Made available in DSpace on 2017-07-21T20:43:48Z (GMT). No. of bitstreams: 1 Dannilo Eduardo Ferreira.pdf: 9079253 bytes, checksum: c166172d47d556878be0589dd0a79c96 (MD5) Previous issue date: 2015-08-31 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Austenitic stainless steels have a wide range of applications and are widely used in applications requiring high corrosion resistance, and CF-8M class are generally submitted to corrosive environments in liquid media. The microstructure of these steels has an austenitic matrix and a variable amount of delta ferrite resulting from the casting process, which is affected by process variables like cooling rate, and chemical composition of the melt. In this work were characterized four alloys of CF-8M class austenitic stainless steels. The chemical compositions differ only in chromium and nickel contents in order to generate different amounts of chromium equivalent (Creq) and nickel equivalent (Nieq) according to the Schoefer diagram with ratio Creq/Nieq ranging between 0.95 and 1.35. The variation in the value of the ratio Creq/Nieq aims to observe the influence of chemical composition on solidification mode and the phase transformations during processing of the alloy. The alloys were cast in the form of pins which samples were removed for characterization. The microstructural characterization of alloys was made with the techniques of optical microscopy with image analysis, Field emission microscope (SEM / FEG) with energy dispersive X-ray spectrometry and electron backscatter diffraction (EDS / EBSD) to compositional map. The ferritoscopy technique, EBSD phase map and XRD were used to determine the ratio between phases. Thermal analysis by DSC determined the solidification interval for alloys in the study and phase transformations resulting during cooling to room temperature. The results showed that alloys 1, 2 and 4 solidify with FA mode, as expected. The alloy 3 showed a corresponding microstructure of eutectic/peritectic solidification, not presented in any of the existing diagrams in the literature. The measures of δ-ferrite fraction for all leagues were valid for the values proposed by Schoefer diagram. The examination of the micrographs of alloys shows the variety of microstructures that a CF-8M steel can display only altering its chemical composition. The EDS/EBSD maps showed a higher segregation of alloying elements between the phases for alloys 1, 2 and 4, due to the solidification mode and further transformation in solid state. The alloy 3 showed a smaller difference in composition between phases and less variation within the same phase, which features a eutectic/peritectic solidification. The comparison between the different regions of the samples presented variation in the fraction and morphology of ferrite. The range of solidification of the four alloys in the study was within a maximum level of 4 ° C, expected to solidification with ferrite as the first phase to form from the liquid. It was observed for the alloys to solidify with FA mode a phase transformation in the solid state in the range 1320 °C to 1340 °C, corresponding to γ transformation. The energy of this transformation is inversely proportional to the amount of ferrite observed in the final microstructure of alloys. / Os aços inoxidáveis austeníticos têm uma vasta gama de aplicações, sendo muito utilizados em aplicações que requerem grande resistência à corrosão, sendo as ligas da classe CF-8M geralmente submetidas a ambientes corrosivos em meios líquidos. A microestrutura desses aços apresenta uma matriz austenítica e uma quantidade variável de ferrita delta resultante do processo de fundição, a qual é afetada por variáveis de processo como a taxa de resfriamento e composição química da liga. No presente trabalho foram caracterizadas quatro ligas de aços inoxidáveis austeníticos da classe CF-8M. As composições químicas variam somente os teores de cromo e níquel com o intuito de gerar valores diferentes de cromo equivalente (Creq) e níquel equivalente (Nieq) segundo o diagrama de Schoefer com relações Creq/Nieq variando entre 0,95 e 1,35. A variação no valor da relação Creq/Nieq tem por objetivo observar a influência da composição química no modo de solidificação e nas transformações de fase durante o processamento das liga. As ligas foram vazadas em forma de pinos dos quais foram retiradas as amostras para caracterização. A caracterização microestrutural das ligas foi feita com auxílio das técnicas de microscopia óptica com análise de imagens, microscópio eletrônico de varredura de efeito de campo (MEV/FEG) com microanálise química e difração de elétrons retro espalhados (EDS/EBSD) para mapeamento composicional. A técnica de ferritoscopia, mapeamento de fases por EBSD e difração de raios X foram utilizadas para a determinação da proporção entre fases. As análises térmicas por DSC determinaram o intervalo de solidificação para as ligas em estudo e as transformações de fase decorrentes ao longo do resfriamento até a temperatura ambiente. Os resultados mostraram que as ligas 1, 2 e 4 se solidificam pelo modo FA, como esperado. A liga 3 apresentou uma microestrutura correspondente à solidificação eutética/peritética, não sendo apresentada em nenhum dos diagramas existentes na literatura consultada. As medidas da fração de ferrita para todas as ligas foram válidas para os valores propostos pelo diagrama de Schoefer. A análise das micrografias das ligas mostra a variedade de microestruturas que um aço CF-8M pode apresentar apenas alterando sua composição química. Os mapeamentos por EDS/EBSD apresentaram uma maior segregação de elementos de liga entre as fases para as ligas 1, 2 e 4, devido ao modo de solidificação e posterior transformação no estado sólido. A liga 3 apresentou uma menor diferença de composição entre as fases bem como uma menor variação dentro da mesma fase, o que caracteriza uma solidificação eutética/peritética. A comparação entre as regiões distintas das amostras apresentaram variação nas fração e na morfologia da ferrita. O intervalo de solidificação das quatro ligas em estudo ficou dentro de um patamar máximo de 4 °C, esperado para a solidificação com ferrita como primeira fase a se formar a partir do líquido. Foi observada para as ligas com modo de solidificação FA uma transformação de fase no estado sólido no intervalo de 1320°C a 1340 °C, correspondendo a transformação →γ. A energia dessa transformação é inversamente proporcional à quantidade de ferrita observada na microestrutura final das ligas.

