To further standardize the management of ship dismantling, it is essential to evaluate and analyze relevant policies as a basis for improving the full life cycle management system of ships. This paper reviews and analyzes 42 policy documents issued by governmental and industrial organizations over the past five years. Based on the content analysis of these policies, a Policy Consistency Index model (abbreviated as the "P Index Model") is established. The ship dismantling policies are categorized into three types: macro guidance, supplementary refinement, and guidance and encouragement. Representative policy documents are selected as evaluation targets, and their rationality is empirically analyzed. The results indicate that China's ship dismantling policies require improvements at three levels. At the national level, it is recommended to establish a coordinated ship dismantling management mechanism involving relevant regulatory departments and to develop unified planning and industry support policies. At the local level, local governments should promptly formulate and implement specific measures to ensure effective policy enforcement. At the industry level, associations need to enhance communication and collaboration with local governments, particularly in establishing consensus on the qualifications of compliant ship dismantling companies, to jointly promote the standardized development of the industry.

In accordance with the MARPOL Annex Ⅵ Nitrogen Oxide (NO_x) Technical Code issued by the International Maritime Organization (IMO), this paper proposes a simplified on-board test scheme for measuring NO_x emissions from marine diesel engines. The scheme employs the carbon balance method to calculate exhaust flow, requiring only the measurement of NO_x and CO₂ concentrations in diesel exhaust at selected load points, and adopts the default fuel element content values provided in the IMO Technical Code. The results show that the weighted average NO_x deviation of the simplified method is approximately 2% compared with the standard cycle, while also reducing the complexity of emission testing equipment and shortening the testing process. The findings provide a theoretical basis for streamlining NO_x emission on-board tests.

In response to increasingly stringent maritime emission regulations, vessels using alternative fuels—such as Liquefied Natural Gas (LNG) and methanol—are being gradually deployed. However, the number of ports offering bunkering for these new fuels remains significantly lower than those supplying conventional fuels, making route planning and operational management more challenging for alternatively fueled vessels. Shipping companies urgently need to rationally design routes and support the operation of alternative fuel bulk carriers within the context of scarce refueling infrastructure. To this end, this study develops a mixed-integer nonlinear programming (MINLP) model aiming to minimize total emissions, incorporating constraints such as total voyage duration and adjustable speed ranges. To address the nonlinear nature of the model, a heuristic algorithm based on variable neighborhood search is proposed to efficiently obtain near-optimal solutions. Finally, through case studies under both single-voyage and continuous-voyage scenarios, the collaborative optimization of refueling strategies and speed adjustments is thoroughly analyzed, demonstrating the effectiveness of the proposed model and algorithm.

This study aims to apply deep reinforcement learning to address the challenges of trajectory planning and control for unmanned surface vehicles. In trajectory planning, the Q-learning algorithm is employed to generate trajectories in real-world aquatic environments. For the design of the reward function, factors such as shallow water areas are taken into account, with an emphasis on minimizing the number of turning points along the path. For trajectory tracking control, we integrate the Soft Actor-Critic (SAC) algorithm with the Proportional-Integral-Derivative (PID) control method to alleviate the difficulties of manual parameter tuning associated with conventional PID controllers. This hybrid approach also mitigates the interpretability limitations often found in pure deep reinforcement learning methods. Comparative experiments involving the traditional PID algorithm, Genetic Algorithm (GA), and Deep Deterministic Policy Gradient (DDPG) algorithm demonstrate the superiority of the proposed SAC-PID method. Simulation results show that the planned trajectories effectively incorporate multiple factors, including travel distance, shallow water regions, and number of turning point, the SAC-PID method achieves outstanding performance in trajectory tracking.

To address challenges such as significant scale variations, high aspect ratios, dense arrangements, and complex backgrounds in ship target detection from remote sensing images, this paper proposes an improved YOLOv7-based algorithm. Using YOLOv7 as the baseline network, the prior anchor generation algorithm is optimized for the dataset. A long-edge representation method combined with circular smooth labeling is introduced to detect ship targets with uncertain rotation directions. The YOLOv7 network is enhanced by embedding both the GAM (Global Attention Mechanism) and SimAM (Simple Attention Mechanism) modules, which effectively suppress interference from complex background regions in remote sensing images and improve target detection accuracy. Additionally, the coordinate loss function is optimized to accelerate model convergence. Experimental results on the DOTA-ship and HRSC2016 datasets for both single-class and multi-class detection tasks show mAP values of 86.1%, 97.7%, and 87.1%, respectively-representing improvements of 7.8%, 4.6%, and 7.9% over the original YOLOv7 model. These results validate the effectiveness and superiority of the proposed method.

