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Empirical Analysis of AI-based Energy Management in Electric Vehicles: A Case Study on Reinforcement Learning
The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function.
Progress and summary of reinforcement learning on energy management of MPS-EV
RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS.
Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model
Accurate traffic conditions prediction provides a solid foundation for vehicle-environment coordination and traffic control tasks.
Transfer Learning and Vision Transformer based State-of-Health prediction of Lithium-Ion Batteries
At the same time, transfer learning (TL) is introduced, and the prediction model based on source task battery training is further fine-tuned according to the early cycle data of the target task battery to provide an accurate prediction.
Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction
The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated.
A Transferable Intersection Reconstruction Network for Traffic Speed Prediction
Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models.
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN
In comparison with the previous non-learning adversarial example attack approaches, the GAN-based adversarial attack example approach can generate the adversarial samples quickly using the GAN architecture every time facing a new sample after training, but meanwhile needs to perturb the attack samples in great quantities, which results in the unpractical application in reality.
