https://ir.lib.vntu.edu.ua//handle/123456789/415 Факультет електроенергетики та електромеханіки 2026-03-10T00:35:47Z https://ir.lib.vntu.edu.ua//handle/123456789/50777 Construction of a model for measuring liquefied gas volume based on an artificial neural network Knysh, В.; Kulyk, Ya.; Pavlyuk, О.; Книш, Б. П.; Кулик, Я. А.; Павлюк, О. This study investigates the process of quantifying liquefied gas volume using an artificial neural net-work. The task addressed relates to the insufficient efficiency of existing methods for measuring liquefied gas volume. It can be partially solved by measuring the parameters of liquefied gas in cylinders remot ely and by processing the data with an artificial neural net-work to quantify its volume. However, there is another issue associated with the complexity of using artificial neural networks in combination with correspond-ing peripherals, in particular devices, means, sen-sors, gauges, etc., and the need for significant comput-ing power.This paper suggests a model for measuring lique-fied gas volume, which takes into account its physical characteristics, based on an artificial neural network that provides communication with gas measurement devices. The mechanism behind such result involves training the model based on performance indica-tors derived from input data, taking into account the formed features. High generalization ability and efficiency are illus-trated by the coefficient of determination, which equals 0.999245. High accuracy is illustrated by the overall low average value of a mean absolute error, which equals 1%. That was made possible by the dis-tinctive features of the proposed solution, namely the optimized model architecture in accordance with the object of study and its input features. These features are the signal from a photodetector, which character-izes the level of liquefied gas, the angles of the cylinder in the vertical plane, as well as in the horizontal plane.The results could be applied to tasks involving the measurement of liquefied gas volume, especially at oil and gas processing plants, gas filling stations, gas stor-age facilities, etc.Keywords: liquefied gas, artificial neural network, mean absolute error, coefficient of determination. 2026-01-01T00:00:00Z https://ir.lib.vntu.edu.ua//handle/123456789/50625 Конструктивні особливості та автоматизований розрахунок магнітопроводів магнітозв`язаних LCL-фільтрів для сонячних електростанцій Мадьяров, В. Г.; Кацив, С. Ш.; Кухарчук, В. В.; Madyarov, V.; Katsyv, S.; Kukharchuk, V. 2021-01-01T00:00:00Z https://ir.lib.vntu.edu.ua//handle/123456789/50623 Визначення оптимальних параметрів магнітозв`язаних LCL-фільтрів для сонячних електростанцій Кацив, С. Ш.; Мадьяров, В. Г.; Katsyv, S.; Madyarov, V. 2021-01-01T00:00:00Z https://ir.lib.vntu.edu.ua//handle/123456789/50619 Нестандартний аналіз в електротехніці: кола постійного струму з ідеальними ємностями Кухарчук, В. В.; Кацив, С. Ш.; Мадьяров, В. Г.; Kukharchuk, V.; Katsyv, S.; Madyarov, V. В роботі запропоновано використання ідей та методів нестандартного аналізу в галузі теоретичної електротехніки. Наведені основні засади нестандартного аналізу. Розглянуті характерні приклади аналізу кіл постійного струму з ідеальними ємнісними елементами. 2022-01-01T00:00:00Z