Transportation of highly viscous and high-curing oils through main pipelines requires significant energy costs. Thus, the task of choosing the cheapest pumping modes is very relevant. The article describes and proposes a solution to the oil flow problem in a pipeline using two methods: with preheating and using a Laval nozzle at the inlet of the pipeline. Mathematical models of the flow of highviscosity oil in the main oil pipeline for the two named pumping methods have been compiled. An algorithm has been developed for calculating temperature, viscosity and pressure along the length of the Uzen-Atyrau pipeline at various oil flow rates. The results of temperature and pressure distribution are analyzed and compared at different oil flow rates along the length of the pipeline for two pumping methods. It is shown that the use of cavitation improves the rheological properties of oil and can significantly reduce the cost of pumping. The research results can be used to predict the operation of main oil pipelines pumping oil both in a heated state and in isothermal mode with a Laval nozzle.
This article describes the recognition of bank card information. Recognizing an object with a camera is one of the most important tasks at the moment. Recognizing credit card data at the same time is a rather complex algorithmic task, but at the moment the implementation of this task is very relevant and in-demand due to the increase in the number of payment transactions via mobile devices. The implementation of this task can save a person from having to enter most of the data when making online payments. The fundamental difficulties of this problem are discussed and methods for solving it are proposed. The problem under consideration is solved for the case of application on mobile devices, which imposes strict requirements for computational complexity. The article presents the results of a formal analysis of the performance and accuracy of the proposed algorithm. The error spectrum of the recognition system as a whole shows that the proposed algorithm solves the problem with the required accuracy. The main question that was investigated at this work: is it possible to use the Tesseract OCR library for text recognition from video images, for example, timecode? That is, digital time data embedded in the footage images. This is important for the automation of individual procedures for video technical expert studies. Object recognition by the camera is one of the most important tasks at the moment. The fundamental difficulties of this problem are discussed and methods for its solution are proposed. The article presents the results of a formal analysis of the performance and accuracy of the proposed algorithm. The spectrum of errors of the recognition system as a whole shows that the proposed algorithm solves the problem with the required accuracy.
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