Orientador: Celso Kazuyuki Morooka / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica / Made available in DSpace on 2018-07-18T14:04:10Z (GMT). No. of bitstreams: 1 Vardaro_Eduardo_M.pdf: 4228728 bytes, checksum: a5dc93b132115a6ead2c42e500846929 (MD5) Previous issue date: 1991 / Resumo: Neste trabalho desenvolveu-se um procedimento para a simulação no domínio do tempo dos movimentos de uma plataforma semi-submersível sujeita a ação de onda, vento e correnteza. Os efeitos devido ao vento e correnteza são obtidos pela discretização da superfície da semi-submersível em painéis. Também são considerados ( quase-estaticamente) os efeitos do sistema de ancoragem e riser. A reação hidrodinâmica é determinada pela convolução da transformada de Fourier no domínio do tempo da reação hidrodinâmica obtida no domínio da freqüência. Através da simulação no domínio do tempo foi analisada a influência do vento, onda e correnteza no comportamento do sistema de ancoragem. Além disso, verificou-se o que ocorre quando uma linha do sistema de ancoragem se rompe ou as condições de vento subitamente se alteram / Abstract: In this work a time domain procedure is developed to simulate the motions of a semisubmersible subjected to wave, wind and current. The effects of wind and current are obtained dividing the semisubmersible surface in panels. The effects of mooring system and riser are also quasi-staticaly considered. The hidrodynamic reaction is determined by the integral convolution from the Fourier transformed in the time domain of the frequency domain hidrodynamic reaction. Trough the time domain simulation it is analised the influence of the wind, wave and current on the behavior of the mooring system. Besides it is verified what happen when a rope of the mooring system failures or when the wind conditions change quickly / Mestrado / Mestre em Engenharia de Petróleo

Orientador : Yoshiyuki Hase / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Quimica / Made available in DSpace on 2018-07-19T03:11:28Z (GMT). No. of bitstreams: 1 Silva_LuisHenriqueMendesda_M.pdf: 2873829 bytes, checksum: 59caf6b1de339fd0070bb5a783963c83 (MD5) Previous issue date: 1994 / Mestrado

Due to the amount of flow simulation and measurement data, automatic detection, classification and visualization of features is necessary for an inspection. Therefore, many automated feature detection methods have been developed in recent years. However, one feature class is visualized afterwards in most cases, and many algorithms have problems in the presence of noise or superposition effects. In contrast, image processing and computer vision have robust methods for feature extraction and computation of derivatives of scalar fields. Furthermore, interpolation and other filter can be analyzed in detail. An application of these methods to vector fields would provide a solid theoretical basis for feature extraction. The authors suggest Clifford algebra as a mathematical framework for this task. Clifford algebra provides a unified notation for scalars and vectors as well as a multiplication of all basis elements. The Clifford product of two vectors provides the complete geometric information of the relative positions of these vectors. Integration of this product results in Clifford correlation and convolution which can be used for template matching on vector fields. Furthermore, for frequency analysis of vector fields and the behavior of vector-valued filters, a Clifford Fourier transform has been derived for 2 and 3 dimensions. Convolution and other theorems have been proved, and fast algorithms for the computation of the Clifford Fourier transform exist. Therefore the computation of Clifford convolution can be accelerated by computing it in Clifford Fourier domain. Clifford convolution and Fourier transform can be used for a thorough analysis and subsequent visualization of vector fields

The FIRST program (Far Infrared Spectroscopy in the Troposphere) was created as an Instrument Incubator Program (IIP) by NASA Langley to demonstrate improved technology readiness levels (TRLs) for two technologies needed in the design of new imaging Fourier transform spectrometers (IFTS). The IIP IFTS was developed at the Space Dynamics Laboratory and flown to an altitude of 103,000 feet on an instrumented NASA balloon payload. The sensor collected approximately 15,000 interferograms during its 6-hour flight. Fourier transforms (FFT) produced acceptable results except for noise equivalent temperature differences (NETD) that were five times higher than goal and inconclusive transforms at seven strong absorption features. An alternate transform technique, maximum likelihood estimation (MLE), was implemented to improve spectral estimations at the absorptions and to improve the NETD for the sensor. Iterative expectation-maximization (EM) algorithms provide numerical solutions for the MLE. Four combinatorial forms of the EM algorithm were developed. Forms of the EM algorithm were developed to optimize amplitude estimations as a function of assumed noise distributions. 'Direct' and 'indirect' EM forms were developed to process the asymmetrical interferograms recorded by the FIRST sensor. The direct method extends the standard even (cosine) EM algorithm to simultaneously transform both the sine and cosine components of the interferogram. The indirect method, uses Fourier and inverse Fourier transforms as pre-processors to convert the measured asymmetrical interferograms to even (cosine) interferograms. Using the indirect Gaussian EM form improved the measured NETD by approximately twenty percent between 100 and 700 wavenumbers. For wavenumbers less than 100 or greater than 700, the improvement increased to a factor of at least two out to 1500 wavenumbers. The indirect Gaussian produced inconclusive results in the areas of high absorption because of large bias errors introduced by the FFT/IFFT pre-processing. The indirect method was found to be inadequate for estimating spectra at the deep absorptions. The direct EM method, on the other hand, has the potential to produce improved amplitude estimations at the absorptions since there are no inherent biases in the algorithm's initial conditions at a cost in computer resources and execution times that are four times those needed for the indirect method.

