Application of recurrent neural network to mechanical fault diagnosis: A review

J Zhu, Q Jiang, Y Shen, C Qian, F Xu, Q Zhu - Journal of Mechanical …, 2022 - Springer
With the development of intelligent manufacturing and automation, the precision and
complexity of mechanical equipment are increasing, which leads to a higher requirement for …

Regenerative braking system development and perspectives for electric vehicles: An overview

C Yang, T Sun, W Wang, Y Li, Y Zhang… - … and Sustainable Energy …, 2024 - Elsevier
Energy depletion and environmental pollution have always been challenges hindering the
rapid development of the automotive industry. Electric vehicles (EVs), being promoted …

Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles

Y Xing, C Lv, D Cao - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
Motion prediction for the leading vehicle is a critical task for connected autonomous
vehicles. It provides a method to model the leading-following vehicle behavior and analysis …

Fault detection and diagnosis of the air handling unit via combining the feature sparse representation based dynamic SFA and the LSTM network

H Zhang, C Li, Q Wei, Y Zhang - Energy and Buildings, 2022 - Elsevier
In recent years, slow feature analysis (SFA) has been successfully employed to deal with the
air handling unit (AHU) system's time-varying dynamic properties. However, since the …

Real-time driver cognitive workload recognition: Attention-enabled learning with multimodal information fusion

H Yang, J Wu, Z Hu, C Lv - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
Driver workload inference is significant for the design of intelligent human–machine
cooperative driving schemes since it allows the systems to alert drivers before potentially …

Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling

Y Xing, C Lv, D Cao, C Lu - Applied Energy, 2020 - Elsevier
Analyzing the energy consumption for road entities and the corresponding driving behaviors
are critical tasks for the realization of public traffic with a low energy cost and high efficiency …

Driver lane change intention recognition of intelligent vehicle based on long short-term memory network

L Tang, H Wang, W Zhang, Z Mei, L Li - IEEE Access, 2020 - ieeexplore.ieee.org
Driving intention prediction is one of the key technologies for the development of advanced
assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by …

Reliable estimation of automotive states based on optimized neural networks and moving horizon estimator

R Song, Y Fang, H Huang - IEEE/ASME Transactions on …, 2023 - ieeexplore.ieee.org
Accurate estimation of vehicle sideslip angle and attitude angles are essential for the safety
control and lateral behaviour of driving performance. In this article, the variation of wheels …

A cooperative driving strategy based on velocity prediction for connected vehicles with robust path-following control

Y Chen, C Lu, W Chu - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
The autonomous vehicles need to cooperate with the nearby vehicles to ensure driving
safety, however, it is challenging to plan and follow the desired trajectory considering the …

A survey of brake-by-wire system for intelligent connected electric vehicles

B Meng, F Yang, J Liu, Y Wang - IEEE Access, 2020 - ieeexplore.ieee.org
Intelligent connected electric vehicles (EVs) are widely considered as a trend in the global
automotive industry to make transportation safer, cleaner and more comfortable. As an …