Abstract: A water-air integrated search and rescue system includes a flight power module, a navigation power module, a biomimetic boat module, a drone, and an integrated control module. The integrated control module is configured to acquire weather information and determine whether to send a flight signal to the flight power module or to send a navigation signal to the navigation power module based on the weather information; the flight power module is configured to fly the drone to a search and rescue region after receiving the flight signal; the navigation power module is configured to navigate the drone to the search and rescue region after receiving the navigation signal; the integrated control module is further configured to control the drone to deploy the biomimetic boat module; and the biomimetic boat module is configured to carry out search and rescue work in the search and rescue region.

Abstract: Disclosed is an intelligent collision avoidance method for a swarm of unmanned surface vehicles based on deep reinforcement learning; firstly, a theoretical framework of autonomous learning collision avoidance of a swarm of unmanned surface vehicles based on deep reinforcement learning is proposed, and the LSTM neural network memory ability is integrated to realize the continuity of collision avoidance actions; then, according to the USV environment in the framework, the characterization method is obtained, and the USV collision avoidance reward and punishment function is proposed to evaluate the collision avoidance effect; finally, an intelligent collision avoidance training system for a swarm of unmanned surface vehicles is formed. The simulation and verification of this disclosure show that the USV trained in this disclosure can navigate safely in the collision avoidance environment with a swarm of unmanned surface vehicles and realize intelligent collision avoidance.

Abstract: Systems and methods for improving magnetic resonance imaging relate to reconstructing multi-slice images based on sharing the strong structural similarities between adjacent image slices. In addition, a joint denoising method exploits these structural similarities. In part the reconstruction is based on use of a residual neural networks and denoising is achieved with a deep learning based strategy. The system and method have proved useful in both simulation and in vivo brain experiments, demonstrating significant noise reduction in all images and revealing more microstructural details in quantitative diffusion maps.

Abstract: The invention discloses an unmanned surface vessel (USV) formation path-following method based on deep reinforcement learning, which includes USV navigation environment exploration, reward function design, formation pattern keeping, a random braking mechanism and path following, wherein the USV navigation environment exploration is realized adopting simultaneous exploration by multiple underactuated USVs to extract environmental information, the reward function design includes the design of a formation pattern composition and a path following error, the path following controls USVs to move along a preset path by a leader-follower formation control strategy, and path following of all USVs in a formation is realized by constantly updating positions of the USVs.

Abstract: The present invention discloses an integrated automated driving system for a maritime autonomous surface ship (MASS).

Abstract: Image reconstruction methods for multi-slice and multi-contrast magnetic resonance imaging with complementary sampling schemes are provided, comprising: data acquisition using complementary sampling schemes between slices or/and contrasts) in spiral imaging or Cartesian acquisition; joint calibrationless reconstruction of multi-slice and multi-contrast data via block-wise Hankel tensor completion.

Abstract: Disclosed is an intelligent collision avoidance method for a swarm of unmanned surface vehicles based on deep reinforcement learning; firstly, a theoretical framework of autonomous learning collision avoidance of a swarm of unmanned surface vehicles based on deep reinforcement learning is proposed, and the LSTM neural network memory ability is integrated to realize the continuity of collision avoidance actions; then, according to the USV environment in the framework, the characterization method is obtained, and the USV collision avoidance reward and punishment function is proposed to evaluate the collision avoidance effect; finally, an intelligent collision avoidance training system for a swarm of unmanned surface vehicles is formed. The simulation and verification of this disclosure show that the USV trained in this disclosure can navigate safely in the collision avoidance environment with a swarm of unmanned surface vehicles and realize intelligent collision avoidance.

Abstract: The invention discloses an unmanned surface vessel (USV) formation path-following method based on deep reinforcement learning, which includes USV navigation environment exploration, reward function design, formation pattern keeping, a random braking mechanism and path following, wherein the USV navigation environment exploration is realized adopting simultaneous exploration by multiple underactuated USVs to extract environmental information, the reward function design includes the design of a formation pattern composition and a path following error, the path following controls USVs to move along a preset path by a leader-follower formation control strategy, and path following of all USVs in a formation is realized by constantly updating positions of the USVs.

Abstract: The present invention discloses an integrated automated driving system for a maritime autonomous surface ship (MASS).