Vessel-borne stabilization platforms can isolate vessel-borne equipment from vessel motions and ensure the operation safety of vessel-borne equipment. For a three-degree-of-freedom parallel vessel-borne stabilization platform, a wave compensation control hardware-in-the-loop (HIL) simulator is developed by using a personal computer (PC) and OP4510 real-time computer, as well as MATLAB and AMESim software for testing the performance of vessel-borne stabilization platform wave compensation controllers, which can reduce the test cost and the risk of sea trial, and shorten the development cycle of vessel-borne stabilization platform wave compensation controllers. The developed HIL simulator is used to test the performance of a joint space wave compensation stabilization controller as an example. The test results indicate that the joint space wave compensation stabilization controller can guarantee that the supporting surface of the vessel-borne platform is maintained at a desired horizontal position and orientation, while verifying that the developed HIL simulator can be used to test the performance of vessel-borne stabilization platform wave compensation controllers.

With the rapid development of science and technology, multi-unmanned ship systems have shown great potential in military, rescue and escort mission scenarios. The purpose of this paper is to explore the formation construction problem of multiple unmanned surface vehicle systems based on multi-agent deep reinforcement learning algorithm. Considering the sluggish convergence speed of the conventional multi-agent deep deterministic policy gradient algorithm (MADDPG), this study incorporates the attention mechanism into the value function stage to enhance the convergence speed of the formation decision model for a multi-UAV system. Through the cooperation of the formation model of the unmanned surface vehicle with the formation collision avoidance and the formation construction reward function, the efficiency of the multi-UAV to complete the formation construction task is finally improved. The simulation results conclusively demonstrate the efficacy of the proposed method in accomplishing multi-unmanned surface vehicle formation construction tasks, thereby establishing a solid theoretical foundation for future applications of multi-unmanned ship formation construction.

A fixed-time sliding mode fault-tolerant control strategy based on a disturbance observer is proposed for the cooperative encirclement control of multiple unmanned surface vehicles (USVs) with actuator faults and unknown environmental disturbances. The method is grounded in the hierarchical control concept, where the cooperative encirclement system is decoupled into a guidance layer and a control layer. Firstly, in the guidance layer, a fixed-time distributed cooperative control law combined with a sideslip angle observer (SO) is designed to achieve equidistant rotation around the target. Secondly, in the control layer, actuator faults are treated as disturbances affecting the layer, and a fixed-time disturbance observer (FxDO) is introduced to observe the nonlinear lumped disturbances caused by actuator faults and unknown environmental factors in real-time. A fixed-time sliding mode controller (FxSMC) is then designed to compensate for these disturbances, ensuring fast convergence of dynamic errors while smoothing the control output. Finally, the stability of the closed-loop control system is proven using the Lyapunov function, and the effectiveness of the proposed method in mitigating the adverse effects of actuator faults on the cooperative encirclement task is validated through a comparative simulation involving three USVs.

Aiming at the nonlinear and strong coupling characteristics of AUV 6-DOF motion, a Deep bidirectional Temporal Convolutional Networks based on dung beetle optimization (DBO) is proposed in this paper as a nonlinear system identification modeling method. First, a bidirectional Temporal Convolutional Networks (BiTCN), a bidirectional Gated Recurrent Unit (BiGRU), and an Attention mechanism (self-attention) are used to construct a Deep bidirectional Temporal Convolutional Networks (Deep-BiTCN) to establish a 6-DOF nonlinear black-box model of the AUV. Secondly, in order to improve the accuracy of Deep-TCN model prediction, this paper uses DBO algorithm to optimize the model hyper-parameters. Finally, the validity and feasibility of the motion modeling method in this paper are verified by comparing with support vector machine (SVM) and random forest (RF) model. The experimental results show that, compared with Deep-BiTCN, the root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of the DBO-Deep-BiTCN algorithm model are reduced by 58.94% and 49.22%, respectively, and the coefficient of determination (R2) is improved by 0.73%; the AUV 6-DOF motion model based on DBO-Deep-BiTCN has high accuracy and strong convergence, and avoids the motion nonlinearity leads to the problems of large forecast error and easy dispersion of the motion system under strong coupling, which can provide an effective strategy for AUV 6-DOF motion identification.

 In order to address the challenges encountered in intelligent ship global path planning using the DQN algorithm, such as paths being planned too close to obstacles, excessive turning points, large turning angles, and slow algorithm convergence, a method based on Noisy DQN (NoisyNet-DQN) for global path planning is proposed. Firstly, to maintain a safe distance between intelligent ships and obstacles, and to reduce path turning points and large turning angles, additional reward functions including heading reward, time reward, turning point reward, and safety reward are incorporated on top of the traditional reward function. Secondly, to tackle the slow convergence issue in complex navigation scenarios, parameter noise is introduced into the output layer of the DQN neural network, thereby enhancing the convergence speed of the DQN network. Simulation studies are conducted in the actual maritime environments of Dalian and Zhoushan. The simulation results indicate that compared to the traditional DQN algorithm, the proposed Noise-DQN algorithm significantly improves the convergence speed and, greatly enhances the safety and economy of the planned global path, better aligning with the actual navigation requirements of ships. The research results can provide a certain reference for global path planning in intelligent ship navigation.