This master´s thesis focuses on the basic principles of the nonconventional technology of electrical discharge machining (EDM) with an emphasis on wire-cut electrical discharge machining performed in a mediumsized company. The thesis deals with manufacture of a belt pulley 75-8M-130, and checks of accuracy of wire-cut EDM machines. Following the study of the current status of electrical discharge machining in the engineering company, measures for assurance and increase of accuracy of wire-cut EDM machines are proposed.

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This study investigates how the feminist movement in the city of São Paulo, Brazil, relates to the International Women’s Strike – a transnational feminist mass strike launched by the Argentinian feminist collective Ni Una Menos in 2017. Based on a qualitative analysis of semi-structured interviews conducted with representatives of 10 feminist organizations in São Paulo, this text explores the feasibility of a feminist strike in the context of São Paulo and highlights the structural challenges in its implementation. In addition, the text employs a qualitative literature review to examine the ways in which women in São Paulo have resorted to the strike as an instrument of their resistance since the early 20th century. Theoretically drawing on the theories of direct action and institutionalization of social movements, this work constitutes a synthesis of previous debates and sheds light on the implications that the institutionalization of the Brazilian feminist movement has had on the viability of direct actions such as the feminist strike. A central finding indicates a relative consensus that the feminist movement in São Paulo must first build a massive and popular feminist movement, before an inclusive and intersectional feminist strike can be carried out.

Indiana University-Purdue University Indianapolis (IUPUI) / Convolutional Neural Networks have come a long way since AlexNet. Each year the limits of the state of the art are being pushed to new levels. EfficientNet pushed the performance metrics to a new high and EfficientNetV2 even more so. Even so, architectures for mobile applications can benefit from improved accuracy and reduced model footprint. The classic Inverted Residual block has been the foundation upon which most mobile networks seek to improve. EfficientNet architecture is built using the same Inverted Residual block. In this thesis we experiment with Harmonious Bottlenecks in place of the Inverted Residuals to observe a reduction in the number of parameters and improvement in accuracy. The designed network is then deployed on the NXP i.MX 8M Mini board for Image classification. / 2023-10-11

<p>Convolutional Neural Networks have come a long way since  AlexNet. Each year the limits of the state of the art are being pushed to new  levels. EfficientNet pushed the performance metrics to a new high and EfficientNetV2 even more so. Even so, architectures for mobile applications can benefit from improved accuracy and reduced model footprint. The classic Inverted Residual block has been the foundation upon which most mobile networks seek to improve. EfficientNet architecture is built using the same Inverted Residual block. In this paper we experiment with Harmonious Bottlenecks in  place of the Inverted Residuals to observe a reduction in the number of parameters and improvement in accuracy. The designed network is then deployed on the NXP i.MX 8M Mini board for Image classification.</p>

