In order to obtain a high performance unmanned surface vehicle (USV) navigation path, this paper proposes a multi-strategy improved sparrow search algorithm (MISSA). When planning a path for an unmanned surface vehicle, in addition to considering the distance factor, it is also necessary to reduce the number of turning times of the unmanned surface vehicle and avoid large steering angles. Therefore, in this paper：firstly, a fitness function with a steering angle penalty element is designed; secondly, the position update strategy is improved by using the golden positive selection method and the parameter self-helix setting. The problem of local optima is to obtain a global path with better fitness. The simulation results show that compared with the three excellent algorithms of improved A*, improved ant colony combined with genetics, and original SSA, the MISSA algorithm in this paper performs best in key performance indicators such as path distance, steering angle and steering times. It provides an effective way for autonomous and safe operation of USV.

This study aims to address the issue of collision avoidance in multi-ships encounter situations. Based on the the ship domain knowledge, international regulations for preventing collisions at sea (COLREGs), and ship maneuvering characteristics, we propose a decision-making method for collision avoidance in multi-ship encounter scenarios based on Deep Deterministic Policy Gradient (DDPG). The method utilizes a neural network model constructed with Gated Recurrent Units (GRU) and applies layer normalization to effectively process high-dimensional observation data, thereby improves the efficiency of the decision-making process. Moreover, the reward function designed in this study conforms to the GOLREGs while considering the common practice of using small rudder angles for maneuvering. The proposed collision avoidance method is validated through simulation experiments in various multi-ship encounter scenarios to demonstrate its flexibility and effectiveness.

This paper proposes a neural network sliding mode controller based on the robust line-of-sight (LOS) method for the path following of underactuated ship. This controller is designed to solve the problem of underactuated and ship model uncertainty. Firstly, the robust LOS is utilized to solve the problem of the ship’s input degrees of freedom dimension are fewer than the output degrees of freedom dimension, at the same time, this method reduces the influence of the differences in ship kinematics. Secondly, the controller is designed based on radial basis function neural network and sliding mode, this controller reduces the impact of external disturbances and achieves the path following of underactuated ships. Finally, the Lyapunov stability theory is applied to prove the stability of the guidance system and the control system. The guidance system is designed by robust LOS theory, and this guidance system reduces the influence of the differences in ship kinematics, at the same time, which reduces the influence of external disturbances. Through the simulation test of the straight path tracking and the curve path tracking of the underactuated ship, the simulations are carried out to verify the effectiveness and feasibility of the designed controller.

To improve the efficiency of drone detection of ship exhaust emissions, this study investigates the drone routing problem under the simultaneous movement of drones and ships. Considering the real-time changes in ship positions during monitoring, a "meeting" model is proposed to determine the meeting locations between ships and drones. Based on the meeting model, a drone path model is developed under the minimization of drones’ flying distance considering ship movement simultaneously ,enabling collaborative detection with multiple drones. Combine sequence insertion algorithm to design Bi-stage algorithms and a genetic algorithm based on two ship sequence decomposition strategies, DroneByDrone and ShipByShip ,to optimize drone paths in different scenarios. Through numerical studies ,the drone routing method considering drones’ flying and ships’ movements, can effectively improve the detection efficiency of moving ships. Both algorithms successfully solve the model, and compared to the Bi-stage algorithm, the genetic algorithm reduces the solution time by 65.12%, meeting the requirement for timely solutions. The drone flying distance is significantly sensitive to the number of drones. In the same dataset, dispatching two drones optimizes the flight distance by 25% compared to three drones, but the detection time is delayed by 8% accordingly ;By comprehensively optimizing the positions of drone base stations, the number of drones, and their speeds according to the experimental scenarios, the drone flight distance can be effectively reduced, leading to improved detection efficiency.

The thrust distribution method of the thruster is crucial for control scenarios such as autonomous berthing and low-speed maneuvering of unmanned vehicles. At present, the domestic research on thrust allocation algorithm is mainly based on the research and verification of the distribution method itself, and there is a lack of consideration for factors affecting the actual response speed, such as thrust change and angle change. Based on the generalized inverse method, this paper studies the thrust distribution problem of double-jet waterjet unmanned vehicles, and improves the generalized inverse method for the situation that the thruster execution time is rarely involved in previous method studies, and adds additional redistribution links to improve the response speed of the thruster. The simulation results show that the proposed method achieves the expected effect in theory.

