in English The aim of this MA thesis is to bring new perspectives on the genre of the African-American slave narrative. Therefore, its wider historical, socio-political and gender contexts are considered and the circumstances surrounding its development and current criticism are briefly outlined. The point of departure is a discussion of definitions that vary among the scholars who select different criteria for the subject of definition. The existing diversity of the texts and voices is discussed in connection to Moses Grandy's Narrative of the Life of Moses Grandy, Late a Slave in the United States of America. Grandy's narrative, an account of the maritime slave life, is analyzed. Its traditional, uniform narrative structures are juxtaposed with passages where some aspects of his masculine identity, problematized by the institution of slavery, can be traced. Ultimately, the thesis attempts to show that while the conventionalized framework pre-defining the narrative outline and themes is delineated by James Olney, any generally recognized definition of the genre does not exist. As a result of that conclusion, the genre is defined in the scope of this thesis. After the major characteristics of the genre are discussed and the definition of the African- American slave narrative is put forward, more...

Orientador: José Antônio Perrella Balestieri / Abstract: Direct injection spark ignition (DISI) engines aim at reducing specific fuel consumption and achieving the strict emission standards in state of the art internal combustion engines. Therefore, in this work the goal is to develop code for simulations of the internal flow in DISI engines, as well as the phenomenon of fuel spray injection into the combustion chamber using a Lagrangian-Eulerian approach for representing the multiphase flow, and Large-eddy Simulations (LES) for modeling the turbulence of the continuum medium by means of the open-source CFD library OpenFOAM. In order to validate the obtained results and the developed models, experimental data from the Darmstadt optical engine, and the non-reactive “Spray G” gasoline injection case, along with the reactive “Spray A” case from the Engine Combustion Network (ECN) will be employed. Finally, a novel open-source solver will be proposed to simulate the Darmstadt optical engine in motored and fired operation under stratified mixture condition, using data compiled by the Darmstadt Engine Workshop (DEW) for validation. Moreover, a deep learning framework is presented to train an artificial neural network (ANN) with the engine LES data generated in this work, in order to make predictions of the small scale turbulence behavior. / Resumo: Motores de ignição a centelha com injeção direta (direct injection spark ignition engines, DISI engines) visam reduzir o consumo específico de combustível e respeitar os restritos níveis de emissão em motores de combustão interna de última geração. Assim, pretende-se com este trabalho desenvolver código para simulação do escoamento interno em motores DISI, assim como os fenômenos de injeção de combustível no interior da câmara de combustão utilizando uma abordagem Lagrangeana-Euleriana para representação do escoamento multifásico e Simulação de Grandes Escalas (Large-eddy simulation, LES) para a modelagem da turbulência no meio contínuo, por intermédio da biblioteca CFD de código aberto OpenFOAM. De modo a validar os resultados e os modelos desenvolvidos, dados experimentais serão utilizados, obtidos do motor óptico de Darmstadt, e do caso de teste de injeção de gasolina não-reativo “Spray G”, juntamente com o caso reativo “Spray A” da Rede de Combustão em Motores (Engine Combustion Network, ECN). Enfim, um novo código aberto será proposto para simular o motor óptico de Darmstadt em condições de escoamento a frio (sem combustão) e com combustão em condição de mistura estratificada, usando dados compilados pelo Workshop do Motor de Darmstadt (Darmstadt Engine Workshop, DEW) para validação. Além disso, uma abordagem de aprendizado profundo (deep learning) será apresentada para treinar uma rede neural artificial (artificial neural network, ANN) com dados de simulação LES de moto... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor

Neste trabalho está apresentada uma alternativa para o cálculo do fator de concentração de tensões (FCT) em juntas tubulares soldadas do tipo Y. Redes Neurais Artificiais (RNA) foram utilizadas para representar a distribuição de tensões ao longo da junta tubular para os casos de carregamento força axial no plano e momento fletor no plano. As RNA podem aprender a partir de um conjunto de dados sem a necessidade de uma expressão matemática entre as variáveis dependentes e independentes; representa uma vantagem sobre o procedimento normalmente utilizado, ou seja, as equações paramétricas. O modelo proposto representa um avanço no projeto de juntas tubulares, uma vez que evita a necessidade de se conhecer uma expressão matemática para representar a distribuição de tensões na junta e fornece um método mais preciso para avaliar a distribuição de tensões ao longo da junta soldada. O conjunto de dados utilizado foi formado a partir de simulações numéricas das juntas soldadas através do MEF, nas quais foi considerada a geometria do cordão de solda. / An alternative approach to calculate stress concentration factors (SCF) in Y-type welded tubular joints is presented. Artificial Neural Networks (ANN) were used to represent the stress distribution along the tubular joints in both in-plane axial force and in-plane bending moment load cases. ANN can learn from a database without establishing a mathematical expression between dependent and independent variables, which is an advantage over the usual parametric equations approach. The proposed model represents an improvement in the tubular joints design, since it avoids the previous knowing of a mathematical expression to represent the stress distribution in the joint and provides an accurate method to evaluate the stress distribution along the welded fillet joint. The database herein used was completed with FE simulations of tubular joints which consider the geometry of the weld fillet.

