To suppress hydrodynamic noise at the source, a noise reduction method for pump-jet propulsors based on porous media is proposed.

By replacing the metallic leading edges of the stator blades of the pump-jet propulsor with porous materials, the interaction between the blade wake and the inner wall of the duct can be effectively modulated, thereby reducing wall pressure fluctuations. Large eddy simulation (LES), combined with acoustic analogy analysis, was employed to investigate the flow characteristics and noise control performance of the stator blades with porous leading edges. The mechanisms by which the porous media modulates the flow field and suppresses noise were analyzed, and the effects of key parameters, such as porosity and advance coefficient, on hydrodynamic noise control were examined.

Comparative results indicate that the porous leading edges of the stator significantly reduce the low-frequency sound pressure level components on the duct wall and the far-field radiation noise. The maximum reduction in the sound pressure level (SPL) reaches 5.52 dB in the direction perpendicular to the rotation axis of the pump-jet propulsor.

The findings of this study provide useful guidance for flow control and hydrodynamic noise reduction in pump-jet propulsors.

To address the challenge of low fault diagnosis accuracy in traditional neural networks with few labeled samples, a method based on contrastive learning and convolution transformer network is proposed.

First, raw monitoring data are transformed into similar sample pairs by data augmentation. These similar sample pairs are then mapped to a deep feature space by a feature extractor. A transformer network is utilized to design cross-prediction tasks for both local and global comparisons, facilitating the clustering of data with the same fault type by comparing the intrinsic similarity between the same batches of data. Finally, the downstream classification network is trained with few labeled samples to improve the diagnostic performance of the proposed model.

The effectiveness of the proposed method is validated using a self-built reducer test rig. The results show that accuracy of the proposed method reaches 98.38% with few labeled samples, showing significant advantages over existing methods.

The research results can provide the key technology for fault diagnosis of industrial equipment with few labeled samples, contributing to the advancement of intelligent manufacturing.

To address the defect that the empirical formula of sea-spray flux depends on observation data from specific ships, this study proposes a correction method for the empirical formula of sea-spray flux on polar ship decks.

By analyzing the parameter characteristics of the empirical formula, it is proposed to modify the empirical formula of sea spray flux using numerical solutions of sea-spray flux for different ship types and corresponding environmental conditions as inputs. The reliability of the numerical method is verified through numerical wave profile and MFV fishing vessel spray flux calculations. Based on the proposed method, the empirical formulas of sea spray flux for four different ship types, such as bulbous bow, ice-resistant bow, clipper bow and raked bow, are modified.

The theoretical and numerical solutions of the empirical formula for fishing vessel spray flux are in good agreement. The spray duration coefficients for the other four ship types are similar to each other, and the liquid water content coefficient is similar to the coefficient in the existing empirical formula.

The results show that the proposed method has the ability to correct the empirical formula of sea-spray flux for different ship types.

This paper introduces a novel data-driven approach for generating realistic and hazardous overtaking scenarios. These scenarios are crucial for rigorously evaluating the autonomous collision avoidance capabilities of autonomous ships. Existing methods often struggle to balance scenario diversity, realism, and the representation of hazardous situations. To overcome this limitation, our method leverages the rich information embedded in automatic identification system (AIS) data to generate diverse and realistic overtaking encounters.

Specifically, we propose a hybrid model that integrates a sequence generative adversarial network (SeqGAN) with a self-attention mechanism (SAM). The SeqGAN captures the complex patterns and dynamics in AIS-based ship trajectories, enabling the generation of novel, yet plausible, overtaking maneuvers. The incorporation of a SAM further enhances the model's ability to capture long-range dependencies in ship trajectories, resulting in more realistic and nuanced simulations. To ensure that the generated scenarios accurately reflect hazardous situations, we have developed a constraint model based on longitudinal and lateral safety distances between vessels to define realistic initial conditions. This model dynamically adjusts the initial positions and velocities of both the target vessel and the autonomous ship under test, ensuring that each generated scenario presents a genuine collision risk.

The results show that the effectiveness of our approach is validated through extensive simulations. A total of 500 hazardous overtaking scenarios were generated, significantly improving the coverage of test scenarios. Notably, 97.3% of these generated trajectories fall within a predefined buffer zone that encompasses real-world trajectories, demonstrating the high fidelity of our model. Furthermore, the speed distributions of the generated target vessels closely match those observed in real-world AIS data, further validating the realism of our approach.

The enhanced realism and diversity of scenarios generated by this method significantly improve the efficiency of autonomous collision avoidance testing. This allows for a more precise definition of safety performance boundaries and accelerates the development and optimization of autonomous collision avoidance algorithms. Ultimately, this work contributes to the development of safer and more reliable autonomous maritime systems capable of navigating the complexities of modern maritime environments.

Complex backgrounds, significant target size variations, and severe sea clutter in maritime infrared imagery often result in missed or false detections. To address this challenge, an improved method based on YOLOv8n, termed maritime infrared target detection-YOLO (MITD-YOLO), is proposed to enhance target detection accuracy in maritime infrared images.

MITD-YOLO incorporates a diverse branch module (DBB) and enhanced multi-scale convolution (EMSConv) to leverage multi-scale convolutions, enabling the model to more effectively capture complex features. A triple attention mechanism is employed to facilitate spatial and channel-wise feature interaction, thereby improving key feature extraction. Additionally, the powerful-IoUv2 (PIoUv2) loss function is introduced to address the anchor box expansion problem, leading to improved detection accuracy and enhanced model robustness.

Experimental results show that the improved model significantly enhances the efficiency of maritime infrared target detection, with a 2.3% increase in precision and a 1.7% increase in recall. The model achieves an average precision of 88.9%, and 132.8 FPS, outperforming the original model.

MITD-YOLO enhances maritime infrared target detection performance and provides a more reliable target detection technology for applications such as maritime surveillance and ship navigation, contributing to the advancement of intelligent maritime systems.

To accurately evaluate the impact of blanking on pulse interception, this study examines the loss probabilities of pulses caused by the blanking.

The influence of blanking on pulse sorting was analyzed. Based on the pulse sorting method used by reconnaissance equipment, the criteria for identifying the loss pulses were established. The output waveform of a pulse erased by the blanking gate was then examined under various time-sequence relationships between the pulse and the blanking gate, allowing the varying law of the residual pulse to be obtained. The expressions were derived for the average loss probabilities of the pulses and the loss probabilities of the periodic pulses both caused by the periodic blanking gates, as well as for the average loss probabilities of the pulses under blanking gates with time-varying duty cycles. Finally, each pulse loss probability was validated by comparing the calculation results with numerical simulation. The maximum absolute deviation between the two was approximately 5.8×10⁻⁴, which is negligible.

The mathematical models for the pulse loss probabilities and the average loss probabilities are obtained, and their accuracy has been verified.

The research provides a quantitative evaluation of the influence of the blanking on the loss of the intercepted pulse. It provides the input for accurately analyzing the missed alarm probability in pulse interception with the blanking gate and supports decision-making regarding the use of blanking measures.