To better guide ships in collision avoidance decision-making, a collision risk evaluation method considering the maneuvering capabilities of ships is proposed. By integrating AIS data, NOMOTO maneuvering motion equations, the COLREGS, and ship domain intrusion measurement model, two risk evaluation indicators related to the latest rudder application time and the safety avoidance angle set are established, namely Avoidance Difficulty (AD) and Avoidance Time Urgency (ATU), which evaluate the risk from spatial and temporal dimensions, respectively. Then, the CRITIC method is used to determine the weight of the two indicators. Finally, the effectiveness of the method is demonstrated through multi-risk comparison experiments in different scenarios using data from the outer waters of the Ningbo Zhoushan port area. The results indicate that the method takes into account the differences in maneuvering performance of various ships, the COLREGS, and the relative motion trends between ships, while paying special attention to the potential collision risk that may arise from avoidance maneuvers during the multi-ship risk assessment process, achieving a more reasonable evaluation of ship collision risk and has certain application value for ship collision avoidance decision-making.

Ship pilotage accident refers to the marine traffic accidents that occur in the process of the pilot manipulating the ship in the team of the ship's auxiliary steering station, and the human factor is the main cause of ship pilotage accidents. In order to better analyze the human factors in ship pilotage accidents, this paper establishes a new human factors analysis model for ship pilotage accidents, identifies and evaluates the transfer path of key risk factors in the ship pilotage process. Firstly, the original Human Factors Analysis and Classification System (HFACS) framework is adjusted and refined based on accident reports to identify human factors in pilotage accidents. Secondly, a HFACS-BN model of ship pilotage accidents is constructed based on the logical relationships within the HFACS framework and Bayesian network (BN) theory to verify the reliability of the model. Finally, the key risk factor transmission path is identified by integrating BN inference and sensitivity analysis. The results of the study showed that of all the human factor transmission pathways, the "R1 (behavioral error) → P2 (bad operator status) → S2 (improper operation plan) → M2 (bad organizational climate)" is the key risk factor transfer path. The implementation of effective control measures to address these factors is important to prevent the further development of risks. 

In order to improve the detection effect of water surface small objects in inland navigation scenarios and reduce the model’s complexity, a lightweight water surface object detection model based on improved YOLOv5l is proposed in this paper. The model takes YOLOv5l as the base model, firstly, the positioning loss function is improved, and the NWD-CIoU function is proposed to improve the recall rate of the model for small objects and increase the multi-scale object detection capability of the model. Secondly, combining FasterBlock module and C3 module, C3_Faster module is proposed to optimize the backbone network of YOLOv5l model, reducing network’s parameters and reducing model’s complexity. Thirdly, the slimming method is used to prune the model, greatly trim the redundant connections, reduce the model’s parameters and operational complexity, and improve the inference speed. Finally, based on the channel knowledge distillation method, the YOLOv5x was used as the teacher model to distill the pruned model to improve the detection effect of the model. The experimental results show that the proposed model has a better detection effect and speed for water surface objects in inland navigation scenarios, and compared with the original YOLOv5l model, the number of parameters decreases by 81.48%, and GFLOPs decreases by 80.69%, which is more suitable for deployment on mobile devices with limited computing resources, and thereby offering practical engineering significance.

This study addresses the challenges associated with high computational parameter volume, intricate complex background lighting, and the diversity of the ship's lights in the context of target detection. improvements have been made to YOLOv8n to meet the requirements for real-time and accurate identification of ship’s lights. Initially, we employ the VanillaNet as the core feature extraction network, which significantly diminishes the model's computational overhead and ensures compliance with real-time detection demands. Subsequently, we integrate a color attention module designed to discern and amplify the distinctive color attributes of the navigation lights, thereby bolstering their recognition even amidst complex lighting conditions. Furthermore, to accommodate the unique characteristics of the ship's lights, including their size, frequency, and spatial distribution across different perspectives, introduce a Mixture of Experts (MoE)-layer module as a substitute for the conventional C2f module, further refining the identification accuracy. Finally, the Focal Loss function is adopted to adjust the focus on easy-to-hard classified samples, addressing the class imbalance issue and improving the model's ability to detect ship’s lights. Experimental results demonstrate that, compared to the original baseline model YOLOv8n, the improved model reduces parameters and computation by 37.7% and 52.8%, respectively, while increasing accuracy and mAP@0.5 by 3.3% and 2.2% to 98.3% and 98.7%, respectively, and therefore the improved YOLOv8n meets the requirements for real-time identification of ship’s lights.

