensemble de points constituant une base de données ou un plan d'expériences peut se faire au travers de critères basés sur des calculs de distance, qui renseigneront sur l'uniformité de la répartition dans l'espace multidimensionnel. Parmi les méthodes de visualisation, l'Analyse en Composantes Curvilignes a l'avantage de projeter des données en grande dimension dans un espace bidimensionnel en préservant la topologie locale, ce qui peut aider à détecter des amas de points ou des zones lacunaires. La réduction de dimension s'appuie sur une sélection judicieuse de sous-ensembles de points ou de variables, via des algorithmes. Les performances de ces méthodes ont été évaluées sur des cas d'étude issus des études QSAR, de la spectroscopie et de la simulation numérique. / Technological progress has now made many experiments (or simulations) possible, along with taking into account a large number of parameters, which result in (very) high-dimensional matrix requiring the development of new tools to assess and visualize the data and, if necessary, to reduce the dimension. The quality of the information provided by all points of a database or an experimental design can be assessed using criteria based on distances that will inform about the uniformity of repartition in a multidimensional space. Among the visualization methods, Curvilinear Component Analysis has the advantage of projecting high-dimensional data in a two-dimensional space with respect to the local topology. This also enables the detection of clusters of points or gaps. The dimensional reduction is based on a judicious selection of subsets of points or variables, via accurate algorithms. The performance of these methods was assessed on case studies of QSAR, spectroscopy and numeric simulation.

The feasibility of FTIR-based spectroscopy as a tool to measure cellular response to therapeutics was investigated. Fourier transform mid-infrared spectroscopy has been used in conjunction with multivariate analysis (MVA) to assess the chemistry of many clinically relevant biological materials; however, the technique has not yet found its place in a clinical setting. One issue that has held the technique back is due to the spectral distortions caused by resonant Mie scattering (RMieS), which affects the ability to confidently assign molecular assignments to the spectral signals from biomaterials. In the light of recently improved understanding of RMieS, resulting in a novel correction algorithm, the analytical robustness of corrected FTIR spectra was validated against multi-discipline methods to characterise a set of renal cell lines which were selected for their difference in morphology.After validation of the FTIR methodology by discriminating different cell lines, the second stage of analyses tested the sensitivity of FTIR technique by determining if discrete chemical differences could be highlighted within a cell population of the same origin. The renal carcinoma cell line 2245R contains a sub-population to contain a sub-population of cells displaying 'stem-cell like' properties. These stem-like cells, however, are difficult to isolate and characterise by conventional '-omic' means. Finally, cellular response to chemotherapeutics was investigated using the established renal cell lines CAKI-2 and A-498. For the model, 5-fluorouracil (5FU), an established chemotherapeutic agent with known mechanisms of action was used. Novel gold-based therapeutic compounds were also assessed in parallel to determine their efficacy against renal cell carcinoma. The novel compounds displayed initial activity, as the FTIR evidence suggested compounds were able to enter the cells in the first instance, evoking a cellular response. The long-term performance, tracked with standard proliferation assays and FTIR spectroscopy in the renal cancer cell model, however, was poor. Rather than dismissing the compounds as in-active, the compounds may simply be more effective in cancer cell types of a different nature. The FTIR-based evidence provided the means to suggest such a conclusion. Overall, the initial results suggest that the combination of FTIR and MVA, in the presence of the novel RMieS-EMSC algorithm can detect differences in cellular response to chemotherapeutics. The results were also in-line with complimentary biological-based techniques, demonstrating the powerful potential of the technique as a promising drug screening tool.

For multivariate data, when testing homogeneity of covariance matrices arising from two or more groups, Bartlett's (1937) modified likelihood ratio test statistic is appropriate to use under the null hypothesis of equal covariance matrices where the null distribution of the test statistic is based on the restrictive assumption of normality. Zhang and Boos (1992) provide a pooled bootstrap approach when the data cannot be assumed to be normally distributed. We give three alternative bootstrap techniques to testing homogeneity of covariance matrices when it is both inappropriate to pool the data into one single population as in the pooled bootstrap procedure and when the data are not normally distributed. We further show that our alternative bootstrap methodology can be extended to testing Flury's (1988) hierarchy of covariance structure models. Where deviations from normality exist, we show, by simulation, that the normal theory log-likelihood ratio test statistic is less viable compared with our bootstrap methodology. For functional data, Ramsay and Silverman (2005) and Lee et al (2002) together provide four computational techniques for functional principal component analysis (PCA) followed by covariance structure estimation. When the smoothing method for smoothing individual profiles is based on using least squares cubic B-splines or regression splines, we find that the ensuing covariance matrix estimate suffers from loss of dimensionality. We show that ridge regression can be used to resolve this problem, but only for the discretisation and numerical quadrature approaches to estimation, and that choice of a suitable ridge parameter is not arbitrary. We further show the unsuitability of regression splines when deciding on the optimal degree of smoothing to apply to individual profiles. To gain insight into smoothing parameter choice for functional data, we compare kernel and spline approaches to smoothing individual profiles in a nonparametric regression context. Our simulation results justify a kernel approach using a new criterion based on predicted squared error. We also show by simulation that, when taking account of correlation, a kernel approach using a generalized cross validatory type criterion performs well. These data-based methods for selecting the smoothing parameter are illustrated prior to a functional PCA on a real data set.

