A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
DRL-based resource allocation for computation offloading in IoV networks
Due to the dynamic nature of a vehicular fog computing environment, efficient real-time
resource allocation in an Internet of Vehicles (IoV) network without affecting the quality of …
resource allocation in an Internet of Vehicles (IoV) network without affecting the quality of …
Spatiotemporal Deep Learning for Power System Applications: A Survey
M Saffari, M Khodayar - IEEE Access, 2024 - ieeexplore.ieee.org
Understanding spatiotemporal correlations in power systems is crucial for maintaining grid
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
A deep reinforcement-learning approach for inverse kinematics solution of a high degree of freedom robotic manipulator
The foundation and emphasis of robotic manipulator control is Inverse Kinematics (IK). Due
to the complexity of derivation, difficulty of computation, and redundancy, traditional IK …
to the complexity of derivation, difficulty of computation, and redundancy, traditional IK …
Multi-agent DRL-based task offloading in multiple RIS-aided IoV networks
This article considers an internet of vehicles (IoV) network, where multi-access edge
computing (MAEC) servers are deployed at base stations (BSs) aided by multiple …
computing (MAEC) servers are deployed at base stations (BSs) aided by multiple …
Power allocation strategy for urban rail HESS based on deep reinforcement learning sequential decision optimization
X Wang, Y Luo, B Qin, L Guo - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
A hybrid energy storage system (HESS) is adopted to tackle the traction network voltage
fluctuation problem caused by high power and large energy demand during the starting and …
fluctuation problem caused by high power and large energy demand during the starting and …
Binocular vision-based motion planning of an AUV: A deep reinforcement learning approach
Vision-based motion planning of autonomous underwater vehicles (AUVs) is regarded as a
critical requirement for marine intelligent transportation systems. However, the limited vision …
critical requirement for marine intelligent transportation systems. However, the limited vision …
Deep stochastic reinforcement learning-based energy management strategy for fuel cell hybrid electric vehicles
Fuel cell hybrid electric vehicles offer a promising solution for sustainable and environment
friendly transportation, but they necessitate efficient energy management strategies (EMSs) …
friendly transportation, but they necessitate efficient energy management strategies (EMSs) …
Design of deep reinforcement learning controller through data-assisted model for robotic fish speed tracking
P Duraisamy, M Nagarajan Santhanakrishnan… - Journal of Bionic …, 2023 - Springer
It is common for robotic fish to generate thrust using reactive force generated by the tail's
physical motion, which interacts with the surrounding fluid. The coupling effect of the body …
physical motion, which interacts with the surrounding fluid. The coupling effect of the body …
[HTML][HTML] Optimal Energy Management Strategies for Hybrid Electric Vehicles: A Recent Survey of Machine Learning Approaches
Abstract Hybrid Electric Vehicles (HEVs) have emerged as a viable option for reducing
pollution and attaining fuel savings in addition to reducing emissions. The effectiveness of …
pollution and attaining fuel savings in addition to reducing emissions. The effectiveness of …