Ship motion is an important influencing factor in passenger ship evacuation that affects the entire evacuation process by reducing individual walking speed. This study used Dalian Maritime University's training ship to conduct human walking experiments to study the influence of ship motion onNormal and fast walking speeds.It was found that during the berthing period, the individual Normal walking speed was 1.28-1.68 m/s, and the fast walking speed was 1.50-2.14 m/s. During the voyage, the ship's rolling motion reduced the Normal walking speed by 3.8%-10.3% and the fast walking speed by 3.7-14.0%. Due to the influence of ship rolling, the higher the deck and the farther away the rolling centre is, the smaller the athwartship and fore-aft walking speeds. Athwartship walking was slightly faster than fore-aft walking. In the Normal walking mode, the athwartship walking speed was 1.6%-3.7% faster than fore-aft walking, and in the fast walking mode, the athwartship walking speed was 0.8%-4.9% faster than fore-aft walking. During the berthing period, the average speed of the younger group was 24.1% higher than that of the older group. During the voyage, the reduction ratio of the individual walking speed was 86.0%-96.2%, and the value decreased as the deck height increased.
Probabilistic models are widely deployed in various systems. To ensure their correctness, verification techniques have been developed to analyze probabilistic systems. We propose the first sound and complete learning-based compositional verification technique for probabilistic safety properties on concurrent systems where each component is an Markov decision process. Different from previous works, weighted assumptions are introduced to attain completeness of our framework. Since weighted assumptions can be implicitly represented by multiterminal binary decision diagrams (MTBDDs), we give an >i<L>/i<*-based learning algorithm for MTBDDs to infer weighted assumptions. Experimental results suggest promising outlooks for our compositional technique.
Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread.To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients’ trajectory data. By using innovative space–time analytics, this framework reveals the spatiotemporal patterns of patients’ mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.
Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet* that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model.
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