Atualmente, o ambiente industrial é caracterizado pela intensa globalização, competição entre cadeias de suprimentos, manutenção das competências centrais e terceirização dos demais serviços. Desta forma, a gestão das relações entre os agentes independentes da cadeia de suprimentos e do processo de aquisição são fatores potenciais para o aumento da competitividade empresarial. No contexto da construção civil, a seleção adequada dos parceiros de negócios é um elemento fundamental para o sucesso dos projetos, uma vez que uma grande proporção das atividades podem ser sub-contratadas e possuem relação de precedência entre si. Os suprimentos representam um percentual significativo dos custos das construções, 60%, dado que demonstra o potencial de lucratividade passível de ser atingida ao estruturar o processo de seleção de fornecedores na construção civil. Seleções baseadas no preço prejudicam os sub-empreiteiros e fornecedores mais responsáveis na concorrência, contribuindo para a queda do nível de desempenho e redução da eficiência global do projeto, uma vez que as ineficiências são somadas ao longo da cadeia. Através da estruturação do processo de seleção de fornecedores, é possível mitigar os riscos de suprimentos oriundos de falhas destes contratados ao longo da relação. O objetivo deste trabalho foi desenvolver uma sistemática para seleção de fornecedores críticos, considerando diversos critérios além do preço, entre qualitativos e quantitativos. A abordagem visa também, a eliminação da subjetividade do processo e a extração do melhor fornecedor de forma objetiva. Para tanto, foram definidas dimensões competitivas para avaliar os fornecedores e posteriormente foram utilizados dois métodos quantitativos, Teoria dos Conjuntos Difusos (TCD) e Análise de Componentes Principais (ACP), para selecionar o melhor fornecedor dentre as alternativas, com base na avaliação de múltiplos agentes. / Currently, the industrial environment is characterized by intense globalization, competition between supply chains, maintenance of core competencies and outsourcing of other services. Thus, the management of relationships between independent agents of the supply chain and the procurement process are potential factors for increasing enterprise competitiveness. In the construction context, the proper selection of business partners is a key element for the success of projects, since a large proportion of the activities can be sub-contracted and have precedence relationship between them. Supplies represent a significant percentage of the cost of buildings, 60%, information that demonstrates the potential of profitability that can be achieved by structuring the process of supplier selection in the construction industry. Selection based on price take off from competition the sub-contractors and suppliers more responsible, contributing to the decline in the level of performance and reduction in the overall project efficiency, since inefficiencies are summed through the chain. By structuring the supplier selection process, it is possible to mitigate the supply risk arising from failures of these suppliers during the relationship. The objective of this study was to develop a systematic for selection of critical suppliers, considering several criteria other than price, among qualitative and quantitative. The approach also aims at eliminating the subjectivity of the process and the extraction of the best supplier in an objective way. In order to that, competitive dimensions were set to evaluate vendors and subsequently two quantitative methods, Fuzzy Sets Theory (FST) and Principal Component Analysis (PCA) were used to select the best supplier among the alternatives based on multiple agents evaluation.

