Modelagem e análise dinâmica de um sistema de conversão de energia eólica dotado de gerador síncrono de imã permanente utilizando a plataforma ATP

Human development requires a broad balance between ecological, social and economic factors in order to ensure its own sustainability. In this sense, the search for new sources of energy generation, with low deployment and operation costs, which cause the least possible impact to the environment, has been the focus of attention of all society segments. To do so, the reduction in exploration of fossil fuels and the encouragement of using renewable energy resources for distributed generation have proved interesting alternatives to the expansion of the energy matrix of various countries in the world. In this sense, the wind energy has acquired an increasingly significant role, presenting increasing rates of power grid penetration and highlighting technological innovations such as the use of permanent magnet synchronous generators (PMSG). In Brazil, this fact has also been noted and, as a result, the impact of the inclusion of this source in the distribution and sub-transmission power grid has been a major concern of utilities and agents connected to Brazilian electrical sector. Thus, it is relevant the development of appropriate computational tools that allow detailed predictive studies about the dynamic behavior of wind farms, either operating with isolated load, either connected to the main grid, taking also into account the implementation of control strategies for active/reactive power generation and the keeping of adequate levels of voltage and frequency. This work fits in this context since it comprises mathematical and computational developments of a complete wind energy conversion system (WECS) endowed with PMSG using time domain techniques of Alternative Transients Program (ATP), which prides itself a recognized reputation by scientific and academic communities as well as by electricity professionals in Brazil and elsewhere. The modeling procedures performed allowed the elaboration of blocks representing each of the elements of a real WECS, comprising the primary source (the wind), the wind turbine, the PMSG, the frequency converter, the step up transformer, the load composition and the power grid equivalent. Special attention is also given to the implementation of wind turbine control techniques, mainly the pitch control responsible for keeping the generator under the maximum power operation point, and the vector theory that aims at adjusting the active/reactive power flow between the wind turbine and the power grid. Several simulations are performed to investigate the dynamic behavior of the wind farm when subjected to different operating conditions and/or on the occurrence of wind intensity variations. The results have shown the effectiveness of both mathematical and computational modeling developed for the wind turbine and the associated controls.

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.

Uma abordagem baseada em filtragem colaborativa integrada a mapas de saliência para a recomendação de imagens

Nowadays, the amount of customers using sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. The sites, differently from physical stores, can make anything available to customers. In this context, Recommender Systems (RS) have become indispensable to help consumers to find products that may possibly pleasant or be useful to them. These systems often use techniques of Collaborating Filtering (CF), whose main underlying idea is that products are recommended to a given user based on purchase information and evaluations of past, by a group of users similar to the user who is requesting recommendation. One of the main challenges faced by such a technique is the need of the user to provide some information about her preferences on products in order to get further recommendations from the system. When there are items that do not have ratings or that possess quite few ratings available, the recommender system performs poorly. This problem is known as new item cold-start. In this paper, we propose to investigate in what extent information on visual attention can help to produce more accurate recommendation models. We present a new CF strategy, called IKB-MS, that uses visual attention to characterize images and alleviate the new item cold-start problem. In order to validate this strategy, we created a clothing image database and we use three algorithms well known for the extraction of visual attention these images. An extensive set of experiments shows that our approach is efficient and outperforms state-of-the-art CF RS.