Focusing on the technical problems of inland waterway level prediction in mountainous area，the environmental factors and technical difficulties affecting waterway water level prediction are analyzed，the current technical methods of waterway water level prediction model are summarized, and a new waterway water level prediction model Multi-Head Attention-Bidirectional Gated Recurrent Unit(MHA-BiGRU is innovatively proposed．The multi-head attention mechanism is introduced into GRU model，and the characteristic weights of important factors such as time and space of waterway water level series data are divided by the model, so that the model focuses on the key factors affecting waterway water level change.．Taking the downstream of Wujiang River as the research object, the model realizes the establishment of monitoring data set by building a real-time dynamic monitoring station for water level and flow velocity. The parameters such as MAE, RMSE and NSE are selected as evaluation indexes to verify the proposed model. The research results show that the model improves the performance of waterway water level prediction through the application of multi-head attention mechanism and bidirectional cyclic neural network. Compared with traditional classical time series prediction models such as LSTM and GRU, the model has better robustness and higher accuracy. Finally，the model is embedded into the system platform for demonstration application, and the real-time dynamic monitoring and short-term prediction of waterway water level are realized，which has high engineering application value.

According to address the issue of poor prediction accuracy caused by the nonlinear and non-stationary characteristics of short-term ship traffic flow data, this paper proposes an attention-based stacked LSTM ship traffic flow prediction model. The model primarily utilizes stacked LSTM neural networks to capture the temporal features of short-term ship traffic flow data, and introduces the attention mechanism to enhance the learning of global features and improve the accuracy of ship traffic flow prediction. The ship Automatic Identification System (AIS) data from three segments in the lower reaches of the Yangtze River are extracted and employed to construct the ship traffic flow datasets for training and testing the model. The results demonstrate that compared to baseline models such as HA, ARIMA, GPR, LSTM, and Seq2Seq, the model proposed in this paper reduces both the root mean square error and mean absolute error for predicting macro traffic flow parameters. This model demonstrates improved accuracy in ship traffic flow prediction compared to the optimal baseline model, achieving a reduction of 4.05% in root mean square error and 4.04% in mean absolute error.

In order to effectively manage the ship capsizing risk caused by liquefaction of liquefiable solid bulk cargo during sea transport, based on ship kinematics and wave energy theory, AWQA hydrodynamic analysis software was used to simulate and analyze the external environmental loads encountered by ships while sailing in various typical sea areas. According to the theory of soil dynamics and results of triaxial tests, the equivalent analysis of the liquefaction risk of the cargo and the related assessment model were carried out. On account of the assessment model and numerical calculation samples, BP propagation neural network algorithm was applied to construct the predict model, which realized the quick intelligent assessment of the liquefaction risk of cargo shipping. The dominant expression of the BP quick predict model was obtained through the weight matrix and threshold matrix of the model, and the predict model was used to quick assess the liquefaction risk of four typical liquefiable solid bulk cargos under shipping environment. The predict results were in good agreement with the assessment results. This risk predict model can quickly and effectively assess the liquefaction risks of solid bulk cargo according to the parameters such as ship type and sea conditions, which provides an effective method for the front-line maritime supervision, and a good promotion for the development of IMSBC Code.

 To explore the causal path of various water traffic accidents in China Coast, a causal path analysis method for water traffic accidents based on Human factors analysis and classification system (HFACS) model, Bayesian network (BN) model and path model was proposed. Firstly, based on the HFACS model, 5 levels including 38 causal factors for water traffic accidents were identified from the investigation report of water traffic accidents in China Coast. Secondly, the K2 algorithm was used for BN structure learning, and combined with chi-square test results and prior knowledge to determine the BN structure. Next, the maximum expectation algorithm was utilized for BN parameter learning. Then, the sensitivity analysis method was used to extract the causal path of various water traffic accidents. Finally, the path model was applied to calculate the causal path coefficients of various water traffic accidents and test their statistical significance. The results indicate that there are respectively 7, 6, 3, 7, 4, 4, and 2 significant causative pathways for accidents such as collision, sinking, contact, grounding, fire/explosion, wind strike, and stranding, in China Coast. From the perspective of effect level of causal path, the largest causal path of both contact accidents and stranding accidents is “improper allocation of chart data → improper navigation plan → contact/stranding accident”.