In order to solve the limitation on communication and computation resources, and complex wireless environments in maritime three-dimensional communication-computing converged networks, the computation offloading scheme for Intelligent Reflecting Surface (IRS) mounted on Unmanned Aerial Vehicle (UIRS)-assisted maritime communication-computing converged networks was studied. The offloading ratios of Unmanned Surface Vehicles (USVs), computation resource allocation of edge server, UIRS phase shifts and deployment were jointly optimized, aiming at minimizing the total energy consumption. Due to the high-dimensional coupled variables, based on the iterative optimization method, the original problem is decomposed into two subproblems, where the relaxation method and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm were used to solve the offloading ratios and communication-computing resources, respectively. Simulation results show that the proposed scheme can minimize the total energy consumption while satisfying the latency requirements, and perform superior performance under various scenarios. Moreover, in comparison with the scheme of without IRS, the total energy consumption in the proposed scheme is reduced by 42.0% on average.

Permanent magnet synchronous motor (PMSM) with advantages of high efficiency, high power density and stable operation is ideal core drive component for liquefied natural gas (LNG) pumps, where its electromagnetic performance is critical in cryogenic environments. This article firstly analyses the electromagnetic properties of key materials of cryogenic permanent magnet synchronous motors under the operating environment of −161 ℃. Then, a transient electromagnetic field finite element model of a cryogenic permanent magnet synchronous motor with a rated power of 6.5 kW is established. The electromagnetic performance and losses of the motor under cryogenic and room temperature environments are studied comparatively, and the influence of permanent magnet thickness on the performance of the cryogenic permanent magnet synchronous motor is analyzed Compared with the room temperature environment, the no-load back electromotive force and electromagnetic torque of the motor increase by 8.93% and 6.03%, respectively, at the temperature of −161 ℃; the stator iron loss increases by 2.5 times, the copper loss decreases by 65.3%, and the proportion of iron loss to total electromagnetic loss significantly increases.

Multi-phase permanent magnet fault-tolerant motors are often used in aviation, transportation and military fields which require high reliability of drive systems. Open circuit fault is a common electrical fault in motor drive system. Aiming at the problems that the existing fault diagnosis methods need to identify the open circuit fault of switch tube and winding with multiple sets of variables, and the change of motor running state is easy to lead to the misdiagnosis of threshold value judgment, this paper proposes a new phase voltage average value algorithm, which combines the motor drive system with the current mode flux observer. and switches the diagnosis variables from fault complex identification to a group of variables identification, and from threshold range diagnosis to a group of positive and negative variables the different open circuit faults of switching tube and winding can be distinguished by the diagnostic results. The experimental platform for fault diagnosis of six-phase permanent magnet fault-tolerant motor is built, and the experimental results verify the effectiveness and superiority of the proposed algorithm.

 This paper studies the exporting offshore/in-transit inventory financing risk problem with high demand uncertainty. Applying the principle of double Stackelberg master-slave game, profit functions of importer, exporter and shipping company were constructed. Employing the conditional value at risk (CVaR) measurement method, an optimization model of exporting offshore/in-transit inventory financing under risk measurement was established, where the overall profit of the supply chain was maximized. Then, the optimal decision-making results such as the pledge rate of the shipping company, the exporter's delivery lead time and the importer's order quantity were demonstrated and analyzed. The study shows that the importer's order lot size and the exporter's delivery lead time decrease with the increase in the pledge rate of the maritime logistics company. The optimal pledge rate of the shipping company is negatively related to the risk aversion coefficient of the exporter, the pledge service fee rate of the shipping company, and the market selling price of the pledged goods, and positively related to the risk aversion coefficient of the importer and the import price of the importer. The findings of the research can provide useful decision-making references for all parties involved in exporting offshore/in-transit inventory financing. 

Freight forwarding alliances often have incomplete or uncertain information in the actual revenue allocation, such as ocean freight rate fluctuation. In order to effectively solve the problem of revenue allocation strategy of freight forwarding alliance under the fuzzy situation, the trapezoidal fuzzy number improved Shapley value is created. This cooperative game solution enhances the stability of the freight forwarding alliance and its market competitiveness by equalizing the satisfaction of freight forwarding enterprises. In the fuzzy situation, the satisfaction of the players is maximized by minimizing the excess contribution, according to which the trapezoidal fuzzy number least squares contribution is calculated and used to replace the marginal contribution of the classical Shapley value, thus creating the trapezoidal fuzzy number improved Shapley value. The example results show that the trapezoidal fuzzy number improved Shapley value fully considers the satisfaction of the players in the game, which makes the revenue allocation strategy more fair and reasonable. The trapezoidal fuzzy number improved Shapley value can effectively solve the problem of revenue distribution strategy of cooperative alliance with fuzzy uncertainty such as freight forwarding alliance, and then promote the stable and sustainable development of cooperative alliance.

