Ferramenta computacional para análise de imagens de ensaios mecânicos de dureza

Este artigo apresenta uma ferramenta computacional capaz de determinar as durezas Brinell e Vickers a partir de imagens de identações. A referida ferramenta integra algoritmos de processamento e análisede imagem, como crescimento de regiões e contornos ativos (snakes). Para validar a ferramenta proposta, foram realizadas comparações dos resultados obtidos por ela e pelo processo convencional a partir de identações realizadas no aço ABNT 1020. A partir dessa comparação, pode-se afirmar que a ferramenta desenvolvida é mais rápida, intuitiva na sua aplicação e menos dependente da subjetividade do operador

Energy production predication via Internet of Thing based machine learning system

© 2019 Elsevier B.V. Wind energy is an interesting source of alternative energy to complement the Brazilian energy matrix. However, one of the great challenges lies in managing this resource, due to its uncertainty behavior. This study addresses the estimation of the electric power generation of a wind turbine, so that this energy can be used efficiently and sustainable. Real wind and power data generated in set of wind turbines installed in a wind farm in Ceará State, Brazil, were used to obtain the power curve from a wind turbine using logistic regression, integrated with Nonlinear Autoregressive neural networks to forecast wind speeds. In our system the average error in power generation estimate is of 29 W for 5 days ahead forecast. We decreased the error in the manufacturer\u27s power curve in 63%, with a logics regression approach, providing a 2.7 times more accurate estimate. The results have a large potential impact for the wind farm managers since it could drive not only the operation and maintenance but management level of energy sells

Ferramenta de análise não destrutiva para obtenção de parâmetros microestruturais baseada em Visão Computacional

Este trabalho apresenta novos parâmetros de medida calculados por um Sistema de Visão Computacional desenvolvido para a Classificação de Microestruturas em Materiais Metálicos. Este sistema é uma ferramenta de análise de imagens adequada para a área de Ciência dos Materiais, permitindo realizar automaticamente a segmentação e quantificação de microestruturas em materiais metálicos. Como evolução deste sistema, este trabalho apresenta novos parâmetros de medida que possibilitam uma análise mais detalhada das microestruturas através de medidas de comprimento, área e perímetro. Para obter estas medidas, utiliza-se o algoritmo de crescimento de regiões e o filtro de Roberts. Após a calibração correta do microscópico óptico usado obtêm-se as fotomicrografias necessárias para a aplicação do sistema desenvolvido. Para validar os resultados obtidos é realizada uma comparação com a análise de microscopia convencional. Portanto, o sistema apresentado é capaz, para além de realizar segmentação e quantificação de microestruturas, de obter parâmetros importantes para uma análise mais detalhada das propriedades mecânica dos materiais baseados em ensaios não destrutivos

Classification of induced magnetic field signals for the microstructural characterization of sigma phase in duplex stainless steels

Duplex stainless steels present excellent mechanical and corrosion resistance properties.However, when heat treated at temperatures above 600 ºC, the undesirable tertiary sigma phaseis formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, thematerial toughness being compromised when the amount of this phase is not less than 4%. Thiswork aimed to develop a solution for the detection of this phase in duplex stainless steels throughthe computational classification of induced magnetic field signals. The proposed solution is based onan Optimum Path Forest classifier, which was revealed to be more robust and effective than Bayes,Artificial Neural Network and Support Vector Machine based classifiers. The induced magneticfield was produced by the interaction between an applied external field and the microstructure.Samples of the 2205 duplex stainless steel were thermal aged in order to obtain different amounts ofsigma phases (up to 18% in content). The obtained classification results were compared against theones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracturesurface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved aclassification accuracy superior to 95% and was revealed to be robust to signal noise, being thereforea valid testing tool to be used in this domain

Automated recognition of lung diseases in CT images based on the optimum-path forest classifier

The World Health Organization estimated that around 300 million people have asthma, and 210 million people are affected by Chronic Obstructive Pulmonary Disease (COPD). Also, it is estimated that the number of deaths from COPD increased 30% in 2015 and COPD will become the third major cause of death worldwide by 2030. These statistics about lung diseases get worse when one considers fibrosis, calcifications and other diseases. For the public health system, the early and accurate diagnosis of any pulmonary disease is mandatory for effective treatments and prevention of further deaths. In this sense, this work consists in using information from lung images to identify and classify lung diseases. Two steps are required to achieve these goals: automatically extraction of representative image features of the lungs and recognition of the possible disease using a computational classifier. As to the first step, this work proposes an approach that combines Spatial Interdependence Matrix (SIM) and Visual Information Fidelity (VIF). Concerning the second step, we propose to employ a Gaussian-based distance to be used together with the optimum-path forest (OPF) classifier to classify the lungs under study as normal or with fibrosis, or even affected by COPD. Moreover, to confirm the robustness of OPF in this classification problem, we also considered Support Vector Machines and a Multilayer Perceptron Neural Network for comparison purposes. Overall, the results confirmed the good performance of the OPF configured with the Gaussian distance when applied to SIM- and VIF-based features. The performance scores achieved by the OPF classifier were as follows: average accuracy of 98.2%, total processing time of 117 microseconds in a common personal laptop, and F-score of 95.2% for the three classification classes. These results showed that OPF is a very competitive classifier, and suitable to be used for lung disease classification

Ferramenta computacional para análise de imagens de ensaios mecânicos de dureza

Este artigo apresenta uma ferramenta computacional capaz de determinar as durezas Brinell e Vickers a partir de imagens de identações. A referida ferramenta integra algoritmos de processamento e análise de imagem, como crescimento de regiões e contornos ativos (snakes). Para validar a ferramenta proposta, foram realizadas comparações dos resultados obtidos por ela e pelo processo convencional a partir de identações realizadas no aço ABNT 1020. A partir dessa comparação, pode-se afirmar que a ferramenta desenvolvida é mais rápida, intuitiva na sua aplicação e menos dependente da subjetividade do operador