Uma metodologia para geração otimizada de ambientes virtuais baseados em usinas hidrelétricas

Based on the possibility of real-time interaction with three-dimensional environments through an advanced interface, Virtual Reality consist in the main technology of this work, used in the design of virtual environments based on real Hydroelectric Plants. Previous to the process of deploying a Virtual Reality System for operation, three-dimensional modeling and interactive scenes settings are very importante steps. However, due to its magnitude and complexity, power plants virtual environments generation, currently, presents high computing cost. This work aims to present a methodology to optimize the production process of virtual environments associated with real hydroelectric power plants. In partnership with electric utility CEMIG, several HPPs were used in the scope of this work. During the modeling of each one of them, the techiniques within the methodologie were addressed. After the evaluation of the computional techniques presented here, it was possible to confirm a reduction in the time required to deliver each hydroelectrical complex. Thus, this work presents the current scenario about development of virtual hydroelectric power plants and discusses the proposed methodology that seeks to optimize this process in the electricity generation sector.

Jogos de negócio : o que leva o docente de cursos da área de Ciências Sociais Aplicadas a adotar a ferramenta de ensino

The Business Games are a growing teaching strategy and alternative to the academic front through the new process of Teaching and Learning. Through literature review and semi-structured interviews, this work addresses the teachers considering their subjectivity in deciding JN as the three profiles suggested by Faria and Wellington (2004): those who use it, those who stopped using and those who do not. The research corpus is limited to contributions of 22 respondents between master and doctors teachers of Applied Social Sciences area courses in colleges of Brasília (DF), Goiânia (GO), Ribeirão Preto (SP) and Uberlandia (MG). The content analysis of the interviews allowed to infer that: (1) join the teaching strategy is a commitment to a complex planning, with constant training and proactivity related to student feedback; (2) abandons the practice is becoming less common, because managers tend to recommend it and there are more and more software available for specific disciplines. Its discussed also other contributions (motivations) given by respondents of the three groups that were not found in the literature. It is hoped that this work will serve (1) incentive to teachers on the use of Business Games as a teaching strategy (2) consultation by managers when they decide about purchasing simulation software. Finally, stands out that the educational success of JN depends not only on the various motivations of teachers, as well as the interest and commitment of the student.

Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Uma estratégia para otimizar a geração de arranjos em subestações virtuais de energia elétrica

The Virtual Reality techniques applied in Electricity Environments provide a new supervisory control paradigm. The fact of existing a virtual environment (VE), geometrically similar to a real substation, reduces the difference of mental models built by field operators compared with those built by system center operation improving the communication. Beside this, those systems can be used as visualization interfaces for electricity system simulators, training systems for professors and undergraduate students, field operators and maintenance professionals. However, the development process of these systems is quite complex, combining several activities such as implementation, 3D modeling, virtual sceneries construction, usability assessment and management project techniques. In this context, this work present a GUI strategy to build field arrangements based on scene graphs, to reduce time in Virtual Electricity Substations Arrangement development. Through this, mistakes during the VE building can be avoided making this process more reliable. As an concept proof, all toolkits developed in this work were applied in the virtualization of the substations from a Brazilian power concessionary named CEMIG.