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MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals
Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain.
Minimization of ion micromotion with artificial neural network
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
First constraints on the AGN X-ray luminosity function at $z \sim 6$ from an eROSITA-detected quasar
We report the X-ray detection of an eROSITA source securely matched to the well-known quasar SDSS J083643. 85+005453. 3 (z=5. 81).
Astrophysics of Galaxies High Energy Astrophysical Phenomena
Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning
For specification, many source driving cycles are utilized for training the parameters of deep network based on PPO.
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles
Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles.
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
Decision-making strategy for autonomous vehicles de-scribes a sequence of driving maneuvers to achieve a certain navigational mission.
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning
Decision-making module enables autonomous vehicles to reach appropriate maneuvers in the complex urban environments, especially the intersection situations.
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles
Deep reinforcement learning (DRL) has emerged as a pervasive and potent methodology for addressing artificial intelligence challenges.
Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving
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
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology.
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles
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.
Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications
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.
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle
This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL).
Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review
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.
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition
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.
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
The advantages of the proposed framework in convergence rate and control performance are illuminated.
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL).
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles
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.
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment
As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years.
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences
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.