To enhance the cognitive capabilities of waterborne transportation systems for intelligent ship navigation, this study first reviews the development of electronic map systems designed for the new generation of shipping systems and summarizes the technical requirements for future map functionalities in waterborne transportation. Subsequently, the Pan-information-based Navigation Scenario Map (PNSM) is proposed as a core component of the "Navigation Brain System". The elements, characteristics, and conceptual foundations of the PNSM are analyzed, with emphasis placed on its capabilities in object-oriented modeling and computer-cognitive representation of navigational environment data. Key technologies underpinning the PNSM are further discussed, including spatiotemporal object modeling of scenario elements, hierarchical organization of all elements, dynamic association among objects, adaptive cross-domain computing for scenario data, cognitive modeling of scenario semantics, intelligent information services, and multi-dimensional dynamic visualization of the scenario map. The application process and practical effectiveness of the PNSM are illustrated using data from the Intelligent Shipping Project in the Water Network Areas of Zhejiang Province, China. Research results demonstrate that the PNSM can effectively integrate, organize, correlate, and visualize water traffic data, enabling comprehensive cognitive representation of all elements within the waterborne transportation system and their interrelationships. It provides essential technical support for the advancement of next-generation shipping systems, including intelligent navigation technologies and standardized frameworks.

Ship trajectory prediction and behavior recognition can help effectively assess navigational risks and provide an important basis for decision-making in collision avoidance and traffic management. To improve the accuracy of ship trajectory prediction and behavior recognition, this paper studies a multi-task Informer model for simultaneous trajectory prediction and behavior recognition. Based on the Informer framework, the model incorporates a multi-task learning approach. It addresses the issue that inaccurate ship behavior records in AIS data cannot be directly used as model inputs by designing a multi-task loss function that jointly trains behavior recognition and trajectory prediction in parallel. During training, an adaptive updating strategy for the loss function-based on homoscedastic uncertainty-is designed to automatically allocate weights to the losses of the two tasks. Evaluated using real AIS data from the Taicang sector waters, the multi-task Informer model reduces trajectory prediction loss by 40.2% and 14.7% compared to LSTM and Informer models, respectively. In behavior recognition, the multi-task model improves accuracy by 11.7% and 5.95% compared to LSTM and Informer models, respectively. The results demonstrate that the multi-task model effectively enhances the performance of ship trajectory prediction while achieving accurate recognition of ship behavior.

Addressing the requirements for ship’s autonomous navigation and high-precision path following control, this paper proposes a fuzzy control method with partitioned integral regions based on the Integral Line-of-Sight (ILOS) guidance law for path following control of autonomous navigation ships. Based on the established ship motion model, an extended Kalman filter is employed to estimate ship states such as speed and heading using GNSS measurement data. Fuzzy controllers are designed to dynamically adjust the look-ahead distance parameter according to different navigation conditions, thereby improving tracking performance. The 700 Twenty-foot Equivalent Unit (TEU) battery-powered container ship built by COSCO Shipping Group is taken as the plant for path following experiments on a simulation platform. Comparative analyses with results from different algorithms show that the proposed control algorithm achieves superior performance and smoother rudder angle manipulation. This method can provide reference for path following control of autonomous navigation ships.

Aiming to address the issues associated with traditional artificial cargo hold clearing operations, such as high risk, low efficiency and an insufficient level of automation, this paper introduces the Intelligent Cargo Hold Clearing Robot (ICHCR) system for bulk carriers. Based on dual-mode control, the ICHCR system achieves unmanned operation and robot-shore collaborative intelligent cargo hold clearing. The article presents an intelligent robot hardware system for cargo hold clearing and a cloud control platform for immersive operation. Intelligent control methods for the ICHCR are investigated, including perception and localization inside the cargo holds, and dual control modes of cloud control and autonomous navigation operation. Other core technologies include robot-shore cooperative cargo hold clearing. Finally, the system was applied and validated on a 70,000-tonne Panamax bulk carrier in a grain port. Experimental results demonstrate that the ICHCR system improves the safety of cargo hold clearing operations, optimizing the overall process and reduces time consumption, meeting the requirements of safety and efficiency.

Ensuring the safety of ship navigation is fundamental to the high-quality development of waterway transportation. However, the complex and ever-changing nature of the navigation environment poses significant challenges to ensuring the safe passage of ships. The management of navigation safety risks is gradually shifting from passive measures, such as emergency response and post-accident analysis, to proactive risk prevention and decision-making. This paper analyses the current state of domestic and international research into the proactive control of ship navigation safety, as well as existing issues. It reviews key technologies and practical engineering applications in three areas: ship navigation information perception; ship-based navigation decision-making; and shore-based risk early warning. The paper proposes an integrated, intelligent and resilient approach to developing ship navigation safety controls, providing insights and technical references to enhance proactive control capabilities.