Lag in detecting ship heave motion signals severely affects the performance of ocean heave compensation systems. Therefore, accurate heave motion prediction can effectively improve the stability and real-time performance of these systems. To improve the engineering practicability of a heave motion prediction model, we designed an autoregressive time-series model featuring high calculation efficiency, simple programing, and a small accumulation error. Moreover, to further address the poor adaptability of the model to nonlinear and nonstationary complex sea conditions and long-term predictions, we developed a combined prediction model based on wavelet transform and improved autoregression using the wavelet multiscale analysis method and achieved online multistep prediction of heave motions by decomposing and transforming historical data, reconstructing sub-sequence prediction, and forecasting data synthesis. Finally, theoretical testing and experiments were conducted on stationary random waveforms and nonstationary waveforms measured on ships. The analysis results show that the combined model exhibits good prediction performance and can effectively reduce the control error of the ocean heave compensation system caused by the lag in the heave motion signal detection.

To achieve the best separation effect and oil phase collection efficiency, a self-induced vortex oil collector was designed to collect residual oil from the turbulent sea. The inlet flow angle and suction pipe insertion depth of the device were adjusted and optimized via numerical simulation calculations. By comparing the volume of oil phase remaining inside the device in the same operation time, we concluded that at an inlet flow angle of 20° and a suction pipe insertion depth of h/3, the device could maintain a high oil phase separation efficiency, suppress oil-water mixing, and reduce oil-water interface diffusion and impurities. After determining the optimal structure, we analyzed the oil removal effect of the device in different water surface environments by changing its inlet flow velocity. The higher the inlet flow velocity, the higher the performance of the device for collecting the oil phase and better its oil removal effect. In addition, the entire oil collection process occurs inside the device without being affected by the external environment, suggesting that the device can collect oil from complex water surface environments. Moreover, the main body of the device has no moving parts, hence, it relies solely on the baffle to guide the swirl for collecting the oil phase.

Real-time, accurate and reliable monitoring of marine environmental information plays a crucial role in marine disaster warning and prediction, disaster prevention and reduction, marine resource development, and ensuring marine safety. In recent years, with the continuous development and upgrading of global navigation satellite systems (GNSS), the detection of atmospheric and marine environmental information based on GNSS navigation signals has become a new method and a hot research topic in the marine environmental monitoring technology. This method has been widely applied to domains such as marine meteorological monitoring and numerical forecasting. This article systematically reviews the current research status of the GNSS technology in marine environmental monitoring, including effective wave height, wind speed, rainfall intensity, water vapor and tide level monitoring. Furthermore, this paper systematically summarizes new technologies and methods and looks forward to provide reference for the future research in related fields.

Underwater biological object detection is crucial for aquaculture, endangered species protection,and ecological environment monitoring. This study comprehensively analyzes the applications of various deep learning methods in underwater biological object detection. The commonly used underwater biological object detection datasets are introduced. The state-of-the-art underwater biological object detection methods are classified, analyzed, and summarized by two stages and one stage. The actual applications of various detection methods are thoroughly described, and the advantages and disadvantages of their optimization strategies are analyzed and summarized. Future works in the field of underwater biological object detection based on deep learning are presented. This study provides a reference basis for researchers in the field of underwater biological object detection.

Water dynamics analysis was conducted on a compact and portable autonomous underwater vehicle(AUV) with side-scan sonar and amodified AUV with streamlined side-scan sonar. The analysis focused on examining the drag forces experienced by both AUVs at different speeds. The results demonstrated that the streamlined side-scan sonar effectively reduced pressure and viscous drag forces, resulting in an overall drag reduction of 15.4% at a normal speed of 3 knots, with a 9% reduction in viscous drag and an 18% reduction in pressure drag.At a high speed of 6 knots, the overall drag was reduced by 10.1%, with a 4.2% reduction in viscous drag and a 12% reduction in pressure drag. These findings demonstrate that optimizing the streamlined design of the AUV with side-scan sonar can effectively enhance the dynamic performance of the AUV, reduce its drag force, and improve its efficiency and performance.

This study proposes an efficient wave sensor fault diagnosis method based on wavelet packet decomposition, dimension reduction, and k-nearest neighbor algorithm(KNN) classification network to address the difficulty of wave sensor fault diagnosis, unidentifiable fault types, and time-consuming diagnosis. First, the standard deviation of the original signal is normalized. The normalized data is then subjected to a three-layer wavelet packet decomposition. The extracted feature vectors represent normalized data from the eight bands on layer 3. The second step involves using the t-distributed stochastic neighbor embedding (t-SNE) algorithm to reduce the dimension of the feature data. Finally, the dimension-reduced feature data is input into the KNN classification network for fault classification and detection. Experimental results show that the proposed method can improve the accuracy and diagnosis speed of the wave sensor fault diagnosis, with a diagnosis accuracy of up to 93.55% for normal and six faulty conditions.