Journal of Marine Science and Engineering

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29 pages, 19185 KiB

Open AccessArticle

An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal Approach

by Akhahenda Whitney Khayenzeli, Woo-Ju Son, Dong-June Jo and Ik-Soon Cho

J. Mar. Sci. Eng. 2025, 13(5), 922; https://doi.org/10.3390/jmse13050922 - 7 May 2025

Viewed by 177

Abstract

The global shipping industry facilitates the movement of approximately 80% of goods across the world but accounts for nearly 3% of total greenhouse gas (GHG) emissions every year, and other pollutants. One challenge in reducing shipping emissions is understanding and quantifying emission characteristics. [...] Read more.

The global shipping industry facilitates the movement of approximately 80% of goods across the world but accounts for nearly 3% of total greenhouse gas (GHG) emissions every year, and other pollutants. One challenge in reducing shipping emissions is understanding and quantifying emission characteristics. A detailed method for calculating shipping emissions should be applied when preparing exhaust gas inventory. This research focused on quantifying CO₂, NO_x, and SO_x emissions from tankers, containers, bulk carriers, and general cargo in the Republic of Korea using spatio-temporal analysis and maritime big data. Using the bottom-up approach, this study calculates vessel emissions from the ship engines while considering the fuel type and operation mode. It leveraged the Geographic Information System (GIS) to generate spatial distribution maps of vessel exhausts. The research revealed variability in emissions according to ship types, sizes, and operational modes. CO₂ emissions were dominant, totaling 10.5 million tons, NO_x 179,355.2 tons, and SO_x 32,505.1 tons. Tankers accounted for about 43.3%, containers 33.1%, bulk carriers 17.3%, and general cargo 6.3%. Further, emissions in hoteling and cruising were more significant than during maneuvering and reduced speed zones (RSZs). This study contributes to emission databases, providing a basis for the establishment of targeted emission control policies. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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22 pages, 2390 KiB

Open AccessArticle

Tramp Ship Routing and Scheduling with Integrated Carbon Intensity Indicator (CII) Optimization

by Haiying Yang, Feiyang Ren, Jingbo Yin, Siqi Wang and Rafi Ullah Khan

J. Mar. Sci. Eng. 2025, 13(4), 752; https://doi.org/10.3390/jmse13040752 - 9 Apr 2025

Viewed by 374

Abstract

In response to growing environmental concerns and regulatory pressures, reducing carbon emissions in maritime transport has become a priority. Shipping companies face the challenge of balancing profitability objectives with the imperative to minimize their environmental footprint. This study addresses the tramp ship routing [...] Read more.

In response to growing environmental concerns and regulatory pressures, reducing carbon emissions in maritime transport has become a priority. Shipping companies face the challenge of balancing profitability objectives with the imperative to minimize their environmental footprint. This study addresses the tramp ship routing and scheduling problem by incorporating the carbon intensity indicator (CII) into the optimization framework. A bi-objective optimization model is developed, with two objective functions aimed at maximizing fleet profit and improving CII ratings. The Gale–Shapley algorithm is employed to achieve stable vessel–cargo matching, and the genetic algorithm is adopted for iterative optimization. This computational study, based on real historical data, verifies the effectiveness of the proposed model and algorithm. The results demonstrate notable improvements in fleet efficiency and environmental performance, increasing profitability by 4.38% while maintaining favorable CII ratings. The findings provide valuable theoretical guidance for shipping companies navigating increasingly stringent CII regulations. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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19 pages, 3261 KiB

Open AccessArticle

Risk Assessment of Hydrogen Fuel System Leakage in Ships Based on Noisy-OR Gate Model Bayesian Network

by Gen Li, Haidong Zhang, Shibo Li and Chunchang Zhang

J. Mar. Sci. Eng. 2025, 13(3), 523; https://doi.org/10.3390/jmse13030523 - 9 Mar 2025

Viewed by 711

Abstract

To mitigate the risk of hydrogen leakage in ship fuel systems powered by internal combustion engines, a Bayesian network model was developed to evaluate the risk of hydrogen fuel leakage. In conjunction with the Bow-tie model, fuzzy set theory, and the Noisy-OR Gate [...] Read more.

