A data-driven approach for railway in-train forces monitoring
暂无分享,去创建一个
[1] C. Cole,et al. Augmented digital twin for railway systems , 2023, Vehicle System Dynamics.
[2] C. Cole,et al. Vehicle system dynamics in digital twin studies in rail and road domains , 2023, Vehicle System Dynamics.
[3] R. Trinchero,et al. Application of machine learning techniques to build digital twins for long train dynamics simulations , 2023, Vehicle System Dynamics.
[4] Jinsong Yang,et al. A diagnostic framework with a novel simulation data augmentation method for rail damages based on transfer learning , 2023, Structural Health Monitoring.
[5] Shaofeng Lu,et al. Energy-efficient automatic train operation for high-speed railways: Considering discrete notches and neutral sections , 2022, Transportation Research Part C: Emerging Technologies.
[6] Q. Zhang,et al. Physics-based machine learning method and the application to energy consumption prediction in tunneling construction , 2022, Adv. Eng. Informatics.
[7] R. Goverde,et al. A literature review of Artificial Intelligence applications in railway systems , 2022, Transportation Research Part C: Emerging Technologies.
[8] T. Tang,et al. Data-driven models for train control dynamics in high-speed railways: LAG-LSTM for train trajectory prediction , 2022, Inf. Sci..
[9] Ling Xiang,et al. Ultra-short term wind power prediction applying a novel model named SATCN-LSTM , 2021, Energy Conversion and Management.
[10] Jia-Ji Wang,et al. A deep learning framework for constitutive modeling based on temporal convolutional network , 2021, J. Comput. Phys..
[11] Yunguang Ye,et al. MBSNet: A deep learning model for multibody dynamics simulation and its application to a vehicle-track system , 2021 .
[12] Jie Liu,et al. High-speed train fault detection with unsupervised causality-based feature extraction methods , 2021, Adv. Eng. Informatics.
[13] Shuai Su,et al. A DQN-based intelligent control method for heavy haul trains on long steep downhill section , 2021, Transportation Research Part C: Emerging Technologies.
[14] Colin Cole,et al. An Investigation of the Effect of Bogie and Wagon Pitch Associated with Longitudinal Train Dynamics , 2021, The Dynamics of Vehicles on Roads and on Tracks.
[15] Javier F. Aceituno,et al. Artificial neural networks applied to the measurement of lateral wheel-rail contact force: A comparison with a harmonic cancellation method , 2020 .
[16] E. Topal,et al. Transformation of the Australian mining industry and future prospects , 2020 .
[17] Shuai Su,et al. Date-driven approaches for modeling train control models: Comparison and case studies. , 2020, ISA transactions.
[18] Yuan Wang,et al. Reinforcement learning approach for optimal control of multiple electric locomotives in a heavy-haul freight train: A Double-Switch-Q-network architecture , 2020, Knowl. Based Syst..
[19] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[20] Francesco Corman,et al. Data-driven perspectives for energy efficient operations in railway systems: Current practices and future opportunities , 2018, Transportation Research Part C: Emerging Technologies.
[21] Maksym Spiryagin,et al. International benchmarking of longitudinal train dynamics simulators: results , 2018 .
[22] Jianjun Zhang,et al. A data-driven dynamics simulation framework for railway vehicles , 2018 .
[23] Maksym Spiryagin,et al. Parallel multiobjective optimisations of draft gear designs , 2018 .
[24] Ziyou Gao,et al. Research and development of automatic train operation for railway transportation systems: A survey , 2017 .
[25] G. Hua,et al. Contact analysis of Type17 coupler based on finite element method , 2017 .
[26] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[27] Paul Weston,et al. System energy optimisation strategies for metros with regeneration , 2017 .
[28] Maksym Spiryagin,et al. International benchmarking of longitudinal train dynamics simulators: benchmarking questions , 2017 .
[29] Ercan Oztemel,et al. Train 3D: the technique for inclusion of three-dimensional models in longitudinal train dynamics and its application in derailment studies and train simulators , 2017 .
[30] Ronghui Liu,et al. A multiphase optimal control method for multi-train control and scheduling on railway lines , 2016 .
[31] Maksym Spiryagin,et al. Design and Simulation of Heavy Haul Locomotives and Trains , 2016 .
[32] Rob M.P. Goverde,et al. Multiple-phase train trajectory optimization with signalling and operational constraints , 2016 .
[33] Amir Hossein Shamdani,et al. Simulation of longitudinal dynamics of a freight train operating through a car dumper , 2016 .
[34] Yuan Yao,et al. Dynamic performances of an innovative coupler used in heavy haul trains , 2014 .
[35] Xiangtao Zhuan,et al. Braking-Penalized Receding Horizon Control of Heavy-Haul Trains , 2013, IEEE Transactions on Intelligent Transportation Systems.
[36] Maksym Spiryagin,et al. Assessing wagon stability in complex train systems , 2013 .
[37] Rajeev Thottappillil,et al. An overview of electromagnetic compatibility challenges in European Rail Traffic Management System , 2008 .
[38] Colin Cole,et al. Simulated comparisons of wagon coupler systems in heavy haul trains , 2006 .
[39] Paul Curcio,et al. Ore-car coupler performance at BHP-Billiton Iron Ore , 2004 .
[40] Richard J. Beckman,et al. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.
[41] S. Hochreiter,et al. Long Short-Term Memory , 1997, Neural Computation.
[42] K. Park. An Improved Stiffly Stable Method for Direct Integration of Nonlinear Structural Dynamic Equations , 1975 .
[43] C. Cole,et al. iNEW method for experimental-numerical locomotive studies focused on rail wear prediction , 2023, Mechanical Systems and Signal Processing.
[44] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..