892 resultados para Dimensional Modeling and Virtual Reality


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Objective. To evaluate the influence of previous adaptation to different computational environments in visuo-spacial tasks performance of healthy individuals. Method. Healthy volunteers (n = 30), 15 male, mean age 25.3 ± 3.3 years, were divided in three groups: the first group, considered control, was not adapted to the proposed environments; the second group was adapted to a closed environment (stable and expected), and the third group was adapted to an open environment A (unexpected). The proposed task was to go through two open environments B and C (maze). The dependent variables Time and Error were considered for the analysis. Results. It was observed that during the adaptation phase, in the Time variable, the groups presented a progressive improvement in the performance to each task (p = 0.0036). The group adapted in the A open environment, showed a tendency to be faster in the execution of B and C open environments tasks, than the group adapted in the closed environment (p = 0.068). Conclusion. The study suggests that subjects adapted to visuo-spacial tasks execution involving unknown and no guided situations, present a tendency to a better time performance in these tasks when compared to subjects adapted in fixed and guided situations.

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This paper presents a general modeling approach to investigate and to predict measurement errors in active energy meters both induction and electronic types. The measurement error modeling is based on Generalized Additive Model (GAM), Ridge Regression method and experimental results of meter provided by a measurement system. The measurement system provides a database of 26 pairs of test waveforms captured in a real electrical distribution system, with different load characteristics (industrial, commercial, agricultural, and residential), covering different harmonic distortions, and balanced and unbalanced voltage conditions. In order to illustrate the proposed approach, the measurement error models are discussed and several results, which are derived from experimental tests, are presented in the form of three-dimensional graphs, and generalized as error equations. © 2009 IEEE.

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This paper presents the work in progress of an on-demand software deployment system based on application virtualization concepts which eliminates the need of software installation and configuration on each computer. Some mechanisms were created, such as mapping of utilization of resources by the application to improve the software distribution and startup; a virtualization middleware which give all resources needed for the software execution; an asynchronous P2P transport used to optimizing distribution on the network; and off-line support where the user can execute the application even when the server is not available or when is out of the network. © Springer-Verlag Berlin Heidelberg 2010.

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This paper explores the benefits of using immersive and interactive virtual reality environments to teach Dentistry. We present a tool for educators to manipulate and edit virtual models. One of the main contributions is that multimedia information can be semantically associated with parts of the model, through an ontology, enriching the experience; for example, videos can be linked to each tooth demonstrating how to extract them. The use of semantic information gives a greater flexibility to the models, since filters can be applied to create temporary models that show subsets of the original data in a human friendly way. We also explain how the software was written to run in arbitrary multi-projection environments. © 2011 Springer-Verlag.

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Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

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Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

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The Finite Element Method is a well-known technique, being extensively applied in different areas. Studies using the Finite Element Method (FEM) are targeted to improve cardiac ablation procedures. For such simulations, the finite element meshes should consider the size and histological features of the target structures. However, it is possible to verify that some methods or tools used to generate meshes of human body structures are still limited, due to nondetailed models, nontrivial preprocessing, or mainly limitation in the use condition. In this paper, alternatives are demonstrated to solid modeling and automatic generation of highly refined tetrahedral meshes, with quality compatible with other studies focused on mesh generation. The innovations presented here are strategies to integrate Open Source Software (OSS). The chosen techniques and strategies are presented and discussed, considering cardiac structures as a first application context. © 2013 E. Pavarino et al.

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The superoxide dismutase (TfSOD) gene from the extremely thermophilic bacterium Thermus filiformis was cloned and expressed at high levels in mesophilic host. The purified enzyme displayed approximately 25 kDa band in the SDS-PAGE, which was further confirmed as TfSOD by mass spectrometry. The TfSOD was characterized as a cambialistic enzyme once it had enzymatic activity with either manganese or iron as cofactor. TfSOD showed thermostability at 65, 70 and 80°C. The amount of enzyme required to inhibit 50% of pyrogallol autoxidation was 0·41, 0·56 and 13·73 mg at 65, 70 and 80°C, respectively. According to the circular dichroism (CD) spectra data, the secondary structure was progressively lost after increasing the temperature above 70°C. The 3-dimensional model of TfSOD with the predicted cofactor binding corroborated with functional and CD analysis. © 2013 The Society for Applied Microbiology.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Educação Matemática - IGCE

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Desenvolvimento Humano e Tecnologias - IBRC

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Pós-graduação em Desenvolvimento Humano e Tecnologias - IBRC

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O padrão IEEE 802.16, também chamado de WiMAX, é uma tecnologia da rede banda larga sem fio para áreas metropolitanas, utilizado como alternativa para transmissão de sinal de Internet a regiões que não possuem infraestrutura de rede cabeada. Atualmente, o ensino desta tecnologia em sala de aula é meramente teórico, o que dificulta a compreensão dos alunos com relação a determinadas funcionalidades do WiMAX. Nesse sentido, a presente dissertação aborda o projeto de desenvolvimento de um simulador em Realidade Virtual, chamado SwImax, voltado para auxílio do ensino do padrão IEEE 802.16. Assim, o SwImax simula algumas características do funcionamento deste padrão, quais sejam: faixas de frequência de operação, área de cobertura, procedimento de handover, transmissão sem linha de visada, entre outros. A dissertação apresenta os trabalhos correlatos que influenciaram o desenvolvimento do projeto, além de um resumo acerca do padrão IEEE 802.16. O texto apresenta também as ferramentas utilizadas no desenvolvimento do SwImax e a implementação do simulador. Ao final do desenvolvimento, o software foi submetido a uma avaliação dos usuários, de modo que os resultados também são abordados nesta dissertação.