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Harnessing the powerful learning and modeling capabilities of artificial intelligence, this study introduces a deep learning-driven wind data quality control algorithm that employs correlation verification rules. By constructing a Dual-Track Information Fusion Network (DTF-Net), it captures local temporal variations in wind speed via the temporal track and uncovers physical coupling relationships among temperature, pressure, w...
Accurate and robust localization is essential for intelligent vessels operating in complex marine and port environments. However, single-sensor localization is often affected by limited observation range, environmental occlusion, local interference, and sensor degradation. Although multi-sensor fusion can improve localization reliability, unknown cross-correlated measurement noise arising from shared disturbances, time synchro...
Ulva prolifera is the primary causative species of large-scale green tides in the Yellow Sea, posing recurrent threats to marine ecosystems and coastal economies. In recent years, research has undergone a fundamental shift from disaster control toward resource utilization. This multidisciplinary review synthesizes progress across the entire chain of green tide management, focusing on advances in monitoring and prediction. Thes...
Cavitating jet impingement is a key phenomenon in marine and ocean engineering that is responsible for cavitation-induced material erosion while also being harnessed for surface treatment applications. However, decoupling these concurrent effects is challenging since hydrodynamic jet pressure, microjet impacts, and shockwave emissions often coincide in space and time, making it difficult to isolate their individual contributio...
Precision measurement of the Ds*+-Ds+ meson mass differenceNARA Subscribed
PG-DyMamba: a physics-guided dynamic graph Mamba network for significant wave height predictionOA Marine
Accurate prediction of Significant Wave Height (SWH) is vital for marine engineering safety, yet balancing computational efficiency with physical consistency in long-sequence modeling remains a challenge for data-driven approaches. To address this, we propose the Physics-Guided Dynamic Graph Mamba Network (PG-DyMamba). By integrating oceanographic priors such as windwave relations, our Physics-Aware Graph Learner adaptively ca...
Based on the large volume of observational data obtained from Argo and several satellites, an increasing number of datasets are being developed and applied to oceanographic research. However, there are still problems such as sparse subsurface observations, insufficient parameters, and weak pertinence. This study provides a basic framework for high-resolution data fusion that focuses on the multi-source observations in the West...