Search Results (5)

When vessels are docked at ports, traditional auxiliary engines produce substantial pollutants and noise, exerting pressure on the port environment. Shore power technology, as a green, energy-efficient, and emission-reducing solution, can effectively mitigate ship emissions. However, its widespread adoption is hindered by challenges such as high costs, compatibility issues, and connection complexity. This study develops a multi-objective optimization model for the coordinated allocation of shore power and berth scheduling, integrating economic benefits, environmental benefits, and operational efficiency. The NSGA-III algorithm is employed to solve the model and generate a Pareto-optimal solution set, with the final optimal solution identified using the TOPSIS method. The results demonstrate that the optimized shore power distribution and berth scheduling strategy can significantly reduce ship emissions and port operating costs while enhancing overall port resource utilization efficiency. Additionally, an economically feasible shore power allocation scheme, based on 80% of berth capacity, is proposed. By accounting for variations in ship types, this study provides more targeted and practical optimization strategies. These findings offer valuable decision support for port management and contribute to the intelligent and sustainable development of green ports. Full article

The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data. Full article

As strategies to build a strong maritime nation are widely implemented, the importance of island ports has been increasingly highlighted, and their fire safety issues are also receiving more attention. However, at present, research on the construction of fire protection systems has mostly been focused on the interior of cities, and less research has been carried out on the linear firefighting needs of island ports. In particular, island ports are characterized by firefighting characteristics such as inconsistent mission objectives and coordinated ship-to-shore rescues, when compared to cities. In this study, a model for the siting of harbor fire stations on a cluster of distant, isolated islands is proposed, considering the costs associated with building, maintaining, and rescuing fire stations on the basis of coverage. At the same time, the loss costs associated with the response time are considered to construct an arc segment demand siting model under ship–shore synergy conditions with the objective of minimizing the total cost. The Sea Island Group is taken as an example for the analysis of the constructed site selection model. The firefighting needs and ship–shore cooperative firefighting and rescue scenarios for eight islands in the island group are the main considerations, and the model is solved using a genetic algorithm. It is concluded that the establishment of five island harbor fire stations in the Sea Island Group can balance the cost of rescue with the cost of construction. Thus, the benefits of island fire stations can be improved, satisfying coverage rates while minimizing the associated costs. Full article

In order to address the dynamic changes in vessel preferences for berth lines caused by the deployment of shore-based power equipment in major ports and the collaborative scheduling problem of berthing and towing assistance, this paper quantifies the environmental costs of pollutants from the main engines of tugs and auxiliary engines of container ships using an environmental tax. Additionally, considering the economic costs such as vessel delay and shore power cable connection, a two-layer mixed-integer linear programming model is constructed using the task sequence mapping method. This model integrates the allocation of continuous berths at container terminals with coordinated towing scheduling for shore power selection. A solution approach is designed by combining the commercial solver (CPLEX) and the immune particle swarm optimization algorithm (IAPSO). The proposed scheme is validated using the example of the Nansha Phase IV Terminal at the Port of Guangzhou. The results show that compared to the traditional first-come-first-served and adjacent scheduling schemes, the collaborative scheduling scheme proposed in this paper reduces the total cost by 21.73%. By effectively utilizing berth resources and shore power equipment while densely arranging collaborative tasks and appropriately increasing the number of tugs, the port can convert the economic cost of leasing a small number of tugs (increased by 10.63%) into environmental benefits (decreased by 33.88%). This approach provides a reference for addressing nearshore pollution emissions in ports. Full article

Floating-algae detection plays an important role in marine-pollution monitoring. The surveillance cameras on ships and shores provide a powerful way of monitoring floating macroalgae. However, the previous methods cannot effectively solve the challenging problem of detecting Ulva prolifera and Sargassum, due to many factors, such as strong interference with the marine environment and the drastic change of scale. Recently, the instance-segmentation methods based on deep learning have been successfully applied to many image-recognition tasks. In this paper, a novel instance-segmentation network named AlgaeFiner is proposed for high-quality floating-algae detection using RGB images from surveillance cameras. For improving the robustness of the model in complex ocean scenes, the CA-ResNet is firstly proposed by integrating coordinate attention into the ResNet structure to model both the channel- and position-dependencies. Meanwhile, the Ms-BiFPN is proposed by embedding the multi-scale module into the architecture of BiFPN to strengthen the ability of feature fusion at different levels. To improve the quality of floating-algae segmentation, the Mask Transfiner network is introduced into the AlgaeFiner to obtain the high-quality segmentation results. Experimental results demonstrate that the AlgaeFiner can achieve better performance on floating-algae segmentation than other state-of-the-art instance-segmentation methods, and has high application-value in the field of floating-macroalgae monitoring. Full article