Sea fog is a common weather phenomenon at sea, which reduces visibility and poses a great threat to marine traffic and operations. Traditional satellite remote sensing sea fog monitoring algorithms need to be improved in terms of accuracy, portability and automation. In this paper, a sea fog prediction model based on a Bidirectional Temporal Convolutional and Long Short-Term Memory Networks is proposed (BiTCN-LSTM). The BiTCN-LSTM introduces a network of causal convolutional and bidirectional gated recurrent cells, which improves the network's attention to sea fog features of important channels by learning forward and backward convolutional features of the input sea fog sequence. In addition, residual multi-scale feature fusion is employed to obtain multiscale information of sea fog, allowing the model to extract and fuse features progressively at different levels for better prediction of relative humidity and visibility time series of sea fog. Experiments show that the proposed BiTCN-LSTM has excellent performance in terms of long-term sea fog humidity and visibility prediction.
In Chinese medicine, eye diagnosis is essential for diagnosis and treatment. However, most current image-processing techniques focus on tongue diagnosis, and most foreign studies on ocular diagnosis focus on segmenting fundus vascular images. Moreover, most of the foreign studies on scleral vessels are focused on identification rather than on TCM discernment. Scleral vessels can significantly characterize the pathological features of the human body’s five internal and six internal organs. Scleral vessels are essential for the objective study of TCM visual diagnosis. However, due to the small size and complex structure of scleral vessels, it is difficult to extract them by existing methods effectively. To achieve more accurate scleral blood vessel extraction, we introduce the residual connection structure and CA-Module attention mechanism in the U2Net1 network to avoid the incompatibility between high-level and low-level features and enhance the information extraction by input fusion and feature extraction of RSU blocks. The experimental results show that Miou achieves an accuracy of 83.3%. The F1-score reaches 91.7%, which is more effective than the existing SOTA fundus vascular segmentation network FR-UNet2 for the experiments. According to the experimental results, Res-U2Net can segment sclerar vessels accurately. In future experiments, we will improve the vessel feature extraction network to increase its accuracy and gradually achieve better results.
Image segmentation is a critical technology in many fields, such as image processing, pattern recognition, and artificial intelligence. It is also the first and critical step in computer vision technology. Tongue diagnosis combined with deep learning for segmentation and extracting pathological features is relatively mature, but deep learning combined with TCM visualization is sporadic. First, We used the U2Net network1 for segmentation extraction of the sclera in this study. Where the U2Net1 network1 (based on PyTorch) relies on the extensive use of data enhancements to use the available annotation samples more efficiently, and compared with the U-Net network, the U2Net network1 updates an RSU module, each RSU module is a small U-net network,merging multiple U-Net outputs to get the merged Mask target. Finally, we applied classical CNN networks to evaluate the segmentation effect, introducing different evaluation metrics such as Miou, Precision, and Recall. We used the publicly available dataset UBIVIS.V12 for our experiments, where our Miou was as high as 97.3%, and U2Net achieved better results among all the networks, which laid the foundation for our subsequent segmentation and extraction of blood filament features.
KEYWORDS: Signal to noise ratio, Oceanography, Receivers, Cognitive modeling, Transmitters, Telecommunications, Sensors, Wireless communications, Received signal strength, Mobile communications
It has been recognized that power control strategy of spectrum sharing for USVs(unmanned surface vessels) counts. The policy for power control of a major USV is proposed to increase profit of spectrum resources. In the environment of an USV sailing on the sea, it is essential for the major USV to communicate effectively with the minor USV. We propose a learning-based power control approach for the major USV to share the joint spectrum with the minor USV. Specifically, the major USV updates its transmitted power abide by a power control policy that is defined beforehand. The minor USV is unaware of the major USV’s condition of transmitting power. The proposed method exploiting the strengths of the Deep Q-Network (DQN), could help the minor USV adjust its transmitting power and make full use of the empty spectrum. Both can transmit information favourably accompanied by the satisfactory service quality. The results of our experiment show that the minor USV communicate with the major USV effectively through our method. Compared to the traditional reinforcement learning method DQN, our method achieves better results.
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