De nombreuses problématiques d'apprentissage artificiel peuvent être modélisées grâce à des fonctions à valeur vectorielles. Les noyaux à valeurs opérateurs et leur espace de Hilbert à noyaux reproduisant à valeurs vectorielles associés donnent un cadre théorique et pratique pour apprendre de telles fonctions, étendant la littérature existante des noyaux scalaires. Cependant, lorsque les données sont nombreuses, ces méthodes sont peu utilisables, ne passant pas à l'échelle, car elle nécessite une quantité de mémoire évoluant quadratiquement et un temps de calcul évoluant cubiquement vis à vis du nombre de données, dans leur implémentation la plus naïve. Afin de faire passer les noyaux à valeurs opérateurs à l'échelle, nous étendons une technique d'approximation stochastique introduite dans le cadre des noyaux scalaires. L'idée est de tirer parti d'une fonction de redescription caractérisant le noyau à valeurs opérateurs, dont les fonctions associées vivent dans un espace de dimension infinie, afin d'obtenir un problème d'optimisation linéaire de dimension finie. Dans cette thèse nous développons dans un premier temps un cadre général afin de permettre l'approximation de noyaux de Mercer définis sur des groupes commutatifs localement compacts et étudions leurs propriétés ainsi que la complexités des algorithmes en découlant. Dans un second temps nous montrons des garanties théoriques en bornant l'erreur commise par l'approximation, avec grande probabilité. Enfin, nous mettons en évidence plusieurs applications des Représentations Opérateurs Aléatoires de Fourier (ORFF) telles que la classification multiple, l'apprentissage multi-tâche, la modélisation des séries temporelles, la régression fonctionnelle et la détection d'anomalies. Nous comparons également ce cadre avec d'autres méthodes de la littérature et concluons par des perspectives à moyen et long terme. / Many problems in Machine Learning can be cast into vector-valued approximation. Operator-Valued Kernels and vector-valued Reproducing Kernel Hilbert Spaces provide a theoretical and practical framework to address that issue, extending nicely the well-known setting of scalar-valued kernels. However large scale applications are usually not affordable with these tools that require an important computational power along with a large memory capacity. In this thesis, we propose and study scalable methods to perform regression with Operator-Valued Kernels. To achieve this goal, we extend Random Fourier Features, an approximation technique originally introduced for scalar-valued kernels, to Operator-Valued Kernels. The idea is to take advantage of an approximated operator-valued feature map in order to come up with a linear model in a finite-dimensional space. This thesis is structured as follows. First we develop a general framework devoted to the approximation of shift-invariant MErcer kernels on Locally Compact Abelian groups and study their properties along with the complexity of the algorithms based on them. Second we show theoretical guarantees by bounding the error due to the approximation, with high probability. Third, we study various applications of Operator Random Fourier Features (ORFF) to different tasks of Machine learning such as multi-class classification, multi-task learning, time serie modelling, functionnal regression and anomaly detection. We also compare the proposed framework with other state of the art methods. Fourth, we conclude by drawing short-term and mid-term perspectives of this work.

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<p> This thesis describes a proposed design of a special-purpose digital instrument that will obtain the first 32 coefficients of the Walsh-Fourier series of a low-fundamental frequency periodic voltage. The mathematics are developed for applying Walsh functions to obtain a Walsh-Fourier series in the same manner as sinusoidal waves are used to obtain a Fourier series of a periodic wave. It is shown how Walsh-Fourier coefficients are employed to obtain a Fourier series. Some familiar waveforms are shown as examples. The mathematical concepts are applied to the design of the instrument, of which two major portions have been constructed using integrated circuits. The Walsh-Fourier coefficients are available at the end of the second cycle of the input. The upper fundamental frequency limit of the instrument is approximately 60 Hz. There is no low-frequency limit.</p> / Thesis / Master of Engineering (MEngr)

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