The main objective of this research is to investigate the effect of wide peening and cleaning (WPC) also known as fine particle peening on the surface properties and cutting performance of cemented carbide inserts. In WPC, the surface of the material is bombarded with millions of high-velocity fine shot generating a uniform layer of plastic deformation near the surface. The plastically deformed layer will have higher compressive residual stress levels, higher surface hardness, experience changes in surface morphology and changes in microstructure. Selecting suitable peening parameters is crucial for achieving proper results. In this study, tools are treated under different pressures varying between 0.2 to 0.4 MPa, and for different peening durations of 2.5 to 10 s. The cutting performance of uncoated tools treated with WPC was examined while turning ductile cast iron and AISI 4140. To have a better understanding, the surface morphology, microstructure, surface roughness, cutting edge radius, residual stresses, and surface hardness were measured and discussed. The results are also compared with untreated tools. The compressive residual stresses were significantly higher after WPC. In addition, uncoated tools treated with WPC resulted in a 12-30% higher tool life over untreated tools. Based on the findings outlined in this thesis, WPC can be recommended as a surface treatment on uncoated cemented carbide inserts for increasing tool life. Also, this study shows significant potential for using WPC as a pre-coating treatment for improving coating adhesion on cemented carbide cutting inserts. / Thesis / Master of Applied Science (MASc)

Shooting ranges concentrate significant quantities of heavy metals, especially lead as spent shot and bullets, on very small parcels of land. Samples taken from a shooting range near Blacksburg, VA, USA provide information about the reservoirs and pathways of lead at shooting ranges in an upland setting and humid environment. Metallic lead corrodes rapidly and develops a coating of corrosion products. The type and amount of corrosion products found on lead shot and bullets are best understood through examination of Eh-pH relationships. X-ray diffraction analysis identified hydrocerussite (Pb₃(CO₃)₂(OH)₂) as the corrosion phase present on lead shot recovered from the range. Hydrocerussite dissolution can produce soluble lead concentrations ranging from 2 ppb to 2 ppm for the soil pH values at this site. This soluble lead is captured by the soil. Sequential chemical extractions revealed that vertical lead migration beyond the A-horizon was minimal. The bound-to-Fe & Mn oxides and bound-to-carbonates soil fractions were identified as significant reservoirs for sequestration of lead in the soil. The highest concentration of extractable lead contained in the soil was directly correlated with the highest concentration of lead shot and bullets measured on the shotgun range surface. The geochemical framework for understanding the corrosion process, identifying the corrosion product(s) that control lead solubility, and identifying the geochemical barriers to lead migration that were employed at the Blacksburg, VA shotgun range, provides a basis for selecting best management practices for this and other shooting ranges. / Master of Science

