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Institution

Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan

EducationDnipro, Ukraine
About: Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan is a education organization based out in Dnipro, Ukraine. It is known for research contribution in the topics: Track (rail transport) & Bogie. The organization has 736 authors who have published 655 publications receiving 1468 citations. The organization is also known as: Institute of Railway Transport Engineers.


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Journal ArticleDOI
TL;DR: In this article, the authors present a survey of the state-of-the-art technologies used in the field of data collection and analysis in the context of data mining. But their focus is on data collection, not data aggregation.
Abstract: Цель : проверить предположение о том, что предложенное содержание психологической и психофизической подготовки студентов железнодорожных вузов в процессе физического воспитания является эффективным с точки зрения снижения высокого уровня личностной тревожности. Материал : в исследовании приняли участие 120 студентов, которые имели высокий уровень личностной тревожности. Возраст участников исследования составлял 17 - 19 лет. Психологическая диагностика уровня личностной тревожности у студентов проводилась с использованием шкалы оценки уровня реактивной и личностной тревожности Ч.Спилбергера. Результаты: использования в процессе психологической и психофизической подготовки на занятиях по физическому воспитанию у юношей (значимых видов спорта - атлетизма и пауэрлифтинга) и у девушек (аэробики и сахаджа-йоги) статистически значимо повлияло на снижение в них личностной тревожности. Выводы : Рекомендуется учебные занятия по физическому воспитанию проводить по следующей структуре. Подготовительная часть занятия - 10 минут. Основна - 75 минут. Из них 25 минут - для решения традиционных задач физического воспитания студентов по формированию у них двигательных навыков и умений и развитию физических качеств. 20 минут - отводилось на выполнение студентами специальных физических упражнений. 30 минут основной части посвящено занятию профессионально значимым видом спорта. Заключительная часть - 5 минут.

8 citations

Journal ArticleDOI
01 Nov 2020
TL;DR: In this article, the authors present the procedure used for establishing the cause of the wagon derailment, which is based on the mathematical models of longitudinal oscillations of a train and the spatial vibrations of wagons, in particular of tank wagons.
Abstract: The paper is presenting the procedure used for establishing the cause of the wagon derailment. To do this, the computer simulations and the computational software, developed in the Dnipro National University of Railway Transport (DIIT) were used. The level of longitudinal forces and the wagons dynamic performance have been evaluated using the mathematical models of longitudinal oscillations of a train and the spatial vibrations of wagons, in particular of tank wagons. As a result of modeling we obtained oscillograms of longitudinal forces in each inter-wagon connection, the dependence of the largest longitudinal forces on travel time and distance traveled, the distribution of the maximum longitudinal forces along the train length, the speed dependence on travel time and track coordinates. We also obtained the dynamic performance of wagons: the vertical dynamics coefficients of the axle-box and central suspension, the horizontal dynamics coefficients and the derailment stability coefficient. The influence of the movable load in the tank wagons and the characteristics of rail irregularities on the stability coefficient against wheel climbing onto the rail is also considered. The presented methodology was used to determine the cause of the tank wagon derailment in a non-homogenous freight train consisting of 50 wagon tanks on an existing track section of the Lithuanian railways. When simulating the train movement, it was assumed that the train was equipped with elastic-friction absorbing devices and air distributors, turned on to the average operation mode. As a result of numerical experiments, an assumption was made about the cause of the train derailment.

8 citations

Journal ArticleDOI
TL;DR: The practical significance of the obtained results is the use of such a calculation method that does not require significant time for its implementation and can be used as a subsystem of the on-board train control system capable of performing calculations taking into account changes in the current train situation.
Abstract: Development of a method for calculating the optimal mode of conducting a train in terms of energy saving meet the safety requirements and schedules. The method of calculation must solve the assigned tasks without significant time spent on the calculation. To implement this method of calculation was used a simplified model of the train as a controlled system. The existing mathematical and algorithmic methods for solving isoperimetric problems of finding the optimal solution in the presence of restrictions on resources were the information base for methodology development. Scientific works of domestic and foreign scientists, professional periodicals, materials of scientific and practical conferences, methodical and normative materials, currently in force on Ukrainian Railways. The results of these studies were used to create simulators on the basis of computer technology for the training of locomotive drivers. The scientific novelty of the proposed calculation method consists in applying the simplified calculations of the status of the train as a controlled system, without the use of differential equations of motion that allows to significantly increase the speed of the calculations. This, in turn, will solve the problems of finding optimal control in real time, taking into account changing conditions during the movement of the train. The practical significance of the obtained results is the use of such a calculation method that does not require significant time for its implementation and can be used as a subsystem of the on-board train control system capable of per-forming calculations taking into account changes in the current train situation.

8 citations

Journal ArticleDOI
TL;DR: Bardas et al. as mentioned in this paper reported ORCID 0000-0001-8772-9328; I.O. Skovron and Ye. Demchenko: ORCid 0000-0002-5393-0004.
Abstract: O. Bardas: ORCID 0000-0001-8772-9328; I. Skovron: ORCID 0000-0003-0697-2698; Ye. Demchenko: ORCID 0000-0003-1411-6744; A. Dorosh: ORCID 0000-0002-5393-0004

8 citations

Proceedings ArticleDOI
01 Sep 2019
TL;DR: An artificial neural network was used to classify ALSN signal disturbances, and the efficiency of the proposed algorithm was verified by processing of several specially simulated as well as real ALSN signals measured during tests.
Abstract: The problem considered in the work is concerned to the methods to reveal and identify the signal disturbances occurred in the railway cab signaling system caused by electromagnetic interference from traction current. The continuous automatic locomotive signalling system (ALSN) is a type of cab signalling systems that provides track status information to the train cab and uses the rails as a continuous communication channel between track and train. To ensure the reliability of the transmission of ALSN commands to the train cab, the basic parameters of the coded current are periodically checked for compliance with certain requirements during planned test trips of a specially equipped railway car-laboratory. The ALSN signal is received by the coils of the car-laboratory and recorded by computer. Then an operator visually analyzes the recorded signal to detect problem segments with interference exceeding the limit level what requires a lot of time and does not provide the necessary accuracy. To automate the detection and identification of the ALSN signal disturbances, the following algorithm has been proposed. At the first stage, the wavelet packet energy Shannon entropy is used to reveal problem segments of ALSN signal. At the second stage, a detailed analysis of the ALSN signal using DWPT is performed to determine the type and parameters of interference at the revealed problem segments. To classify ALSN signal disturbances, an artificial neural network was used. The efficiency of the proposed algorithm was verified by processing of several specially simulated as well as real ALSN signals measured during tests.

8 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20222
202131
202057
201984
201859