Navegando por Autor "Ananias, João Víctor Gomes Guimarães"

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Numerical and physical simulation of steel ladle draining operation with different well block design.
(2019) Conceição, Paulo Vinícius Souza da; Silva, Carlos Antônio da; Silva, Itavahn Alves da; Ananias, João Víctor Gomes Guimarães; Resende, Alexandre Dolabella
At the end of a ladle draining operation a structure called drain sink forms at the top surface of the liquid steel allowing slag carry over from the ladle to the tundish and causing metallic losses. On this study, the effect of different well block configuration on the critical height for drain sink formation (HC) was investigated using numerical and physical modeling. The modified well block configuration showed lower HC in most of the cases when compared with the standard design. It could represent almost 50% of metallic lost savings during the continuous casting process. Air injection was also investigated and increased significantly the HC value. The numerical model showed good agreement with the physical model and was used to help undertanding this behavior.
Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images.
(2021) Luz, Eduardo José da Silva; Silva, Pedro Henrique Lopes; Silva, Rodrigo Pereira da; Silva, Ludmila; Ananias, João Víctor Gomes Guimarães; Miozzo, Gustavo; Moreira, Gladston Juliano Prates; Gomes, David Menotti
Purpose Confronting the pandemic of COVID-19 is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction method, is time-consuming and in short supply due to the pandemic. Thus, researchers have been looking for alternative screening methods, and deep learning applied to chest X-rays of patients has been showing promising results. Despite their success, the computational cost of these methods remains high, which imposes difficulties to their accessibility and availability. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays. Methods To achieve the defined objective, we propose a new family of models based on the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints. We also exploit the underlying taxonomy of the problem with a hierarchical classifier. A dataset of 13,569 X-ray images divided into healthy, non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposed approaches and other 5 competing architectures. We also propose a cross-dataset evaluation with a second dataset to evaluate the method generalization power. Results The results show that the proposed approach was able to produce a high-quality model, with an overall accuracy of 93.9%, COVID-19 sensitivity of 96.8%, and positive prediction of 100% while having from 5 to 30 times fewer parameters than the other tested architectures. Larger and more heterogeneous databases are still needed for validation before claiming that deep learning can assist physicians in the task of detecting COVID-19 in X-ray images, since the cross-dataset evaluation shows that even state-of-the-art models suffer from a lack of generalization power. Conclusions We believe the reported figures represent state-of-the-art results, both in terms of efficiency and effectiveness, for the COVIDx database, a database of 13,800 X-ray images, 183 of which are from patients affected by COVID-19. The current proposal is a promising candidate for embedding in medical equipment or even physicians’ mobile phones.