The issues of high-quality and reliable water purification for medical needs are considered. The cleaning process is inextricably linked with the automated control of engineering systems. Water for medical purposes places high demands on the degree of filtration. In this regard, the task is formulated to ensure continuous automated monitoring and control of the cleaning process. The paper shows an algorithm for controlling the process of preliminary water purification. Based on the algorithm, a program was written in the ST language, which describes the logic of the visualization with the function of displaying and controlling the process. A fragment of the functional diagram of preliminary filtering is given. A visualization of the filter washing process is presented.
Keywords: maximum level, minimum level, backup filter, storage tank, pre-cleaning, technological equipment, visualization algorithm, functional diagram, alarm, filter washing
This article discusses the problems of determining the organs of air respiration on computed tomography images using convolutional neural networks of the U-NET architecture. The prospects of using neural networks in the analysis of medical images, as well as the use of the U-NET architecture for semantic segmentation of images are described. The structure of an artificial neural network based on the U-NET architecture is being formed. The structure of the layers of this network is visualized and the components of this structure are described. Special attention is paid to the description and implementation of the convolution process. The formula for determining the weight coefficients of the separation boundary is presented. Algorithms for the formation of an artificial neural network model and an algorithm for constructing layers are proposed. A method of increasing data for a training sample of images of medical images is considered. The image of the result of the determination of the chest organs and the corresponding mask are presented.
Keywords: convolutional neural networks, U-NET architecture, deep learning, image recognition, machine learning