nÃo hà / Sistemas climatolÃgicos sÃo caracterizados por apresentarem modelagem complexa e de baixa previsibilidade. Em regiÃes de clima semiÃrido, como o Nordeste Brasileiro, informaÃÃes de previsÃo climatolÃgicas sÃo de interesse para um melhor aproveitamento dos recursos hÃdricos. O Estado do CearÃ, localizado no norte do Nordeste Brasileiro, sofre periodicamente com os problemas de estiagem. Atualmente a FundaÃÃo Cearense de Meteorologia e Recursos HÃdricos (FUNCEME), ÃrgÃo pertencente ao governo do Estado do CearÃ, à responsÃvel por gerar pesquisas voltadas a trazer um melhor entendimento fenomenolÃgico do clima do Estado e com isso efetuar uma melhor previsÃo de como serà o perÃodo de chuvas. Hoje a FundaÃÃo utiliza-se de modelagem numÃrica composta por dois modelos regionais, Modelo Regional Espectral 97 (MRE) e o Regional Modeling Atmospheric System (RAMS), aninhados por uma tÃcnica de downscaling ao modelo dinÃmico de grande escala ECHAM4.5, para efetuar suas previsÃes. Os modelos dinÃmicos sÃo caracterizados por apresentarem elevado custo computacional, grande quantidade de dados para sua entrada e alta complexidade na utilizaÃÃo. O desenvolvimento de modelos de previsÃo baseados em Redes Neurais Artificias (RNA) abrange diversas Ãreas do conhecimento e tem apresentado resultados promissores. Modelos baseados em redes neurais sÃo capazes de reproduzir deferentes tipos de sistemas atravÃs da sua capacidade de aprendizado. Nesta dissertaÃÃo foi desenvolvido um modelo de previsÃo de chuvas para as oito regiÃes homogÃneas do Estado do CearÃ, que apresenta um baixo custo computacional e de fÃcil utilizaÃÃo. Para atingir este desenvolvimento foi utilizada uma RNA baseada na tÃcnica Neo-Fuzzy Neuron (NFN). Apesar de ser proposto um novo modelo de previsÃo, nÃo se deseja a substituiÃÃo dos atuais modelos, o novo modelo proposto nesta dissertaÃÃo tem por finalidade enriquecer as informaÃÃes geradas atravÃs de modelos de previsÃo para que assim possa ser gerada uma melhor prediÃÃo de como serà o perÃodo de chuvas no Estado do CearÃ. O modelo proposto foi comparado ao modelo MRE que à atualmente utilizado pela FUNCEME para suas previsÃes. Nesta comparaÃÃo utilizou-se como indicadores de desempenho: tempo de execuÃÃo, valor da raiz quadrada do erro mÃdio quadrÃtico (REMQ) e a correlaÃÃo com os valores observados. Ao final pode-se concluir que o modelo desenvolvido apresentou um melhor desempenho com menor tempo de processamento em relaÃÃo ao modelo dinÃmico MRE para efetuar a previsÃo de chuvas. / Climatological systems are characterized by complex modeling and having low predictability. In semi-arid regions, as the Brazilian Northeast, weather forecast information are necessary for the maintenance of life and a better use of water resources. The State of CearÃ, located on the north of Brazilian Northeast, is a region that suffers with drought for a long time. The FundaÃÃo Cearense de Meteorologia e Recursos HÃdricos (FUNCEME), which belongs to the state government, is responsible for generating research to bring a better phenomenological understanding on the weather of the State of Cearà and thus make a better prediction on how the rainy season will be. Today the foundation makes use of numerical modeling consisting of two regional models, the Regional Spectral Model (RSM) and the Regional Modeling Atmospheric System (RAMS), nested by a downscaling technique to the large scale dynamic model ECHAM4.5, in order to do its predictions. Dynamic models are characterized by their high computational costs, large amounts of information on its input and high complexity usage. The development of forecasting models based on Artificial Neural Networks (ANN) covers various areas of knowledge showing promising results. Neural network based models are capable of reproducing different types of systems through its learning capability. In this thesis it was developed a model for predicting rain for the eight homogeneous regions of the state of Cearà that presents low computational cost and easy use. In order to achieve this development it was used an ANN base on a Neo-Fuzzy Neuron (NFN) technique. Despite being offered a new prediction model, this thesis aims to enrich the information generated by forecast models and do a better prediction on the rainy season of the State of CearÃ. The proposed model was compared to the RSM model that is currently in use by FUNCEME in its predictions. In this comparison, as performance indicators, it was used: the execution time, value of the root mean square error (RMSE) and the correlation with the observed values. At the end, it is concluded that the proposed model had a better performance and was faster than the RSM dynamic model in its predictions.

