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- Low-Cost CNN for Automatic Violence Recognition on Embedded SystemPublication . Vieira, J. C.; Sartori, Andreza; Stefenon, Stéfano Frizzo; Perez, Fabio Luis; Schneider De Jesus, Gabriel; LEITHARDT, VALDERIDue to the increasing number of violence cases, there is a high demand for efficient monitoring systems, however, these systems can be susceptible to failure. Therefore, this work proposes the analysis and application of low-cost Convolutional Neural Networks (CNNs) techniques to automatically recognize and classify suspicious events. Thus, it is possible to alert and assist the monitoring process with a reduced deployment cost. For this purpose, a dataset with violence and non-violence actions in scenes of crowded and non-crowded environments was assembled. The mobile CNNs architectures were adapted and obtained a classification accuracy of up to 92.05%, with a low number of parameters. To demonstrate the models validity, a prototype was developed by using an embedded Raspberry Pi platform, able to execute a model in real-time with 4 frames-per-second of speed. In addition, a warning system was developed to recognize pre-fight behavior and anticipate violent acts, alerting security to potential situations.
- Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning MethodsPublication . Stefenon, Stefano Frizzo; Bruns, Rafael; Sartori, Andreza; Meyer, Luiz Henrique; Garcia, Raul; LEITHARDT, VALDERIOutdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR).