In order to solve the complexity and uncertainty problems in the berthing process of large ships, this paper develops a decision support model based on Case-Based Reasoning (CBR). This model integrates CBR technology with a cloud model and BP neural networks. It comprehensively considers multi-dimensional attributes such as vessel characteristics, meteorological and hydrological conditions, and port factors to establish a case framework comprising a basic information domain, a characteristic attribute domain, and a decision support domain. By integrating expert scoring with the cloud model, the model processes the randomness and fuzziness in expert evaluations to optimize the case attribute weights. Furthermore, it utilizes BP neural network to achieve case reuse and decision prediction, thereby reducing subjective errors introduced by manual intervention. In this paper, we collect the berthing cases of Chiwan and Shekou container terminals at Shenzhen Port for model validation. The preliminary verification model can provide relevant decision support for pilots, expand new scenarios of artificial intelligence technology in maritime applications, and provide new ideas for intelligent berthing planning of unmanned ships.

Improving ship energy efficiency and reducing greenhouse gas emissions are major research priorities in the maritime industry. Accurate prediction of main engine power is fundamental to enhancing vessel energy efficiency. Using historical operational data collected from a Very Large Crude Carrier (VLCC), this study integrated and cleaned meteorological data to construct training and test datasets. Three models for main-engine power estimation are investigated and compared:a mechanistic model （SNNM), a non-mechanistic model based on Random Forest （RF), and a semi-mechanistic RF-based model. Simulation results indicate that while the mechanistic SNNM model can meet application requirements under specific engineering conditions, but R² coefficient is relatively low. In contrast, both the non-mechanistic model based on RF and the semi-mechanistic RF-based model demonstrated excellent predictive accuracy for both main engine shaft rotational speed and power, with R² values exceeding 0. 98.

Foggy weather significantly degrades ship visibility and image quality, posing serious risks to navigation safety. Enhancing the dehazing performance of ship navigation images is therefore of great importance. To address the insufficient fog removal and poor detail restoration of existing dehazing methods in maritime scenarios, this study proposes an end-to-end ship image dehazing method that integrates an improved CycleGAN with attention mechanisms. A Squeeze-and-Excitation （SE）channel-attention module is introduced to aggregate feature maps, compress spatial information, and strengthen the network's ability to learn global representations. Multi-scale channel fusion is achieved through skip connections, which not only reduces computational complexity but also enables the model to better capture fog characteristics under complex atmospheric conditions and to process ship targets of different sizes. Furthermore, a Channel Attention module is incorporated to enhance feature selection and improve the restoration of ship contours and fine structural details. Quantitative evaluations and real fog-navigation experiments confirm the robustness of the proposed method, demonstrating consistent improvements over existing dehazing approaches across all tested metrics and navigation scenarios.

With the continued advancement of China "dual-carbon" goals and the increasingly stringent emission reduction regulations of the International Maritime Organization (IMO), the shipping industry is facing more severe emission reduction challenges and urgently needs to clarify the green transition pathways. Most of the existing research focuses on the selection of alternative fuels, while there is relatively little research on emission reduction strategies from the perspective of the fleet. To address the shortcomings of the existing research, this study identifies the key factors influencing fleet green transition decisions. Based on this, a bi-objective linear programming model jointly considering economic and environmental objectives is established, and a genetic algorithm combined with the ε-constraint method is constructed to solve the model. Finally, taking the 10, 000-11, 000 TEU container fleet of COSCO Shipping Group as a case study, this research derives the optimal green transition strategy for the fleet during the planning horizon, encompassing fuel choices and operational configurations for individual vessels. Sensitivity analysis reveals that fluctuations in fuel prices significantly affect the selection of engine types during vessel retrofitting and renewal decisions, while the stringency of emission reduction targets directly influences fleet transition costs, thereby affecting corporate proactiveness in pursuing decarbonization initiatives. Consequently, policymakers should establish appropriately calibrated emission reduction targets and incentive mechanisms to accelerate the advancement of low-carbon technologies, reduce fleet transition costs, and expedite the achievement of decarbonization objectives in the shipping industry.

Efficient scheduling of large vessels entering and leaving ports is critical to improving port efficiency, particularly for ports affected by tidal constraints. This study investigates the ship scheduling problem in tide-influenced ports, incorporating tidal window constraints while accounting for berth size differences, vessel safety distances, and mooring／unmooring operations. A Mixed-Integer Linear Programming (MILP) model is developed with the objective of minimizing the total delay time of all vessels, and a Lagrangian relaxation heuristic algorithm is designed for its solution. A case study using real tidal data from the Ningbo-Zhoushan Wai Diao operation area was conducted. The results show that the proposed scheduling model and algorithm, which consider tidal window constraints, can reduce vessel delays by 28. 5% while meeting safety requirements. This approach provides valuable insights for scheduling in ports significantly affected by tides.

