Percorrer por autor "Agulhari, Cristiano"
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- Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic SignalPublication . Bressan, Glaucia; Azevedo, Beatriz Flamia; Santos, Herman Lucas dos; Endo, Wagner; Agulhari, Cristiano; Goedtel, Alessandro; Scalassara, PauloConsidering the classification of failures in electrical machines, the present paper aims to use supervised machine learning techniques in order to classify faults in electrical machines, using attributes from audio signals. In order to analyze data and recognize patterns, the considered supervised learning methods are: Bayesian Network, together with the BayesRule algorithm, Support Vector Machine and k-Nearest Neighbor. The performances and the results provided from these algorithms are then compared. The main contributions of this paper are the acquisition process of audio signals and the elaboration of Bayesian networks topologies and classifiers structures using the acquired signals, since the algorithms provide the generalization of the classification model by revealing the network structure. Also, the utilization of audio signals as input attributes to the classifiers is infrequent in the literature. The results show that the Support Vector Machine and k-Nearest Neighbor present a high accuracy. On the other hand, the Bayesian approach is advantageous due to the possibility of showing, through graph representations, the generalized structure to represent the trend of faults in real cases on industry applications.
- Titanium based dry electrodes for biostimulation and data acquisitionPublication . Conselheiro, Raul; Lopes, Claudia; Azevedo, Nelson; Leitao, Paulo; Agulhari, Cristiano; Veloso, H.; Vaz, F.; Gonçalves, João Lucas; Franco, Tiago; Oliveira, Leonardo Sestrem deWet electrodes rely on conductive electrolyte gel for proper perfor- mance, usually presenting high setup time and being disposable or requiring time-consuming cleaning methods. Skin irritation and signal quality deterioration are some of the problems that may occur with these electrodes. Therefore, to overcome these drawbacks, 3D printed bases using Fused Deposition Modelling (FDM) with Polylactic acid (PLA), Polyurethane (PU) and Cellulose filaments were functionalized with titanium (Ti) and titanium-nitride (TiN) thin films and dopped with copper (Cu). The electrodes, implemented using different diameters, were used to record electromyography (EMG) signals proceeded by biostimula- tion sessions to access their electrical and mechanical characteristics and com- pare them to that of commercial AgCl/Ag electrodes. The results show that TiN and TiNCu0.45 dry electrodes present results similar to wet AgCl/Ag electrodes for data acquisition. To be comparable to usual carbon electrodes for muscle stimulation, they need some conductivity improvements to lower the necessary voltage. Besides, the endurance of the thin films must be enhanced as well as the adhesion to the polymer.