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Relevant bibliographies by topics / Autonomous Train

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Journal articles

Academic literature on the topic 'Autonomous Train'

Author: Grafiati

Published: 4 May 2024

Last updated: 14 September 2024

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Journal articles on the topic "Autonomous Train"

1

Guo, Yichen. "Design and research of train-centric autonomous control CBTC system." Applied and Computational Engineering 12, no. 1 (2023): 260–67. http://dx.doi.org/10.54254/2755-2721/12/20230364.

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Existing CBTC systems suffer from a number of limitations, some of which include convoluted communication between the ground and trains, costly construction, and high costs associated with the installation of ground equipment. The purpose of this study is to further improve the operational efficiency of a train-centric autonomous CBTC system by analyzing the system design, control principles, and module functioning. The planning of ground and onboard equipment will be the main research objects of the novel system. When compared to conventional CBTC systems, the TACS architecture, most notably

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2

AFANASOV, A., D. LINIK, S. ARPUL, D. BELUKHIN, and V. VASYLYEV. "PROSPECTS OF USING AUTONOMOUS ELECTRIC TRAINS WITH ONBOARD STORAGE STORES." Transport systems and transportation technologies, no. 23 (July 28, 2022): 46. http://dx.doi.org/10.15802/tstt2022/261652.

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Purpose. Improving the efficiency of passenger traffic on non-electrified sections of the railway of Ukraine by optimizing the structure and creating principles for building a traction electric drive of a promising autonomous electric train powered by traction engines from the system of onboard storage of electricity. Methods. The methodological basis of the study are the general theoretical provisions and principles of the system approach of theoretical electrical engineering, theoretical mechanics, theory of electrical machines and converters. The basic principles of management theory and th

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3

Oh, Sehchan, Kyunghee Kim, and Hyeonyeong Choi. "Train interval control and train-centric distributed interlocking algorithm for autonomous train driving control system." Journal of the Korea Academia-Industrial cooperation Society 17, no. 11 (2016): 1–9. http://dx.doi.org/10.5762/kais.2016.17.11.1.

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4

Atherton, Mark, Stuart Hill, David Harrison, and Marco Ajovalasit. "Economic and technical feasibility of a robotic autonomous system for train fluid servicing." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 3 (2019): 338–50. http://dx.doi.org/10.1177/0954409719830520.

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Rail traffic in passenger miles [in the UK] is projected to increase by 100% over the next 30 years, which presents a considerable challenge for the current infrastructure to perform the regular fluid servicing tasks. Developing robotic autonomous systems for train fluid servicing is a prospect for which no solutions currently exist. Therefore, the economic and technical feasibility of a robotic autonomous system to perform several key fluid servicing tasks on passenger train vehicles is investigated. The fluid servicing tasks chosen include those that to a significant degree are repetitive or

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5

Hadas, Zdenek, Jan Smilek, and Ondrej Rubes. "Energy harvesting from passing train as source of energy for autonomous trackside objects." MATEC Web of Conferences 211 (2018): 05003. http://dx.doi.org/10.1051/matecconf/201821105003.

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This paper deals with an energy harvesting review and analysis of an ambient mechanical energy on a trackside during a passing of a train. Trains provide very high level of vibration and deformation which could be converted into useful electricity. Due to maintenance and safety reasons a rail trackside includes sensing systems and number of sensor nodes is increased for modern transportation. Recent development of modern communication and ultra-low power electronics allows to use energy harvesting systems as autonomous source of electrical energy for these trackside objects. Main aim of this p

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6

Hwang, Jong-Gyu, Sung-Yoon Chae, Byung-Hun Lee, and Rag-Gyo Jeong. "Design of Train Driving Control System for SITL-based Autonomous Train Control Simulator." Journal of Korean Institute of Information Technology 21, no. 11 (2023): 71–79. http://dx.doi.org/10.14801/jkiit.2023.21.11.71.

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7

Kim, Kyung Min, and Suk-Joon Ko. "Path Search for Autonomous Train via Dial Algorithm." Journal of Korean Institute of Communications and Information Sciences 43, no. 4 (2018): 775–83. http://dx.doi.org/10.7840/kics.2018.43.4.775.

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8

Lee, Dong-Jin, Ilmu Byun, and Rag-Gyo Jeong. "Integrated Antenna Design for Autonomous Train Control System." Journal of the Korean Society for Railway 27, no. 6 (2024): 492–502. http://dx.doi.org/10.7782/jksr.2024.27.6.492.

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9

Kim, Hyunkun, Hyeongoo Pyeon, Jong Sool Park, Jin Young Hwang, and Sejoon Lim. "Autonomous Vehicle Fuel Economy Optimization with Deep Reinforcement Learning." Electronics 9, no. 11 (2020): 1911. http://dx.doi.org/10.3390/electronics9111911.

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The ever-increasing number of vehicles on the road puts pressure on car manufacturers to make their car fuel-efficient. With autonomous vehicles, we can find new strategies to optimize fuels. We propose a reinforcement learning algorithm that trains deep neural networks to generate a fuel-efficient velocity profile for autonomous vehicles given road altitude information for the planned trip. Using a highly accurate industry-accepted fuel economy simulation program, we train our deep neural network model. We developed a technique for adapting the heterogeneous simulation program on top of an op

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10

Jing, Chunhui, Haohong Dai, Xing Yao, et al. "Influence of Multi-Modal Warning Interface on Takeover Efficiency of Autonomous High-Speed Train." International Journal of Environmental Research and Public Health 20, no. 1 (2022): 322. http://dx.doi.org/10.3390/ijerph20010322.

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As a large-scale public transport mode, the driving safety of high-speed rail has a profound impact on public health. In this study, we determined the most efficient multi-modal warning interface for automatic driving of a high-speed train and put forward suggestions for optimization and improvement. Forty-eight participants were selected, and a simulated 350 km/h high-speed train driving experiment equipped with a multi-modal warning interface was carried out. Then, the parameters of eye movement and behavior were analyzed by independent sample Kruskal–Wallis test and one-way analysis of vari

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