To investigate the influence of sediment soil parameters to pile foundations of piled wharf to force deformation, a numerical computational model of the interaction system of bank slope and piled wharf is established, Firstly, extensive fiducial error is used to identify the key siltation stages during bank slope siltation process. Furthermore, the Sobol’ and TGP global sensitivity analysis method are also employed to analyze the sensitivity of the sediment soil parameters to the force deformation of pile foundations. On this basis, a normalized sensitivity index (SVI) method is utilized to determinate the importance of cohesion, friction angle, Young’s modulus, and Poisson's ratio in influencing the force deformation of the pile foundations. The computational results show that the Young’s modulus is the primary driving factor for affecting the force deformation of pile foundations. And the sensitivity of the pile foundation to horizontal displacement at the mudline is not influenced by sediment thickness, with the importance ranking as follows: Young’s modulus, cohesion, Poisson's ratio, and friction angle. The quantitative findings of this study contribute to a deeper understanding of the influence of sediment soil parameters on the force deformation of pile foundations during bank slope siltation process, which can provide valuable theoretical references for the engineering design and reliability assessment of the interaction system of bank slope and piled wharf. 

Bank slope siltation and pile foundation cracking damage are common issues in coastal port high-piled wharves in China, posing a significant threat to the safe operation of the wharves. To implement effective maintenance measures, such as bank slope desilting and pile repair, the primary task is to understand the cracking mechanism of wharf piles under bank slope siltation. The paper investigated the siltation status of bank slopes of 16 major coastal ports in Jiangsu and Zhejiang regions, as well as the cracking of pile foundations of 7 typical wharves. Based on specific engineering, finite element models of the interaction between silted bank slope and wharf pile foundations were established, and the force and deformation characteristics of the pile-soil system were analyzed. The plastic deformation state of the pile foundation, as determined by the internal force combination value, was largely consistent with the actual location of the wharf pile cracking axis. This confirmed the validity of the modeling approach. A total of seven factors were selected for orthogonal testing in terms of both the engineering geological conditions of the bank slope and the structural parameters of the piled wharf, and the sensitivity of each factor to the internal force of the pile foundation was determined using extreme deviation analysis. The main conclusions are as follows. In marine environment, sediment continues to silt back under and behind piled wharf can cause sustained deformation of soft soil layers, leading to the passive pile problem. As the bank slope is silted, the soil beneath the wharf moves significantly towards the sea, causing large horizontal displacement of neighboring piles. This leads to a surge in the axial force and bending moment of the pile foundations, which in turn induces the top or the maximum pile displacement of some piles to exceed the limit of elastic-plastic deformation. Moreover, during the siltation process, the shoreward sloping piles at the rear of the wharf have greater additional internal forces, making them more susceptible to cracking and damage. The axial force and bending moment at the top of the piles under siltation conditions are more sensitive to the structure parameters and the slope of the bank slope.

In order to explore the influence mechanism of cleaning agent on the cleaning effect of chemical tanker during tank washing, palm oil cleaning experiment was carried out based on self-built experimental platform. Combined with FLUENT numerical simulation, the influence of cleaning water temperature and ethanol concentration in cleaning water on the cleaning effect of palm oil was systematically analyzed. The results showed that with the increase of cleaning water temperature, the adhesion of residual palm oil was decreased, and the cleaning effect of residual palm oil on the wall was accelerated at the initial stage of washing (0-8s). The cleaning speed of cleaning water at 30℃ and 40℃ was 3%/s and 6%/s, respectively, which was significantly higher than that of cleaning water at 20℃. With the increase of the concentration of cleaning agent added to the rinsing water, the viscosity of the oil alcohol mixture is critically affected. In the early stage of rinsing (0-8s), the higher concentration of cleaning water can effectively reduce the viscosity of the palm oil-ethanol mixture, thus improving the cleaning effect. With the extension of washing time (8-15s), the influence of cleaning water concentration on cleaning effect is gradually weakened. The influence degree of cleaning water temperature and concentration on the cleaning effect is compared, and it is found that the cleaning water concentration has a great influence on the cleaning effect at 0-2s and 12-15s, and the cleaning water temperature plays a key role in the cleaning effect at 5-10s. In order to reduce the consumption of cleaning agent and shorten the cleaning time, it is recommended to use cleaning water mixed with high concentration of cleaning agent to quickly flush the chemical tank during the pre-washing stage. After pre-washing, rinse the chemical tank with hot water.