To mitigate the risk of hydrogen leakage in ship fuel systems powered by internal combustion engines, a Bayesian network model was developed to evaluate the risk of hydrogen fuel leakage. In conjunction with the Bow-tie model, fuzzy set theory, and the Noisy-OR Gate model, an in-depth analysis was also conducted to examine both the causal factors and potential consequences of such incidents. The Bayesian network model estimates the likelihood of hydrogen leakage at approximately 4.73 × 10⁻⁴ and identifies key risk factors contributing to such events, including improper maintenance procedures, inadequate operational protocols, and insufficient operator training. The Bow-tie model is employed to visualize the causal relationships between risk factors and their potential consequences, providing a clear structure for understanding the events leading to hydrogen leakage. Fuzzy set theory is used to address the uncertainties in expert judgments regarding system parameters, enhancing the robustness of the risk analysis. To mitigate the subjectivity inherent in root node probabilities and conditional probability tables, the Noisy-OR Gate model is introduced, simplifying the determination of conditional probabilities and improving the accuracy of the evaluation. The probabilities of flash or pool fires, jet fires, and vapor cloud explosions following a leakage are calculated as 4.84 × 10⁻⁵, 5.15 × 10⁻⁵, and 4.89 × 10⁻⁷, respectively. These findings highlight the importance of strengthening operator training and enforcing stringent maintenance protocols to mitigate the risks of hydrogen leakage. The model provides a valuable framework for safety evaluation and leakage risk management in hydrogen-powered ship fuel systems. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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24 pages, 7333 KiB

Open AccessArticle

ANFIS-Based Course Controller Using MMG Maneuvering Model

by Yu Guo, Rui Yang, Zhiheng Zhang and Bing Han

J. Mar. Sci. Eng. 2025, 13(3), 490; https://doi.org/10.3390/jmse13030490 - 1 Mar 2025

Viewed by 761

Abstract

In the domain of course control, traditional methods such as proportional–integral–derivative (PID) control often exhibit limitations when addressing complex nonlinear systems and uncertain disturbances. To mitigate these challenges, the adaptive neuro-fuzzy inference system (ANFIS) has been integrated into course control strategies. The primary [...] Read more.

In the domain of course control, traditional methods such as proportional–integral–derivative (PID) control often exhibit limitations when addressing complex nonlinear systems and uncertain disturbances. To mitigate these challenges, the adaptive neuro-fuzzy inference system (ANFIS) has been integrated into course control strategies. The primary objective of this study is to investigate the course control characteristics of vessels governed by the ANFIS controller under both normal and severe sea conditions. A three-degree-of-freedom (3-DOF) maneuvering model set (MMG) was employed and validated through sea turning tests. The design of the ANFIS controller involved a combination of the backpropagation algorithm with the least square method. Training data for the ANFIS control system were derived from a linear control framework, followed by simulation tests conducted under normal and severe sea conditions to assess control performance. The simulation results indicate that in normal sea conditions, ANFIS has more stable heading control (smaller A_ψ), but at the cost of more energy consumption (larger I_δ). Notably, response time is reduced by approximately 36.7% compared to that of the linear controller. Conversely, during severe sea conditions, ANFIS exhibits an increase in response time by about 33.3% relative to the linear controller while maintaining a smaller I_δ. In the whole course control stage, the stability is better than the linear controller, and it has better energy-saving characteristics. Under scenarios involving small and large course alterations, A_ψ values for ANFIS are approximately 11.28% and 13.97% higher than those observed with the best-performing linear controller (λ_ψ = 60), respectively. As the propeller speed increases, the A_ψ value of the ANFIS controller decreases significantly, to about 62.71%, indicating that the energy efficiency is improved and the course stability is also enhanced. In conclusion, it can be asserted that the implementation of an ANFIS controller yields commendable performance in terms of controlling vessel courses effectively. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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20 pages, 11049 KiB

