Navegando por Assunto "Rede neural convolucional"

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Acesso aberto (Open Access)
Classificação de arritmias cardíacas através de uma estrutura competitiva de redes neurais convolucionais autoassociativas
(Universidade Federal do Pará, 2023-05-11) CORRÊA FILHO, Sérgio Teixeira; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
This work proposes a system for classifying cardiac arrhythmias based on a competitive structure of Autoassociative Convolutional Neural Networks. Three neural networks were trained to reconstruct Electrocardiogram (ECG) signals for cases of patients with supraventricular, ventricular and normal beats. After training, the networks were allocated in a competitive parallel structure for classification of arrhythmias. The MIT-BIH arrhythmia public database of ECG signals was used for training and testing the networks, and for each ECG signal, from each patient, the QRS complexes of the heartbeats were extracted, which were the characteristics used as input. for the system, and these signals, which were in the form of temporal signals (1D), were transformed into digital images (2D) in order to use the capacity of convolutional neural networks for pattern recognition and feature extraction in images. For the development and performance analysis of the proposed structure, two paradigms that have been used in works already presented in the literature were used: interpatient paradigm and intrapatient paradigm, and the system obtained an accuracy of 96.97%, sensitivity of 96.30% and precision of 93.59% for the intrapatient case and accuracy of 94.05%, sensitivity of 70.43% and precision of 65.74% for the interpatient case. A comparative analysis with results from arrhythmia classification systems already presented in the literature shows that the proposed system presented similar results or, in some cases, better results than those already obtained, thus showing the applicability of the proposed structure to the problem
Acesso aberto (Open Access)
Estrutura competitiva de redes neurais convolucionais auto-associativas para classificação de arritmias
(Universidade Federal do Pará, 2019-04-17) BAIA, Alexandre Farias; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
This work presents the proposal of two automatic systems to aid in the detection of anomalies in heart beats and medical decision support. The systems were developed for the identification of rhythmic arrhythmia and morphological arrhythmias from signals obtained from an electrocardiogram (ECG). Both systems are based on a competitive structure of Convolutional Autoencoders (CAE), and each network was trained to reconstruct the signals presented at its entrance. For the case of the rhythmic classifier, the system was developed from the use of the ECG signals, without undergoing a feature extraction process, and for the case of the morphological classifier, the system was based on the QRS complex extracted from the ECG signal. For the development and testing of the systems, the database MIT-BIH Arrhythmia of ECG signals was used. An accuracy of 88.9% was achieved for the Rhythmic Classifier and 81.73% for the Morphological Classifier, in the case in which the evaluation basis is considered. The results obtained demonstrate the applicability of the proposed competitive structures to the arrhythmia classification problem.
Acesso aberto (Open Access)
Redes neurais profundas aplicadas ao diagnóstico de faltas incipientes em transformadores imersos em óleo isolante.
(Universidade Federal do Pará, 2019-09-11) MORAES, Hugo Riviere Silva; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
Diagnosing incipient faults in transformers is a major challenge because it is very difficult to define the source and type of fault, due to the variability in the conditions under which faults occur. Conventional methods based on the analysis of dissolved gases in oil have been used by companies to diagnose faults, however, these methods still need to be applied together to reach a satisfactory result, as well as relying heavily on the knowledge of a specialist. In order to solve the difficulties related to conventional methods, some systems based on Computational Intelligence have been proposed in the literature and have presented promising results. This paper presents the results of the study developed of the application of deep neural networks to fault diagnosis, considering then the importance of fault diagnosis in transformers. Two models are proposed using Convolutional Neural Networks and Stacked Autoencoding Neural Networks. For the development of the systems we used the TC 10 database with faulty transformer situations. This base was used to develop the IEC 60599 method, which is one of the main methods used by power utilities for transformer diagnostics through the analysis of dissolved gases in oil. The promising results achieved with the two proposed models (100% accuracy in the test base) show the great applicability of deep neural networks to the problem of incipient transformer fault diagnosis, however showing a great alternative to the conventional methods commonly used.