952 resultados para Circuito piezelétrico semi-passivo


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This study at aims performing the stability analysis of the rotational motion to artificial satellites using quaternions to describe the satellite attitude (orientation on the space). In the system of rotational motion equations, which is composed by four kinematic equations of the quaternions and by the three Euler equations in terms of the rotational spin components. The influence of the gravity gradient and the direct solar radiation pressure torques have been considered. Equilibrium points were obtained through numerical simulations using the softwares Matlab and Octave, which are then analyzed by the Routh-Hurwitz Stability Criterion.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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In this paper we study the behavior of a semi-active suspension witch external vibrations. The mathematical model is proposed coupled to a magneto rheological (MR) damper. The goal of this work is stabilize of the external vibration that affect the comfort and durability an vehicle, to control these vibrations we propose the combination of two control strategies, the optimal linear control and the magneto rheological (MR) damper. The optimal linear control is a linear feedback control problem for nonlinear systems, under the optimal control theory viewpoint We also developed the optimal linear control design with the scope in to reducing the external vibrating of the nonlinear systems in a stable point. Here, we discuss the conditions that allow us to the linear optimal control for this kind of non-linear system.

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The present study aimed to identify Eimeria species in young and adult sheep raised under intensive and / or semi-intensive systems of a herd from Umuarama city, Parana State, Brazil using the traditional diagnostic methods and to correlate the infection level/types of infection in the different age/system in this herd. Fecal samples were collected from the rectum of 210 sheep and were subjected to laboratory analysis to differentiate the species. Furthermore, animals were observed to determine the occurrences of the clinical or subclinical forms of eimeriosis. Out of the 210 collected fecal samples, 147 (70%) were positive for Eimeria oocysts, and 101 (47.86%) belonged to young animals that were raised under intensive and / or semi-intensive farming systems. Oocysts from 9 species of Eimeria parasites were identified in the sheep at the following prevalence rates: E. crandallis, 50.0%; E. parva, 21.6%; E. faurei, 8.1%; E. ahsata, 8.1%; E. intricata, 5.4%; E. granulosa, 2.7%; E. ovinoidalis, 2.0%; E. ovina, 1.3%; and E. bakuensis, 0.6%. There were no differences regarding the more frequent Eimeria species among the different ages of animals or between the different farming management systems. Based on these data, E. crandallis was the most prevalent, followed by E. parva and E. faurei species, regardless of the age. Higher parasitism was diagnosed in the young animals that were raised in a confinement regime, and the disease found in the herd was classified as subclinical. Further studies should be conducted in this herd, to verify if the eimeriosis subclinical can cause damage especially in young animals with a high level of infection.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Pós-graduação em Engenharia Mecânica - FEIS

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Pós-graduação em Engenharia Mecânica - FEG

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

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Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)