Tagline type of anti-swing device of marine crane is a new type of crane load reduction equipment. By controlling three traction cables, the energy of the swing of the load is consumed and the swing of the load is reduced. However, it is found in the real ship test that the traction cable is easy to produce vibration when working, which leads to the decrease of anti-roll effect, and reduces the stability and safety of the system.In this paper, based on catenary theory and chord vibration principle, the vibration model of traction cable is established, and the simulation analysis is carried out with Matlab. The vibration of the traction cable in practice is simulated by Adams, and the correctness of the model is verified by comparison.The results show that the hysteresis of the hydraulic system will lead to the vibration of the traction cable. When the response time of the hydraulic system is reduced from 1s to 0.1s, the amplitude of the traction cable is reduced by 90%. In addition, the unit mass and length of the traction cable, whether to install a damper and other factors will have an impact on the vibration, adding a damper can reduce the amplitude of the traction cable in 4s by 97%. The research results of this paper have certain guiding significance for the application of tagline type of anti-swing device of marine crane.

In order to solve the problem that there is a large gap between frames, which leads to decreased tracking accuracy and low efficiency for targets such as cruise ships or near-surface sailing submarines at low frame rate or missing some frames in video images, this paper proposed a multi-target tracking algorithm based on the fusion of YOLOv5 and DeepSORT for surface sailing. In this algorithm, the super-resolution reconstruction network is introduced to enhance the tracking target image to eliminate the interference of cloud, fog and wave on the recognition network and make the target features clear in the image. Secondly, the ShuffleAttention module is introduced in YOLOv5 to enhance the ability of extracting target features in the recognition network. Finally, Euclidean distance matching is introduced into the cascade matching of DeepSORT algorithm to replace IOU matching, so as to improve the tracking accuracy of the target. Simulation results show that the proposed algorithm has a good tracking effect, and the tracking accuracy in the improved YOLOv5 model is improved by 9.4% relative to mAP50-95, and the tracking accuracy in the DeepSORT tracking network is improved by 8.11% compared with that before optimization.

The introduction of blockchain technology provides an effective solution to the problems in shipping logistics such as low transparency and low efficiency of transportation documents. However, the construction of blockchain system led by different members of shipping chain can have different effects on the operational decisions of all parties. In this regard, by taking into account shippers’ preference for traceability information, a three-level shipping supply chain composed of ports, carriers and freight forwarders is constructed, where each party leads the construction of the blockchain traceability system. On the basis of cost sharing strategy, the Stackelberg game model is used to compare and analyze the optimal pricing strategy under the benchmark model and the three blockchain technology models. In addition, the subsidy intensity and members’ pricing strategies under different government subsidy models are considered. The research results show that blockchain technology is not always the optimal decision-making for members of the shipping supply chain, but is related to the cost-sharing ratio, shippers’ preference for traceability information, and the delay cost of shipping documents. When shippers’ preference for traceability information is high but the cost of traceability is low, all parties should improve the traceability to increase respective revenue. Although the government’s fixed subsidy model cannot bring benefits to followers, while in the retrospective subsidy model, when the sharing ratio of the leaders of the shipping chain is higher than a certain threshold, the enhanced traceability subsidy intensity will bring maximum profits to the entire supply chain. 

To help drayage enterprise alliances achieve a trade-off operation with high efficiency and low costs, this study proposed a drayage enterprise alliance mode based on the “triangular drayage” strategy. First, a two-stage profit allocation model was constructed to facilitate the tasks within the alliance of truck scheduling, container route planning, and profit distribution among the member companies. Then, the designed model was verified and analyzed with CPLEX by taking a drayage company alliance in the container terminal as a study case. The results indicate that: (1) The drayage efficiency, and the alliance profit are improved through the proposed alliance mode based on ‘triangular drayage’; (2) In the least square profit distribution method based on profit and contribution, the companies with high-level profitability and contribution in alliance are allocated more reward, while their profit share decrease gradually as the increase of profit importance among enterprises alliance; (3) The weights of influencing factors (e.g., customer satisfaction, resource commitment, and risk-taking ability) are positively correlated with a company’s distributed profit. Finally, it is proved that the proposed two-stage profit distribution model can effectively improve the cooperation stability of alliance enterprises through comparing with the traditional profit allocation methods, e.g., the average distribution method and Shapley value method.