The University of Canberra (UC) has been involved in GPS processing since the late 1980s. This processing commenced with the GOTEX 1988 campaign and progressed through a series of project specific regional campaigns to the current daily processing of a distributed set of continuously operating sites for the determination of precise GPS station positions for user applications. Most of these earlier campaigns covered only short periods of time, ranging from a few weeks to multiple occupations of a few days to a time over one to two years. With software developments, these multiple occupations were able to be combined to produce results from which crustal motion velocities could be extracted. This first became feasible with the processing of the Australian National Network (ANN), which yielded realistic tectonic velocities from two occupations (1992 and 1993) of sites 12 months apart. Subsequently, this was successfully extended by a further 12 months, with re-occupation of certain sites for a third time in 1994. Analysis of the results indicated that the accuracy of determining the earth signals improved as the time span from first to last observation was increased. The same was true also for the determination of the position of global references sites. However, by current standards the results achieved were poor. Consequently, the process was extended to combine the results of subsequent campaigns with the original ANN data set. From 1995 to 1999, campaigns were conducted across Australia, covering many State and tide gauge sites included in the original ANN solution. These provided additional multiple occupations to improve the determinations for both position and velocity. UC has maintained a data set of the global IGS sites, commencing with the IGS pilot campaign of 1992. Daily data sets for those global sites, which contained days common to the regional campaigns, were processed to produce our own independent global orbit and reference frame connection. The motivation for doing so was fourfold. �Firstly, to see if historic data could be reprocessed using current modern software and thus be able to be incorporated in this and other analysts research programs. �Secondly, to compare the results of the reprocessing of the original data set using modern software with the original ANN solution and then validate both the solutions. �Thirdly, to extend the timespan of observations processed to include more recent campaigns on as many original sites as possible. This to achieve a stronger solution upon which to base the determination of an Australian tectonic plate velocity model and provide quality assurance on the solution comparisons with re-observed sites. �Fourthly, to develop a set of transformation parameters between current coordinate systems and the GDA94 system so as to be able to incorporate new results into the previous system. The final selection of regional and global sessions, spanning from mid 1992 to late 2002, contained almost 1000 individual daily solutions. From this 10 year data span a well determined rigid plate tectonic motion model was produced for Australia. This site velocity model was needed to develop a transformation between the thesis solution in ITRF00 an the GDA94 solution in ITRF92. The significant advantage of the plate velocity model is that all Australian sites can now have computed a realistic velocity, rather than being given a value which has been interpolated between sites whose velocities had been determined over a one or two year span. This plate velocity model is compared with the current tectonic motion NNR-NUVEL-1A model and other recently published models. To perform the comparison between the thesis solution in ITRF00 and the GDA solution in ITRF92 a transformation was developed between the two reference systems. This set of transformation parameters, in conjunction with the plate velocity model developed, enables site solutions at any epoch in the current ITRF00 to be converted onto the GDA94, and vice versa, with a simple, non-varying seven parameter transformation. The comparisons between the solutions are analysed for both horizontal position and height consistency. There were 77 sites whose differences were compared. The horizontal consistency was within estimated precisions for 75 of the 77 sites. However, the vertical comparisons revealed many of the single epoch sites, especially in 1992, have inconsistent results between the two solutions. The heights from this thesis for some West Australian sites were compared with analysis done by DOLA and the height recoveries are very similar, indicating a weakness in the GDA94 solution for some of the single epoch sites. Some of these differences have been resolved but others are still under investigation. This thesis describes the repocessing of the original ANN data set, the addition of later data sets, the results obtained, and the validation comparisons of the old and new solutions. As well as the plate velocity model, transformation is provided which enables the user to compute between the GDA94 system, and any epoch result in ITRF00. Recommendations are made as to which sites need additional work. This includes sites which only need further analysis or investigation and those which require further observations to achieve a result which will have acceptable accuracy and reliability.

