Кафедра "Інтернет речей"
Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/5398
Увага! Поповнення колекції кафедри "Інтернет речей" – призупинено.
Від вересня 2022 року кафедри "Інтернет речей" та "Мультимедійних інформаційних технологій і систем" об’єднані у кафедру "Мультимедійні та інтернет технології і системи".
Первісна назва кафедри – "Розподілені інформаційні системи і хмарні технології".
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Документ Conveyor Model with Input and Output Accumulating Bunker(Institute of Electrical and Electronics Engineers, Inc., USA, 2020) Pihnastyi, O. M.; Kozhevnikov, G. K.; Khodusov, V. D.In this article, a model of a conveyor-type transport system with an input and output bunker is developed. The transport conveyor is presented in the form of a dynamic distributed system. It is shown that the material flow is proportional to the linear density of material distribution along the transport route. The coefficient of proportionality is the speed of the belt. When constructing the model, the assumption of the absence of oscillatory processes associated with the tension of the conveyor belt is introduced, which corresponds to the case when the function determining the speed of the belt is only a function of time. A solution is given, that determines the state of the flow parameters of the conveyor section for a given point of the transport route at an arbitrary point in time. It is shown that the state of the flow parameters for an arbitrary place in the transport route is determined by the state of the flow parameters at the input of the conveyor section, considering the transport delay. An expression is written that allows to calculate the amount of transport delay. The relationship of the transport delay value with the algorithm for controlling the conveyor belt speed is demonstrated. A system of equations for the model of a conveyor-type transport system with an input and output bunker is obtained. The behavior of the model for several characteristic cases of the functioning of the transport system is analyzed. The constructed model of the control object can be used to design highly efficient control systems for the flow parameters of the transport system with an input and an output bunker.Документ Linear regression model of the conveyor type transport system(2020) Pihnastyi, O. M.; Khodusov, V. D.; Subbotin, SergeyThis article discusses the prospects of using linear regression models to describe multi-section branched transport systems of conveyor type. A characteristic feature of the functioning of a multi-section transport system is the presence of resonant peak values for the flow parameters of the transport system and transport delay. Various variants of the linear regression model are investigated. It is shown that for multisection transport systems with a periodic nature of the magnitude of the incoming material flow into the transport system and periodic nature of the regulation of the belt speed the value of the transport delay is a quasi-stationary value. The transport delay can be excluded from model variables. Analysis of the various variants of linear regression models considered in the article shows that using them to describe branched transport systems is ineffective. The considered models can only be used for a qualitative analysis of the output stream from the transport system. The absence of a linear relationship between the input and output flow parameters of the transport system is shown.Документ Neural model of conveyor type transport system(2020) Pihnastyi, O. M.; Khodusov, V. D.In this paper, a model of a transport conveyor system using a neural network is demonstrated. The analysis of the main parameters of modern conveyor systems is presented. The main models of the conveyor section, which are used for the design of control systems for flow parameters, are considered. The necessity of using neural networks in the design of conveyor transport control systems is substantiated. A review of conveyor models using a neural network is performed. The conditions of applicability of models using neural networks to describe conveyor systems are determined. A comparative analysis of the analytical model of the conveyor section and the model using the neural network is performed. The technique of forming a set of test data for the process of training a neural network is presented. The foundation for the formation of test data for learning neural network is an analytical model of the conveyor section. Using an analytical model allowed us to form a set of test data for transient dynamic modes of functioning of the transport system. The transport system is presented in the form of a directed graph without cycles. Analysis of the model using a neural network showed a high-quality relationship between the output flow for different conveyor sections of the transport system.Документ The optimal control problem for output material flow on conveyor belt with input accumulating bunker(Южно-Уральский государственный университет, 2019) Pihnastyi, O. M.; Khodusov, V. D.The article is devoted to the synthesis of optimal control of the conveyor belt with the accumulating input bunker. Much attention is given to the model of the conveyor belt with a constant speed of the belt. Simulation of the conveyor belt is carried out in the one-moment approximation using partial differential equations. The conveyor belt is represented as a distributed system. The used PDE-model of the conveyor belt allows determining the state of the flow parameters for a given technological position as a function of time. We consider the optimal control problem for flow parameters of the conveyor belt. The problem consists in ensuring the minimum deviation of the output material flow from a given target amount. The control is carried out by the material flow amount, which comes from the accumulating bunker into the conveyor belt input. In the synthesis of optimal control, we take into account the limitations on the size of the accumulating bunker, as well as on both max and min amounts of control. We construct optimal control of the material flow amount coming from the accumulating bunker. Also, we determine the conditions to switch control modes and estimate time period between the moments of the switching.