Recent advances in large neural network-style models have demonstrated great performance in various applications, such as image generation, question answering, and audio classification. However, these deep and high-capacity models require a large amount of labeled data to function properly, rendering them inapplicable in many real-world scenarios. This dissertation focuses on the development and evaluation of advanced machine learning algorithms to solve the following research questions: (1) How to learn novel classes with limited labeled data, (2) How to adapt a large pre-trained model to the target domain if only unlabeled data is available, (3) How to boost the performance of the few-shot learning model with unlabeled data, and (4) How to utilize limited labeled data to learn new classes without the training data in the same domain. First, we study few-shot learning in text classification tasks. Meta-learning is becoming a popular approach for addressing few-shot text classification and has achieved state-of-the-art performance. However, the performance of existing approaches heavily depends on the interclass variance of the support set. To address this problem, we propose a TART network for few-shot text classification. The model enhances the generalization by transforming the class prototypes to per-class fixed reference points in task-adaptive metric spaces. In addition, we design a novel discriminative reference regularization to maximize divergence between transformed prototypes in task-adaptive metric spaces to improve performance further. In the second problem we focus on self-learning in cross-lingual transfer task. Our goal here is to develop a framework that can make the pretrained cross-lingual model continue learning the knowledge with large amount of unlabeled data. Existing self-learning methods in crosslingual transfer tasks suffer from the large number of incorrectly pseudo-labeled samples used in the training phase. We first design an uncertainty-aware cross-lingual transfer framework with pseudo-partial-labels. We also propose a novel pseudo-partial-label estimation method that considers prediction confidences and the limitation to the number of candidate classes. Next, to boost the performance of the few-shot learning model with unlabeled data, we propose a semi-supervised approach for few-shot semantic segmentation task. Existing solutions for few-shot semantic segmentation cannot easily be applied to utilize image-level weak annotations. We propose a class-prototype augmentation method to enrich the prototype representation by utilizing a few image-level annotations, achieving superior performance in one-/multi-way and weak annotation settings. We also design a robust strategy with softmasked average pooling to handle the noise in image-level annotations, which considers the prediction uncertainty and employs the task-specific threshold to mask the distraction. Finally, we study the cross-domain few-shot learning in the semantic segmentation task. Most existing few-shot segmentation methods consider a setting where base classes are drawn from the same domain as the new classes. Nevertheless, gathering enough training data for meta-learning is either unattainable or impractical in many applications. We extend few-shot semantic segmentation to a new task, called Cross-Domain Few-Shot Semantic Segmentation (CD-FSS), which aims to generalize the meta-knowledge from domains with sufficient training labels to low-resource domains. Then, we establish a new benchmark for the CD-FSS task and evaluate both representative few-shot segmentation methods and transfer learning based methods on the proposed benchmark. We then propose a novel Pyramid-AnchorTransformation based few-shot segmentation network (PATNet), in which domain-specific features are transformed into domain-agnostic ones for downstream segmentation modules to fast adapt to unseen domains. / Doctor of Philosophy / Nowadays, deep learning techniques play a crucial role in our everyday existence. In addition, they are crucial to the success of many e-commerce and local businesses for enhancing data analytics and decision-making. Notable applications include intelligent transportation, intelligent healthcare, the generation of natural language, and intrusion detection, among others. To achieve reasonable performance on a new task, these deep and high-capacity models require thousands of labeled examples, which increases the data collection effort and computation costs associated with training a model. Moreover, in many disciplines, it might be difficult or even impossible to obtain data due to concerns such as privacy and safety. This dissertation focuses on learning with limited labeled data in natural language processing and computer vision tasks. To recognize novel classes with a few examples in text classification tasks, we develop a deep learning-based model that can capture both cross- task transferable knowledge and task-specific features. We also build an uncertainty-aware self-learning framework and a semi-supervised few-shot learning method, which allow us to boost the pre-trained model with easily accessible unlabeled data. In addition, we propose a cross-domain few-shot semantic segmentation method to generalize the model to different domains with a few examples. By handling these unique challenges in learning with limited labeled data and developing suitable approaches, we hope to improve the efficiency and generalization of deep learning methods in the real world.

Many real world datasets are characterized by having a long tailed distribution, with several samples for some classes and only a few samples for other classes. While many Deep Learning based solutions exist for object recognition when hundreds of samples are available, there are not many solutions for the case when there are only a few samples available per class. Recognition in the regime where the number of training samples available for each class are low, ranging from 1 to couple of tens of examples is called Lowshot Recognition. In this work, we attempt to solve this problem. Our framework is similar to [1]. We use a related dataset with sufficient number (a couple of hundred) of samples per class to learn representations using a Convolutional Neural Network (CNN). This CNN is used to extract features of the lowshot samples and learn a classifier . During representation learning, we enforce the learnt representations to obey certain property by using a custom loss function. We believe that when the lowshot sample obey this property the classification step becomes easier. We show that the proposed solution performs better than the softmax classifier by a good margin. / Master of Science / Deep learning, a branch of Artificial Intelligence(AI) is revolutionizing the way computers can learn and perform artificial intelligence tasks. The power of Deep Learning comes from being able to model very complex functions using huge amounts of data. For this reason, deep learning is criticized as being data hungry. Although AI systems are able to beat humans in many tasks, unlike humans, they still lack the ability to learn from less data. In this work, we address the problem of teaching AI systems with only a few examples, formally called the “low-shot learning”. We focus on low-shot visual recognition where the AI systems are taught to recognize different objects from images using very few examples. Solving the low-shot recognition problem will enable us to apply AI based methods to many real world tasks. Particularly in the cases where we cannot afford to collect huge number of images because it is either costly or it is impossible. We propose a novel technique to solve this problem. We show that our solution performs better at low-shot recognition than the regular image classification solution, the softmax classifier.

