Electrocardiogram (ECG) signal classification is an important task in healthcare as it plays a vital role in early prevention and diagnosis of cardiovascular diseases. In this work, we propose an attention-based hybrid ResNet-Transformer network (ART-Net) for 12-lead ECG signal classification. It is comprised of a stacked multi-scale attention-based ResNet and self-attention-based Transformer. At first, ECG signals are divided into several signal segments with the same length. Then multi-scale features are extracted by attention-based Resnet through signal segments, and attention mechanisms are used to adjust the weight of different channel features based on their importance. Next, these multi-scale features from a same ECG signal are integrated in chronological order as input to the Transformer network. In this end, extracting and fusing contextual information based on self-attention mechanism, and extracting the correlation between beats at different positions. The experimental results on CPSC2018 indicate that our model outperforms three state-of-the-art methods, and achieve 85.27% of accuracy, 86.01% of sensitivity and 85.59% of specificity, respectively.

Traffic congestion frequently occurs on slope segments of highways, which is a typical bottleneck. With the development of connected and autonomous vehicle (CAV) technology, there will be a scene that CAVs and human driven vehicles (HDVs) co-exist. This work studies a slope bottleneck on highway in mixed traffic scenarios and proposes a traffic flow model for slope bottlenecks incorporating CAV platooning based on cellular automata. A novel traffic flow control strategy for slope bottlenecks is proposed based on variable speed limit (VSL) and vehicle platooning. Firstly, it divides the upstream section of the slope bottleneck into two zones for implementing VSL and vehicle platooning, respectively. Via speed restrictions within the VSL zone, the inflow of vehicles into the vehicle platooning zone is effectively mitigated to create low traffic density. In the vehicle platooning zone, a hybrid vehicle platooning method for mixed scenarios is proposed. The experimental results demonstrate that our strategy effectively enhances traffic flow of the slope bottleneck, thereby mitigating traffic congestion.

Two-Wheelers (TWs) such as bikes, e-bikes, and motorcycles often occupy lanes illegally and exceed speed limits, which leads to many traffic accidents. Therefore, we use deep reinforcement learning to design driving strategies for Autonomous Vehicles (AVs) to avoid collision with TWs and reduce injury of TW riders with irregular riding behaviors at un-signalized occluded intersections. First, the collision-avoidance behaviors of TWs are modeled, respectively. The state spaces integrate a safe avoidance range of AVs, a new position of AVs after taking deceleration and a steering angle, a predicted acceleration, and position, speed, and steering angle of AVs and other vehicles. At the same time, a reward function is designed based on the injury of TW riders and the driving safety and comfort of AVs. Secondly, a reinforcement learning model for autonomous driving strategy is constructed. Finally, Soft Actor-Critic is used to train the model, and the randomness policy is used to help AVs flexibly deal with the uncertain behaviors of TW riders and realize the balance between exploring unknown behaviors and using existing information. The simulation results show that compared with an autonomous emergency braking system, the injury of the riders using the driving strategy is reduced by 18.02% on average; compared with a risk-aware high-level decision strategy, the injury is reduced by 41.24% on average.

Abstract For semantic segmentation of cardiac magnetic resonance image (MRI) with low recognition and high background noise, a fusion‐attention Swin Transformer is proposed based on cognitive science and deep learning methods. It has a U‐shaped symmetric encoding–decoding structure with an attention‐based skip connection. The encoder realizes self‐attention for deep feature representation and the decoder up‐samples global features to the corresponding input resolution for pixel‐level segmentation. By introducing a skip connection between the encoder and decoder based on fusion attention, the remote interaction of global information is realized, and the attention to local features and specific channels is enhanced. A public ACDC cardiac MRI image dataset is used for experiments. The segmentation of the left ventricle, right ventricle, and myocardial layer is realized. The method performs well on a small sample dataset, for example, the pixel accuracy obtained by the proposed model is 93.68%, the Dice coefficient is 92.28%, and HD coefficient is 11.18. Compared with the state‐of‐the‐art models, the segmentation precision has been significantly improved, especially for the low recognition and heavily occluded targets.

Abstract In the medical image analysis domain, medical image segmentation has a significant impact on the quantitative analysis of organ or tissue function, as the first and critical component of diagnosis and treatment pipeline. In this paper, a dense R‐CNN segmentation model based on dual‐attention are proposed for medical images multi‐target instance segmentation. The model combines channel and spatial attention mechanism to extract image features and fuse multi‐scale feature information hierarchically. It combines up‐sampling strategies such as dilated convolution and bilinear interpolation to strengthen the distinguishability between multi‐target instances and pixel‐level features in other regions. The multi‐target detection mechanism of R‐CNN is combined with the multi‐scale feature extraction and fusion ability of dense convolution network. In the encoding stage, the multi‐scale hybrid bottleneck module and deformable convolution are introduced to extract more accurate structural feature information and increase the receptive‐field. In the decoding stage, the bilinear interpolation and the adaptive hierarchical fusion mechanism are used to strengthen the distinguishability between the target region and other regions, and improve the accuracy of instance segmentation. Taking cardiac MRI segmentation as an example, the left and right ventricles, and left ventricular myocardium are selected as segmentation targets. The pixel accuracy is 90.82%, the class pixel accuracy is 87.91%, the mean intersection‐over‐union is 81.52%, the Dice coefficient is 89.82%, and Hausdorff distance is 9.2, which is improved compared with other methods. It verifies the accuracy and applicability of the proposed method for multi‐target instance segmentation of medical images.