André Eugênio Lazzaretti

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—This paper presents a method for automatic classification of faults and events related to quality of service in electricity distribution networks. The method consists in preprocessing event oscillographies using the wavelet transform and then classifying them using autonomous neural models. In the preprocessing stage, the energy present in each sub-band of(More)
—This paper presents a new approach for automatic oscillography classification in distribution networks, including the detection of patterns not initially presented to the classifier during training, which are defined as novelties. We performed experiments with coupled novelty detection and multi-class classification , and also in separate stages, using the(More)
—This paper presents some new results for a fundamental step in automatic oscillography analysis: transient detection. We performed experiments with usual detection methods, such as the Kalman filter (KF) and autoregressive (AR) models, and we are proposing a new method based on the Discrete Wavelet Transform (DWT) and Support Vector Data Description(More)
This paper proposes a new approach to motion segmen-tation from video sequences acquired using a single camera , whose aim is to identify which components are due to pure egomotion and which components are due to independent moving objects within the observed motion field. The model has three main steps, namely computation of the optical flow field,(More)
The identification of non-linear systems by artificial neural networks has been successfully applied in many applications. In this context, the radial basis function neural network (RBF-NN) is a powerful approach for non-linear system identification. An RBF neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden(More)
The identification of nonlinear systems by artificial neural networks has been successfully applied in many applications. In this context, the radial basis function neural network (RBF-NN) is a powerful approach for nonlinear identification. A RBF neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden layer contain(More)
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