Orientador: Ronei Jesus Poppi / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-22T08:18:21Z (GMT). No. of bitstreams: 1 Avila_ThiagoCarvalhode_M.pdf: 7831860 bytes, checksum: 010f2295e00f097a9ecfaf3f498a7069 (MD5) Previous issue date: 2013 / Resumo: Este trabalho visou o uso de Espectroscopia Raman e de Quimiometria para monitoramento e controle da fermentação de glicose por Saccharomyces cerevisiae. Na primeira etapa, foi utilizada calibração multivariada baseada no método dos Mínimos Quadrados Parciais (PLS) para quantificação de glicose, etanol, glicerol, ácido acético e células. Os modelos foram desenvolvidos baseados nos valores de concentração obtidos pelos métodos de referência, cromatografia líquida de alta eficiência ¿ HPLC e espectrofotometria UV/Vis. Tanto na etapa de calibração quanto na de validação, a otimização foi realizada com eliminação de amostras anômalas, baseada nos valores de leverage, resíduos e escores. Na segunda etapa, cartas de controle multivariadas foram usadas para identificação de falhas em bateladas durante o processo de fermentação. Foram construídos modelos MPCA (Análise de Componentes Principais Multimodo) a partir de bateladas NOC (Condições Normais de Operação). As cartas de controle multivariadas foram aplicadas em dois modos de desdobramento dos dados obtidos durante o monitoramento, um preservando a direção das bateladas e outro a direção do tempo. As falhas estudadas foram temperatura, mudança no substrato e contaminação do sistema. No modo de desdobramento por bateladas, a carta de controle Q foi eficiente para detecção das falhas estudas, fato comprovado pela classificação correta de três bateladas NOC como dentro de controle. No entanto, a carta de controle T2 não foi capaz de identificar as falhas estudadas corretamente como fora de controle. O modo de desdobramento pelo tempo também apresentou classificações corretas das falhas estudadas / Abstract: This work aims the use of Raman Spectroscopy and Chemometrics in the monitoring and control in the fermentation of the glucose by Saccharomyces cerevisiae. In the first step, it was applied the multivariate calibration based on Partial Least Squares (PLS) for the quantification of glucose, ethanol, glycerol, acetic acid and cells. The developed of calibration models was performed against the concentration values obtained by the reference methods, High Performance Liquid Chromatography and UV/Vis spectrophotometer. The optimization of the calibration and validation steps, the elimination of outliers was performed based on the values of leverage, residues and scores. In the second step, multivariate control charts were used for identification of batch-fault during the fermentation process. Multi-way Principal Component Analysis (MPCA) models were developed from batch NOC (Normal Operation Conditions). The multivariate control charts were based on two modes of unfolding the multi-way data, obtained during monitoring, one preserving the direction of the batch and another the direction of time. The fault studied were temperature, changes in the substrate and contamination of the system. In unfolding batch mode, the chart Q was effective for detection of the faults studied, proven by the correctly classification of 3 NOC batches as in control. However, the chart T2 failed to identify faults studied. The unfolding in time mode, also presented correct classifications of the faults studied / Mestrado / Quimica Analitica / Mestre em Química

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Poměrně velké množství soukromých firem v České republice vyrábí lehká sportovní letadla. Značná část těchto letadel využívá kompozitní pružiny ve svých přistávacích zařízení. Tyto pružiny jsou buďto menší díly, absorbující energii (na příďové noze či ostruze), anebo jde celé pružnice hlavního podvozku. Všechny tyto pružiny sdílí základní charakteristiky: jsou vyrobeny převážně z jednosměrného kompozitu s významnou tloušťkou, hlavním druhem zatížení je ohybový moment a jsou očekávány velké deformace. Podobnou charakteristiku můžeme použít i při popisu hlavního nosníku křídla. Jak vypadá návrh a analýza takovýchto dílů? V zásadě jsou dvě možnosti. První z nich je poměrně jednoduchá analytická analýza, případně naprogramovaná v tabulkovém výpočetním prostředí. Nevýhody tohoto řešení jsou limitované možnosti výpočtu a jeho nízká flexibilita. Druhou možností je využít komerční konečno-prvkový systém pro analýzu, případně i pro optimalizaci. Pochopitelnou nevýhodou této možnosti je cena programu a obsluhy. Cílem této disertační práce je vytvořit program, jež nabídne třetí možnost, která umožní provádět zevrubnou analýzu řešených produktů bez nutnosti pořizovat nákladný software. Tento program zjednoduší a urychlí návrh a pevnostní kontrolu. Umožní uživateli rychle analyzovat více návrhových variant. Program dále bude zohledňovat specifika analyzovaných produktů (například velké deformace a lokální koncentrace napětí kolmo na vlákno). Z pohledu uživatele by program měl být jednoduchý na ovládání. Minimum množství vstupních dat a přehledné grafické rozhraní zajistí komfortní používání. Samostatně spustitelný program (bez instalace a bez podpůrného softwaru) zlepšuje rozšiřitelnost programu.

This master's thesis focuses on detection of pathological vertebrae in spinal CT utilized by machine learning. Theoretical part describes anatomy of the spine and occurrence of pathologies in CT image data, contains an overview of existing methods intended for automated detection of pathological vertebrae. Practical part devotes to design a computer aided detection systems to identify pathological vertebrae and to classify a type of pathology. Designed classification system is based on using neural network, which performs classification step and on principal component analysis (PCA), which is used to reducing the original number of observation features. For completing this task were used real data. Conclusion contains evaluation of obtained results.