Search Results for author: Teng Liu

Found 20 papers, 0 papers with code

MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals

no code implementations15 Jun 2023 Donghong Cai, Junru Chen, Yang Yang, Teng Liu, Yafeng Li

Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain.

EEG Seizure Detection +1

Minimization of ion micromotion with artificial neural network

no code implementations3 Mar 2021 Yang Liu, Qi-feng Lao, Peng-fei Lu, Xin-xin Rao, Hao Wu, Teng Liu, Kun-xu Wang, Zhao Wang, Ming-shen Li, Feng Zhu, Luo Le

Minimizing the micromotion of the single trapped ion in a linear Paul trap is a tedious and time-consuming work, but is of great importance in cooling the ion into the motional ground state as well as maintaining long coherence time, which is crucial for quantum information processing and quantum computation.

Atomic Physics Quantum Physics

Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon

no code implementations26 Aug 2020 Hao Chen, Xiaolin Tang, Teng Liu

Decision-making strategy for autonomous vehicles de-scribes a sequence of driving maneuvers to achieve a certain navigational mission.

Autonomous Vehicles Decision Making +1

Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning

no code implementations14 Aug 2020 Feng Wang, Dongjie Shi, Teng Liu, Xiaolin Tang

Decision-making module enables autonomous vehicles to reach appropriate maneuvers in the complex urban environments, especially the intersection situations.

Autonomous Vehicles Decision Making +3

A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles

no code implementations4 Aug 2020 Teng Liu, Yuyou Yang, Wenxuan Xiao, Xiaolin Tang, Mingzhu Yin

Deep reinforcement learning (DRL) has emerged as a pervasive and potent methodology for addressing artificial intelligence challenges.

Autonomous Driving Decision Making +4

Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving

no code implementations26 Jul 2020 Teng Liu, Yang Xing, Long Chen, Dongpu Cao, Fei-Yue Wang

The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles.


Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning

no code implementations24 Jul 2020 Teng Liu, Wenhao Tan, Xiaolin Tang, Jiaxin Chen, Dongpu Cao

This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology.

energy management Management +3

Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles

no code implementations24 Jul 2020 Teng Liu, Bo wang, Dongpu Cao, Xiaolin Tang, Yalian Yang

As the core of this study, model predictive control and reinforcement learning are combined to improve the powertrain mobility and fuel economy for a group of automated vehicles.

Autonomous Vehicles Decision Making +3

Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications

no code implementations21 Jul 2020 Teng Liu, Xing Yang, Hong Wang, Xiaolin Tang, Long Chen, Huilong Yu, Fei-Yue Wang

The three virtual vehicles (descriptive, predictive, and prescriptive) dynamically interact with the real one in order to enhance the safety and performance of the real vehicle.


Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition

no code implementations16 Jul 2020 Teng Liu, Xiaolin Tang, Jiaxin Chen, Hong Wang, Wenhao Tan, Yalian Yang

Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission reduction.

Computational Efficiency energy management +3

Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences

no code implementations16 Jul 2020 Hao Chen, Xiaolin Tang, Guo Hu, Teng Liu

With online and real-time requirements in mind, this article presents a human-like energy management framework for hybrid electric vehicles according to deep reinforcement learning methods and collected historical driving data.

energy management Management +2

Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study

no code implementations16 Jul 2020 Teng Liu, Xingyu Mu, Xiaolin Tang, Bing Huang, Hong Wang, Dongpu Cao

This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL).

Autonomous Vehicles Decision Making +2

Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle

no code implementations16 Jul 2020 Xiaowei Guo, Teng Liu, Bangbei Tang, Xiaolin Tang, Jinwei Zhang, Wenhao Tan, Shufeng Jin

This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL).

energy management Management +3

Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles

no code implementations16 Jul 2020 Teng Liu, Bin Tian, Yunfeng Ai, Long Chen, Fei Liu, Dongpu Cao

As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and control.

Autonomous Vehicles Decision Making +2

Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review

no code implementations16 Jul 2020 Teng Liu, Wenhao Tan, Xiaolin Tang, Jinwei Zhang, Yang Xing, Dongpu Cao

This paper focusing on helping the relevant researchers realize the state-of-the-art of HEVs energy management field and also recognize its future development direction.

energy management Management

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