Intelligent transportation is related to the developments in various types of technology. In this chapter we want to discuss the use of deep learning model to predict potential vibrations of high-speed trains. In our research we have tested developed deep learning model to predict potential vibrations. We have tested various time steps and potential error margins. Results of our research show that our system is able to predict with accuracy of above 95% with precise results in a series of values forward.
Deep Neural Network-Based Prediction of High-Speed Train-Induced Subway Track Vibration
Internet of Things: Tech., Communicat., Computing
Intelligent Cyber-Physical Systems for Autonomous Transportation ; Kapitel : 12 ; 201-212
15.12.2021
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Deep learning , Recurrent Neural Network , NAdam algorithm , Machine learning , Long Short-Term Memory , Artificial neural networks , Min–Max approach , Vibration estimation , High-speed train , Induced subway track vibration Engineering , Cyber-physical systems, IoT , Communications Engineering, Networks , Automotive Engineering , Computer Science
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