This paper aims to introduce the fundamental content, legislative purpose, and implementation pathways of Korea's "Act on Promoting the Development and Commercialization of Autonomous Ships." It delves into an in-depth analysis of the measures taken to promote the research, development, and commercialization of autonomous surface ships, the regulatory framework and foundational systems established, as well as the intrinsic logic and practical as well as long-term significance underlying these aspects. By sharing Korea's basic practices in promoting cutting-edge maritime technological innovation through legislation and driving the development of smart shipping via commercialization, this paper seeks to provide valuable references for China's related legislative efforts, the acceleration of smart shipping development, and the advancement of its transport sector.

During the reconstruction of the ship lock, excavation of the rock masses on both sides is often required. In current practice, slope instability is commonly judged using indicators such as displacement, the extent of the plastic zone, and the number of numerical iterations. These criteria typically rely on manual interpretation, which can be subjective and may not clearly determine whether slope failure has occurred. Thus, this paper proposes a slope stability analysis method based on a kinetic-energy evolution criterion, and applies it to the reconstructed ship-lock slope at Baishi Hydropower Station. A representative excavation cross-section at the ship lock is selected and the strength reduction method is adopted to analyze the kinetic energy evolution of the slope soil under different reduction coefficients, thereby identifying the occurrence of instability and failure. The proposed criterion is further compared with the conventional static assessment method based on displacement. The results show that the dynamic analysis method based on the kinetic energy evolution can obtain an accurate slope safety factor. Compared with the traditional displacement judgment method, the method proposed in this paper provides a more objective and explicit identification of slope instability which has greater advantages. The findings of this paper provide practical value and guidance for the high rock slope engineering, which can serve as useful reference for professionals in related fields.

In the context of port shore power deployment, studying the impact of different policies on port and shipping enterprises is crucial for improving shore power utilization and achieving established emission reduction goals. To explore the policy effects on these enterprises, a Stackelberg game model was constructed with the port as the leader and shipping companies as the follower, incorporating innovation subsidies into the framework. This model aims to address innovation challenges in shore power equipment and examines the combined impact of subsidies and carbon trading policies on port and shipping enterprises. The model is solved using backward induction and numerically simulated via Matlab. The results indicate that in the early stages of emission reduction, subsidy policies help enhance innovation levels, while after the maturation of shore power technology, the implementation of dual policies involving both subsidies and carbon trading is more effective in motivating the industry to develop emission reduction technologies. Therefore, it is recommended that the government take measures to expand market scale, strengthen societal low-carbon awareness, and increase innovation subsidies in the early phase to promote shore power utilization. After the technology matures, the subsidy ratio and carbon price can be adjusted to sustain the utilization of shore power.

To address the challenges in dynamic modeling of Autonomous Underwater Vehicle (AUV), this paper proposes a black-box identification method for nonlinear systems based on deep convolutional neural networks, taking into account the nonlinear characteristics of the AUV's six-degree-of-freedom (6-DOF) motion. First, the frequency corresponding to the maximum amplitude of the rudder signal is extracted and used as a threshold for Variational Mode Decomposition (VMD) denoising. This reduces noise in the experimental data of the AUV model and resolves the issue of difficult parameter tuning in VMD decomposition. Then, a black-box model for the nonlinear system is constructed using Bidirectional Long Short-Term Memory (BiLSTM) and Attention mechanisms, with the Adam optimization algorithm employed to solve the AUV 6-DOF motion model. Finally, the AUV model data are used for model training and predictive validation, and the results are compared with modeling methods such as CNN-LSTM, CNN-BiLSTM, and CNN-LSTM-Attention to analyze the velocity, Euler angles, and trajectory of AUV motion. Experimental results show that, compared to the CNN-LSTM model, the proposed method improves the Root Mean Square Error (RMSE), the coefficient of determination (R²), and the Symmetric Mean Absolute Percentage Error (SMAPE) by 79.29%, 3.84%, and 74.41%, respectively, validating the feasibility and effectiveness of the proposed dynamic modeling approach. This method provides an alternative strategy for precise obstacle avoidance and autonomous navigation of underwater vehicles.

To accurately predict the fuel consumption of in service ships, analyze the complex and variable influencing factors of fuel consumption, and quantify their respective impacts, this study selects tankers and bulk carriers for operational data collection and preprocessing. A fuel consumption prediction model based on the Extreme Gradient Boosting (XGBoost) algorithm is established, and factor importance is evaluated using the Gain method. The results demonstrate that the proposed model achieves strong computational and predictive performance, with mean absolute percentage errors of 4.88% and 3.92% for the tanker and bulk carrier models, respectively. Among internal factors, ship speed shows the greatest influence, with weights of 0.671 and 0.429 for the two vessel types. Regarding external factors, navigation environment conditions such as wind and waves also exhibit significant impacts.