Ces dernières années, le domaine de l’apprentissage profond a connu des progrès énormes dans des applications allant de la génération d’images, détection d’objets, modélisation du langage à la réponse aux questions visuelles. Les approches classiques telles que l’apprentissage supervisé nécessitent de grandes quantités de données étiquetées et spécifiques à la tâches. Cependant, celles-ci sont parfois coûteuses, peu pratiques, ou trop longues à collecter. La modélisation efficace en données, qui comprend des techniques comme l’apprentissage few-shot (à partir de peu d’exemples) et l’apprentissage self-supervised (auto-supervisé), tentent de remédier au manque de données spécifiques à la tâche en exploitant de grandes quantités de données plus “générales”. Les progrès de l’apprentissage profond, et en particulier de l’apprentissage few-shot, s’appuient sur les benchmarks (suites d’évaluation), les métriques d’évaluation et les jeux de données, car ceux-ci sont utilisés pour tester et départager différentes méthodes sur des tâches précises, et identifier l’état de l’art. Cependant, du fait qu’il s’agit de versions idéalisées de la tâche à résoudre, les benchmarks sont rarement équivalents à la tâche originelle, et peuvent avoir plusieurs limitations qui entravent leur rôle de sélection des directions de recherche les plus prometteuses. De plus, la définition de métriques d’évaluation pertinentes peut être difficile, en particulier dans le cas de sorties structurées et en haute dimension, telles que des images, de l’audio, de la parole ou encore du texte. Cette thèse discute des limites et des perspectives des benchmarks existants, des fonctions de coût (training losses) et des métriques d’évaluation (evaluation metrics), en mettant l’accent sur la modélisation générative - les Réseaux Antagonistes Génératifs (GANs) en particulier - et la modélisation efficace des données, qui comprend l’apprentissage few-shot et self-supervised. La première contribution est une discussion de la tâche de modélisation générative, suivie d’une exploration des propriétés théoriques et empiriques des fonctions de coût des GANs. La deuxième contribution est une discussion sur la limitation des few-shot classification benchmarks, certains ne nécessitant pas de généralisation à de nouvelles sémantiques de classe pour être résolus, et la proposition d’une méthode de base pour les résoudre sans étiquettes en phase de testing. La troisième contribution est une revue sur les méthodes few-shot et self-supervised de détection d’objets , qui souligne les limites et directions de recherche prometteuses. Enfin, la quatrième contribution est une méthode efficace en données pour la description de vidéo qui exploite des jeux de données texte et vidéo non supervisés. / In recent years, the field of deep learning has seen tremendous progress for applications ranging from image generation, object detection, language modeling, to visual question answering. Classic approaches such as supervised learning require large amounts of task-specific and labeled data, which may be too expensive, time-consuming, or impractical to collect. Data-efficient methods, such as few-shot and self-supervised learning, attempt to deal with the limited availability of task-specific data by leveraging large amounts of general data. Progress in deep learning, and in particular, few-shot learning, is largely driven by the relevant benchmarks, evaluation metrics, and datasets. They are used to test and compare different methods on a given task, and determine the state-of-the-art. However, due to being idealized versions of the task to solve, benchmarks are rarely equivalent to the original task, and can have several limitations which hinder their role of identifying the most promising research directions. Moreover, defining meaningful evaluation metrics can be challenging, especially in the case of high-dimensional and structured outputs, such as images, audio, speech, or text. This thesis discusses the limitations and perspectives of existing benchmarks, training losses, and evaluation metrics, with a focus on generative modeling—Generative Adversarial Networks (GANs) in particular—and data-efficient modeling, which includes few-shot and self-supervised learning. The first contribution is a discussion of the generative modeling task, followed by an exploration of theoretical and empirical properties of the GAN loss. The second contribution is a discussion of a limitation of few-shot classification benchmarks, which is that they may not require class semantic generalization to be solved, and the proposal of a baseline method for solving them without test-time labels. The third contribution is a survey of few-shot and self-supervised object detection, which points out the limitations and promising future research for the field. Finally, the fourth contribution is a data-efficient method for video captioning, which leverages unsupervised text and video datasets, and explores several multimodal pretraining strategies.