Кафедра "Інтернет речей"

Результати пошуку

Зараз показуємо 1 - 4 з 4

The Input Material Flow Model of the Transport Conveyor
(Institute of Electrical and Electronics Engineers, Inc., 2022) Pihnastyi, O. M.; Sobol, Maksym
This paper discusses the problem of forming a data set for training a neural network used to build a model of a multi-section conveyor. The analysis of the models, which are used by designing the flow parameters control system of the transport system, is given. The conditions of applying a neural network in the transport conveyer model are justified and determined. Methods for generating a data set for training a neural network are discussed. As the main approach, the use of production data obtained from functioning transport conveyors is considered. Statistically processed data can be used to build generators of stochastic processes that model the incoming material flow for the transport system. The development of these generators to form the input flow of the material of the transport system opens up the possibility of analyzing and monitoring conveyor models in various modes of its configuration. A statistical analysis of the incoming material flow of the transport system was carried out and its number characteristics were determined. The correlation function characterizing the input flow of material for the transport system is considered. The introduction of dimensionless parameters to describe the input material flow made it possible to scale the results of work for a wide class of conveyor-type transport systems.
Analysis of a Dataset for Modeling a Transport Conveyor
(2022) Pihnastyi, O. M.; Burduk, Anna
The analysis of the works, which considered the use of neural networks for modeling a multi-section transport conveyor, was carried out. The prospects for the use of neural networks for the design of highly efficient control systems for the flow parameters of a multi-section transport conveyor are studied. The problem that limits the use of neural networks for building control systems for the flow parameters of a multi-section transport conveyor is considered. The possibility of constructing generators for generating a data set for the process of training a neural network is being studied. A method for generating a data set based on experimentally obtained measurements of the instantaneous values of the input material flow as a result of the operation of industrial transport systems is proposed. Using dimensionless variables, a statistical analysis of a stochastic flow of material entering the input of the transport system was performed. An estimate of the correlation time of a stochastic process characterizing the input flow of material is given. The recommendations on choosing the type of correlation function for the model of the input material flow were confirmed. It is demonstrated that the input flow of material is a non-stationary stochastic process. Approximations for modeling the input flow of materials of the operating transport system are considered.
On the Characteristics of the Input Material Flow of the Transport Conveyor
(Vasyl Stefanyk Precarpathian National University, 2022) Pihnastyi, O. M.; Sobol, Maksym; Yelchaninov, D. B.
In this paper, the statistical characteristics of the flow of material entering the input of a conveyor-type transport system are studied. For a set of data obtained as a result of experimental measurements of the input flow of material, the law of distribution of a random variable and the correlation function is investigated. Theoretical assumptions about the law of change of the correlation function for the input flow of material are confirmed.
Construction of Control Systems of Flow Parameters of the Smart Conveyor using a Neural Network
(University of Žilina, Slovakia, 2021) Sytnikova, Anastasiya; Pihnastyi, O. M.
In this paper, the results of the model for forecasting the flow parameters of a distributed transport system of the conveyor type are briefly considered. It is shown that the model of the transport system based on the neural network can be successfully applied to predict the flow parameters of the transport system which consists of a very large number of sections.

Кафедра "Інтернет речей"

Переглянути

Фільтри

Налаштування

Сортувати за

Результатів на сторінку

Результати пошуку