<p>In this study, Artificial Neural Network (ANN) is used to check the integrity of the Global Positioning Recommended Minimum (GPRMC) sentences. The GPRMC sentences are the most common sentences transmitted by the Global Positioning System (GPS) devices. This sentence contains nearly every thing a GPS application needs. The data integrity is compared on the basis of the classification accuracy and the minimum error obtained using the ANN. The ANN requires data to be presented in a certain format supported by the learning process of the network. Therefore a certain amount of data processing is needed before training patterns are presented to the network. The data pre processing is done by the design and development of different algorithms in C# using Visual Studio.Net 2003. This study uses the BackPropagation (BP) feed forward multilayer ANN algorithm with the learning rate and the momentum as its parameters. The results are analyzed based on different ANN architectures, classification accuracy, Sum of Square Error (SSE), variables sensitivity analysis and training graph. The best obtained ANN architecture shows a good performance with the selection classification of 96.79 % and the selection sum of square error 0.2022. This study uses the ANN tool Trajan 6.0 Demonstrator.</p>

In this study, Artificial Neural Network (ANN) is used to check the integrity of the Global Positioning Recommended Minimum (GPRMC) sentences. The GPRMC sentences are the most common sentences transmitted by the Global Positioning System (GPS) devices. This sentence contains nearly every thing a GPS application needs. The data integrity is compared on the basis of the classification accuracy and the minimum error obtained using the ANN. The ANN requires data to be presented in a certain format supported by the learning process of the network. Therefore a certain amount of data processing is needed before training patterns are presented to the network. The data pre processing is done by the design and development of different algorithms in C# using Visual Studio.Net 2003. This study uses the BackPropagation (BP) feed forward multilayer ANN algorithm with the learning rate and the momentum as its parameters. The results are analyzed based on different ANN architectures, classification accuracy, Sum of Square Error (SSE), variables sensitivity analysis and training graph. The best obtained ANN architecture shows a good performance with the selection classification of 96.79 % and the selection sum of square error 0.2022. This study uses the ANN tool Trajan 6.0 Demonstrator.

Power systems all over the world advance towards the direction of deregulation in the past few years. Introducing competition mechanism and the principle of market rules in deregulation. Utility companies will face unprecedented changes and challenges. Taiwan power company is also working on the deregulation direction with a competitive environment opened up, it will improve the scientific and technological levels and the service quality of electricity. Load management functions as the marginal price of electricity is predicted. Consumers can get Real-Time Pricing information determine their own buying strategy. One most representative deregulation example in U.S.A. is the PJM(Pennsylvania¡BNew Jersey¡BMaryland)system combining generating, transmitting, distribution and sales of electricity. It offers the information of real-time power supply and is one of the cases in the world. Historical data in the thesis comes from PJM. Artificial Neural Network was designed to the Locational Marginal Price(LMP), considering the factors such as temperature and other relevant data from deregulation with the introduction of various parameters in forecasting, and the use of week as a counting base. LMP will be forecasted. The forecasted results will be to check the accuracy and performance with initial data.

This project examines contemporary performances that disrupt normative understandings of time/history. I argue that the complimentary regimes of heterosexuality and capitalism produce the temporal logics that create the psychic and material conditions under which U.S. queer subjects experience everyday, national, and transnational trauma. These logics include the construction of time/history as linear, teleological, and progress-oriented, and the idealized citizen as similarly straight, productive, and amnesic. I then analyze the ways in which queer performance can resist and transform chrono-normativity by creating "time slips": worlds in which past and present are given permission to touch; history/memory to repeat; and the future to reside in the now. Case studies include Ann Carlson and Mary Ellen Strom's Geyser Land (2003); floodlines (2004-2010), which I conceived and directed; and Peggy Shaw and The Clod Ensemble's Must: The Inside Story (2011). I situate my analysis against the backdrop of a post-9/11 security state that makes these performative disruptions particularly vital at this historical moment. / text

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