Nowadays, despite a negative impact on the natural environment, coal combustion is still a significant energy source. One way to minimize the adverse side effects is sophisticated combustion technologies, such as, e.g., staged combustion, co-combustion with biomass, and oxy-combustion. Maintaining the combustion process at its optimal state, considering the emission of harmful substances, safe operation, and costs requires immediate information about the process. Flame image is a primary source of data which proper processing make keeping the combustion at desired conditions, possible. The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states. The method is based on the adaptive selection of the gamma correction coefficient (G) in the flame segmentation process. It uses the empirically determined relationship between the G coefficient and the average intensity of the R image component. The pre-trained VGG16 model for classification was used. It provided accuracy in detecting particular combustion states on the ranging from 82 to 98%. High accuracy and fast processing time make the proposed method possible to apply in the real systems.

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This paper presents the use of Hellwig's method for dimension reduction in feature space of thyroid ultrasound images. On the base of this method, the combination of three features with the greatest value of Hellwig's index information capacity from the input set of 283 features was obtained. This set was used to build and test the classifiers. Classification results were compared with the results obtained for a set of 48 features obtained using correlation method. It turned out that the accuracy of classifiers built on the base of 3 features is not worse than the accuracy of classifiers built on the base of 48 features, and in some cases it is even higher. This suggests that the Hellwig's method can be used as an effective method for dimension reduction in feature space for the future thyroid ultrasound images classification.

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