O tratamento superficial de shot peening tem por objetivo aumentar a resistência à fadiga sendo comparada pelas medidas de tensão residual. O processo peen forming é uma variante do processo shot peening onde se obtém uma curvatura na placa produzida pelo jateamento das esferas através da compressão dos grãos localizados próximos à superfície. Foi estudado neste trabalho a influência dos parâmetros pressão e tamanho de granalha, utilizado no processo de peen forming, no perfil de tensão residual e no raio de curvatura em amostras de alumínio 7050. A avaliação do perfil de tensão de residual foi efetuada por difração de raios-x utilizando o método de sen2 . Os resultados mostram que a formação da altura do arco de curvatura é proporcional a pressão de jateamento e ao tamanho das esferas e inversamente proporcional a espessura da amostra, e que o fator de concentração de tensões é maior para amostras jateadas com menores esferas. Na seção final deste trabalho apresenta um estudo complementar sobre microdeformação e tamanho médio de cristalito, podendo avaliar o perfil das amostras após jateamento. / Shot peening is a superficial cold work process used to increase the fatigue life evaluated by residual stress measurements. The peen forming process is a variant of the shot peening process, where a curvature in the plate is obtained by the compression of the grains near to the surface. In this paper, the influence of the parameters such as: pressure of shot, ball shot size and thickness of aluminum 7050 samples with respect to residual stress profile and resulting arc height was studied. The evaluation of the residual stress profile was obtained by sin2 method. The results show that the formation of the curvature arc height is proportional to the shot peening pressure, of spheres size and inversely proportional to the thickness of the sample, and that stress concentration factor is larger for samples shot peened with small balls. On final of this paper presents an additional study on microstrain and average crystallite size, which can evaluate the profile of the samples after blasting.

Atualmente, quando se tem a necessidade de nitretar peças produzidas pela metalurgia do pó, usa-se a nitretação a plasma. Apesar de ser um processo de alto custo, com diversas dificuldades operacionais e de ajuste de processo, a nitretação a plasma é o único processo viável para nitretar esses materiais por ter uma ação apenas superficial, não apresentando ação nitretante no interior dos poros. Nos processos de nitretação a gás e banho de sais, o meio nitretante penetra na porosidade (interconectada) dos materiais sinterizados, havendo assim a formação de camada branca em uma grande profundidade da peça (ou mesmo na peça toda), gerando problemas de deformação e fragilização do componente. Este trabalho teve por objetivo a aplicação do processo shot peening em peças sinterizadas com a finalidade de fechar a porosidade superficial das peças e estudar seu comportamento sob o processo de nitretação a gás. O estudo verifica que o material sinterizado submetido à nitretação gasosa permitiu a entrada do meio nitretante pelos poros abertos e interconectados promovendo a formação de camada branca no interior dos poros de praticamente todo o volume da peça. Essa camada branca no interior do material fragiliza o componente e inviabiliza sua utilização como componente em praticamente qualquer aplicação industrial. As peças sinterizadas jateadas com granalhas de aço antes da nitretação também permitiram o acesso do meio nitretante no interior do componente, porém, sem potencial suficiente para a formação de camada branca. As amostras jateadas apresentaram apenas agulhas de nitretos formados durante a nitretação. / Plasma nitriding is the process used to nitriding components produced by powder metallurgy. Although its high coast and operational difficulties, this is the best process for this kind of materials because the nitring occurs only in the surface. In gas and liquid nitriding processes the nitriding atmosphere goes through interconnected porous and the white layer forms not only on the surface but around internal porous resulting in embrittlement and deformation of the component. The aim of this work was evaluate the gas nitriding behavior of iron samples as received and previously submitted to shot peening process in order to close superficial porosities in a gas nitriding process. The results have shown that during gas nitriding the samples as received, did not present white layer at the surface but around the porous in the bulk of the sample. This fact suggested that the gaseous atmosphere goes through interconnected porous. This white layer causes the embrittlement of the component and its use in industrial application is not recommended. Otherwise, samples previously submitted to shot peening process before nitriding showed an external white layer but also permitted the access of nitriding atmosphere to the bulk of the sample, but in this case the nitriding potential was not sufficient to form white layer around internal porous.

Nesta dissertação, fazemos a primeira investigação sobre flutuações em corrente e corrente média dependentes de spin em potenciais duplo e simples da estrutura Zn1-xMnxSe. Consideramos efeitos de campos magnético e elétrico externos à temperatura nula. Na presença de um campo magnético, a interação dos íons de Mn com elétrons de condução e valência (interação de troca sp-d) origina potenciais dependentes de spin para o transporte em Zn1-xMnxSe. Aqui, flutuações em corrente (\"shot noise\") e a corrente média são calculados usando o modelo quântico de transporte através do potencial dependente de spin é descrito por uma matriz s de espalhamento. Os elementos da matriz de espalhamento, i.e., as amplitudes de transmissão e reflexão, são determinados pelo método da matriz transferência. Nossos resultados indicam que estruturas de potenciais simples e duplos Zn1-xMnxSe agem como se fossem \"filtros de spin\" para corrente. Em determinadas faixas de parâmetros do sistema, \"shot noise\" pode complementar informações obtidas da corrente média / In this dissertation we investigation for the first time spin dependent-current and its fluctuations in double and single barrier potentials of the Zn1-xMn xSe structure sandwiched between ZnSe layers. We consider effects of external magnetic field, the interaction of the Mn ions with thew conduction and valence electrons (sp-d exchange interation) give rises to spin-dependent potentials for transport across the Zn1-xMn xSe layer. Here, the average current and its fluctuations are calculated using the quantum transport model in which transport across the spin-dependent potential is described via scattering matrix s. The elements of the scattering matrix, i.e., the transmission and reflection amplitudes, are determined through the transfer-matrix method. Our results indicate date single and double potentials of the Zn1-xMn xSe structure act as \"spin filters\" for the current. Within some system parameter range, shot noise can supplement the information contained in the average current

