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An automated classification based on S-transform as feature extraction tool and Artificial Neural Network as algorithm classifier is presented. The signals generated according to mathematical models have been used to obtain experimental results in two stages, first, with a data set with only simple disturbances and, later, including complex disturbances, more usual in real electrical systems. In both...
An automated classification system based on Wavelet transform as a feature extraction tool in combination with Artificial Neural Network as algorithm classifier is presented. Perturbed signals generated according to mathematical models have been used to obtain experimental results in two stages, first, with a data set with simple disturbances and, later, including complex disturbances, more usual...
A novel multidisciplinary system for the automatic driving risk level classification is presented. The data considered involves the three basic traffic safety elements (driver, road, and vehicle), as well as knowledge from traffic experts. The driving experiments were conducted in a truck cabin simulator handled by a professional driver, considering the most common real-world enviroments. Each traffic...
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