The conditions in which ship pipelines are located are relatively harsh, and leakage is difficult to avoid. In order to detect small drip faults in time in the early stage of pipeline leakage, and timely maintenance to avoid more serious leakage caused by negligence, a visual monitoring model of pipeline drip leakage is proposed. In this model, the mean background difference method is used to detect pipeline drip, output the characteristic parameters of droplet prospect, combine the virtual coil method to statistically count the number of drip droplets, and finally propose an evaluation scheme for drip volume flow. In order to verify the effectiveness of the model, the method of building a drip experiment bench was used to obtain the drip video to verify the model. The results show that the model can detect pipeline drip droplets well, especially for leaks with slow drip frequency, it can accurately count the number of drips, the flow accuracy is more than 98%, and the relative error of the estimated volume flow is within 20%. This method can effectively monitor pipeline drip leakage and provide reference for the making of pipeline maintenance decisions.

Aiming at the problems of poor real-time, small storage data and low quality of ship power load data prediction, this paper proposes a new short-term load prediction method that combines data interpolation, wavelet threshold denoising and ProbSparse Self-Attention mechanism. Firstly, in data preprocessing stage, the database is expanded by interpolation to meet the model training requirements without affecting the characteristics and trends of the original data; at the same time, taking into account noise disturbance in the original ship power load data, in order to reduce its impact on the model prediction effect, a new wavelet threshold denoising method is adopted to process the original signals and to improve the quality of the data. Secondly, by introducing probabilistic sparse self-attention mechanism in the forecasting model. While effectively capturing the dependencies and important features in the time series power data, it reduces the memory resource consumption and model complexity. It meets the real-time requirements of ship power load forecasting and realizes the double optimization of forecasting accuracy and efficiency. In the comparison experiments with other models, this paper's model reduces at least 13.1% and 18.6% on average in the two indicators of root mean square error and average absolute percentage error, respectively, and improves the efficiency by more than 24.0% on average, and the results show that the method has obvious advantages in the accuracy and efficiency of the ship power load data forecasting model.

In order to study the aerodynamic performance of the bumps in the transonic compressor rotor, NASA Rotor 37 is taken as the research object with the spanwise continuous and individual bumps configured on its suction surface. The flow field structure and aerodynamic performance of the prototype rotor and the bump rotors under variable speed conditions are studied by numerical simulation. The results show that under the design speed and 110 % design speed, the continuous bump has better control effect on the shock wave near the blade tip. The flow can be pre-compressed ahead of the shock wave, which helps decrease the pressure gradient of the shock wave. This results in a reduction of the shock wave strength and minimizes the loss of shock wave correlation. Under peak efficiency condition of the design speed, the total pressure ratio of the rotor with the spanwise individual bump is increased by 0.236 %, and the efficiency is increased by 0.309 %. The total pressure ratio of the rotor with the continuous bump is increased by 0.285 %, and the efficiency is increased by 0.454 %. Under 50 %, 60 % and 90 % of design speed, the bumps has no beneficial effect on the aerodynamic performance of the rotor.

Cutting-off the trailing edge on the guide vanes increase the exhaust area of low pressure turbine (LPT) guider. It’s a convenient method to increase flow capacity of LPT. Numerical simulation was used to study effects of cutting-off trailing edge on aerodynamic performance of LPT cascade. The aerodynamic performance variation of cutting-off trailing edge for 2.5%,5% and 7.5% chord was displayed. And three different modifying shape methods were used after cutting-off trailing edge. Result shows: Cutting-off trailing edge can increase flow capacity of the passage in cascade. Flow capacity and flow loss are positively associated with the length of cutting-off trailing edge. Methods of modifying shape can affect loss of the cascade. Modifying the shape of profile tail to ellipse has most effect on decreasing cutting loss. It can reduce end-wall loss and wake loss at same time. Cutting-off trailing edge has a weak influence on the exhaust flow angle of cascade. Different modifying shape methods cause the positive variations of cascade exhaust flow angle form 0.13° to 0.16° while cutting-off length increase for 1% chord.