In this thesis, a mixed type video segmentation algorithm is implemented to find the scene cuts in MPEG compressed video data. The main aim is to have a computationally efficient algorithm for real time applications. Due to this reason partial decoding of the bitstream is used in segmentation. As a result of partial decoding, features such as bitrate, motion vector type, and DC images are implemented to find both continuous and discontinuous scene cuts on a MPEG-2 coded general TV broadcast data. The results are also compared with techniques found in literature.

Nesta dissertação, fazemos a primeira investigação sobre flutuações em corrente e corrente média dependentes de spin em potenciais duplo e simples da estrutura Zn1-xMnxSe. Consideramos efeitos de campos magnético e elétrico externos à temperatura nula. Na presença de um campo magnético, a interação dos íons de Mn com elétrons de condução e valência (interação de troca sp-d) origina potenciais dependentes de spin para o transporte em Zn1-xMnxSe. Aqui, flutuações em corrente (\"shot noise\") e a corrente média são calculados usando o modelo quântico de transporte através do potencial dependente de spin é descrito por uma matriz s de espalhamento. Os elementos da matriz de espalhamento, i.e., as amplitudes de transmissão e reflexão, são determinados pelo método da matriz transferência. Nossos resultados indicam que estruturas de potenciais simples e duplos Zn1-xMnxSe agem como se fossem \"filtros de spin\" para corrente. Em determinadas faixas de parâmetros do sistema, \"shot noise\" pode complementar informações obtidas da corrente média / In this dissertation we investigation for the first time spin dependent-current and its fluctuations in double and single barrier potentials of the Zn1-xMn xSe structure sandwiched between ZnSe layers. We consider effects of external magnetic field, the interaction of the Mn ions with thew conduction and valence electrons (sp-d exchange interation) give rises to spin-dependent potentials for transport across the Zn1-xMn xSe layer. Here, the average current and its fluctuations are calculated using the quantum transport model in which transport across the spin-dependent potential is described via scattering matrix s. The elements of the scattering matrix, i.e., the transmission and reflection amplitudes, are determined through the transfer-matrix method. Our results indicate date single and double potentials of the Zn1-xMn xSe structure act as \"spin filters\" for the current. Within some system parameter range, shot noise can supplement the information contained in the average current

O tratamento superficial de shot peening tem por objetivo aumentar a resistência à fadiga sendo comparada pelas medidas de tensão residual. O processo peen forming é uma variante do processo shot peening onde se obtém uma curvatura na placa produzida pelo jateamento das esferas através da compressão dos grãos localizados próximos à superfície. Foi estudado neste trabalho a influência dos parâmetros pressão e tamanho de granalha, utilizado no processo de peen forming, no perfil de tensão residual e no raio de curvatura em amostras de alumínio 7050. A avaliação do perfil de tensão de residual foi efetuada por difração de raios-x utilizando o método de sen2 . Os resultados mostram que a formação da altura do arco de curvatura é proporcional a pressão de jateamento e ao tamanho das esferas e inversamente proporcional a espessura da amostra, e que o fator de concentração de tensões é maior para amostras jateadas com menores esferas. Na seção final deste trabalho apresenta um estudo complementar sobre microdeformação e tamanho médio de cristalito, podendo avaliar o perfil das amostras após jateamento. / Shot peening is a superficial cold work process used to increase the fatigue life evaluated by residual stress measurements. The peen forming process is a variant of the shot peening process, where a curvature in the plate is obtained by the compression of the grains near to the surface. In this paper, the influence of the parameters such as: pressure of shot, ball shot size and thickness of aluminum 7050 samples with respect to residual stress profile and resulting arc height was studied. The evaluation of the residual stress profile was obtained by sin2 method. The results show that the formation of the curvature arc height is proportional to the shot peening pressure, of spheres size and inversely proportional to the thickness of the sample, and that stress concentration factor is larger for samples shot peened with small balls. On final of this paper presents an additional study on microstrain and average crystallite size, which can evaluate the profile of the samples after blasting.