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Found 8 papers, 3 papers with code
Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning
Experimental results showcase that this semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis attains high accuracy in segmentation and correctness in prediction, demonstrating substantial practical guidance and application value.
Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization
Therefore, a risk-manageable PM investment framework integrating both RL and barrier functions (BF) is proposed to carefully balance the needs for high returns and acceptable risk exposure in PM applications.
Channel prior convolutional attention for medical image segmentation
An efficient Channel Prior Convolutional Attention (CPCA) method is proposed in this paper, supporting the dynamic distribution of attention weights in both channel and spatial dimensions.
Remote Identification Trajectory Coverage in Urban Air Mobility Applications
As Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) continue to mature, a safety-critical system that will need to be implemented in tandem is Remote Identification (Remote ID) for uncrewed aircraft systems (UAS).
Complementary consistency semi-supervised learning for 3D left atrial image segmentation
A network based on complementary consistency training, called CC-Net, has been proposed for semi-supervised left atrium image segmentation.
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement Learning
Safety guarantee is essential in many engineering implementations.
Sum-of-Squares Program and Safe Learning On Maximizing the Region of Attraction of Partially Unknown Systems
In this way, we maximize the estimated region of attraction of partially unknown nonlinear systems, while guaranteeing both safety and stability.
On Estimating the Probabilistic Region of Attraction for Partially Unknown Nonlinear Systems: An Sum-of-Squares Approach
Estimating the region of attraction for partially unknown nonlinear systems is a challenging issue.