Open AccessArticle

Effects of High-Frequency Vibration on Residual Stress and Microstructure of Carbon Steel for Marine Structures: Comparative Analysis with Tempering

by Guanhua Xu and Feilong Liu

J. Mar. Sci. Eng. 2025, 13(3), 408; https://doi.org/10.3390/jmse13030408 - 22 Feb 2025

Viewed by 668

Abstract

To improve the safety and service life of carbon steel used in marine structures, appropriate regulation of residual stress in carbon steel is required. This paper investigates the effects of high-frequency vibratory stress relief (VSR) and tempering on the residual stress, microstructure, and [...] Read more.

To improve the safety and service life of carbon steel used in marine structures, appropriate regulation of residual stress in carbon steel is required. This paper investigates the effects of high-frequency vibratory stress relief (VSR) and tempering on the residual stress, microstructure, and surface hardness of 45 steel. After the high-frequency VSR and tempering at 200 °C for 30 min treatment, the microstructure is still tempered martensite. When the 45 steel experimental specimens were tempered at 600 °C for 30 min, the microstructure changed from tempered martensite to tempered sorbite, and the residual stress regulation effect of 45 steel experimental specimens was significantly improved. However, its surface hardness decreased significantly, which reduces the mechanical properties of marine structural components. Comparatively, high-frequency VSR is an effective method to regulate residual stress while ensuring that the microstructure of marine structural components does not undergo drastic changes. This study provides technical and theoretical support for the residual stress regulation treatment of 45 steel in marine engineering. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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20 pages, 4550 KiB

Open AccessArticle

Optimization of Inbound and Outbound Vessel Scheduling in One-Way Channel Based on Reinforcement Learning

by Rong Zhen, Meng Sun and Qionglin Fang

J. Mar. Sci. Eng. 2025, 13(2), 237; https://doi.org/10.3390/jmse13020237 - 26 Jan 2025

Cited by 1 | Viewed by 866

Abstract

As the size and number of ships continue to grow, effective management of vessel scheduling has become more and more important for the efficient one-way channel port operation, whose characteristics significantly affect the safety and efficiency of ports. This paper presents a reinforcement-learning-based [...] Read more.

As the size and number of ships continue to grow, effective management of vessel scheduling has become more and more important for the efficient one-way channel port operation, whose characteristics significantly affect the safety and efficiency of ports. This paper presents a reinforcement-learning-based approach to optimize the scheduling of vessels in a one-way channel, aiming to quickly identify a scheduling solution that enhances port operational efficiency. This method models the vessel scheduling problem in a one-way channel by incorporating navigational constraints, safety requirements, and vessel-specific characteristics. Using the Q-learning algorithm to minimize vessel wait times, it identifies an optimal scheduling solution. Experiments were conducted using real data from the Dayao Bay Pier of Dalian Port to validate the rationality and effectiveness of the proposed model and algorithm. The results show that the reinforcement learning approach achieved approximately a 16% improvement in solution quality compared to the genetic algorithm (GA) while requiring only half the computation time. Additionally, it reduced delay times by over 40% relative to the traditional FCFS strategy, indicating superior overall performance. This research presents an efficient, intelligent approach to vessel scheduling, providing a theoretical foundation for further advancements in this field and enhancing decision support for vessel scheduling in one-way channels with practical implications. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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23 pages, 4982 KiB

Open AccessArticle

Emission Estimation and Spatiotemporal Distribution of Passenger Ships Using Multi-Source Data: A Case from Zhoushan (China)

by Xubiao Xu, Xingyu Liu, Lin Feng, Wei Yim Yap and Hongxiang Feng

J. Mar. Sci. Eng. 2025, 13(1), 168; https://doi.org/10.3390/jmse13010168 - 18 Jan 2025

Cited by 1 | Viewed by 1169

Abstract

Quantifying and estimating shipping emissions is a critical component of global emission reduction research and has become a growing area of interest in recent years. However, emissions from short-distance passenger ships operating on inter-island routes and their environmental impacts have received limited attention. [...] Read more.

Quantifying and estimating shipping emissions is a critical component of global emission reduction research and has become a growing area of interest in recent years. However, emissions from short-distance passenger ships operating on inter-island routes and their environmental impacts have received limited attention. This contribution investigated the temporal and spatial distribution characteristics of pollutants emitted by short-distance passenger ships at Zhoushan (China) using Automatic Identification System (AIS) data and the bottom–up emission model integrated with multi-source meteorological data. A year-long emission inventory was investigated. The results indicated that high-speed passenger ships contributed to the largest share of the emissions. The emissions were predominantly concentrated during daytime hours, with the routes between Zhoushan Island and Daishan, Daishan and Shengsi, and Zhoushan Island and Liuheng Island accounting for most of the emissions. Furthermore, intra-port waterways were identified as the primary emission areas for short-distance passenger ships. This study provides essential data support and references for the relevant authorities to understand the emission patterns of short-distance passenger ships, thereby facilitating the formulation of targeted emission reduction strategies for the maritime passenger transport sector. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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18 pages, 3329 KiB

Open AccessArticle

Distributionally Robust Optimal Scheduling of Hybrid Ship Microgrids Considering Uncertain Wind and Wave Conditions

by Fang Lu, Yubin Tian, Hongda Liu and Chuyuan Ling

J. Mar. Sci. Eng. 2024, 12(11), 2087; https://doi.org/10.3390/jmse12112087 - 19 Nov 2024

Cited by 2 | Viewed by 954

Abstract

A hybrid ship uses integrated generators, an energy storage system (ESS), and photovoltaics (PV) to match its propulsion and service loads, and together with optimal power and voyage scheduling, this can lead to a substantial improvement in ship operation cost, ensuring compliance with [...] Read more.

A hybrid ship uses integrated generators, an energy storage system (ESS), and photovoltaics (PV) to match its propulsion and service loads, and together with optimal power and voyage scheduling, this can lead to a substantial improvement in ship operation cost, ensuring compliance with the environmental constraints and enhancing ship sustainability. During the operation, significant uncertainties such as waves, wind, and PV result in considerable speed loss, which may lead to voyage delays and operation cost increases. To address this issue, a distributionally robust optimization (DRO) model is proposed to schedule power generation and voyage. The problem is decoupled into a bi-level optimization model, the slave level can be solved directly by commercial solvers, the master level is further formulated as a two-stage DRO model, and linear decision rules and column and constraint generation algorithms are adopted to solve the model. The algorithm aims at minimizing the operation cost, limiting greenhouse gas (GHG) emissions, and satisfying the technical and operational constraints considering the uncertainty. Extensive simulations demonstrate that the expected total cost under the worst-case distribution is minimized, and compared with the conventional robust optimization methods, some distribution information can be incorporated into the ambiguity sets to generate fewer conservative results. This method can fully ensure the on-time arrival of hybrid ships in various uncertain scenarios while achieving expected operation cost minimization and limiting greenhouse gas (GHG) emissions. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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20 pages, 5672 KiB

Open AccessArticle

A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption

by Tianrui Zhou, Jinggai Wang, Qinyou Hu and Zhihui Hu

J. Mar. Sci. Eng. 2024, 12(11), 1954; https://doi.org/10.3390/jmse12111954 - 31 Oct 2024

Cited by 2 | Viewed by 1460

Abstract

Ship fuel consumption plays a crucial role not only in understanding ships’ energy efficiency but also in gaining insights into their emissions. However, enhancing the accuracy of these predictions poses significant challenges due to data limitations and the methods employed. Due to these [...] Read more.

Ship fuel consumption plays a crucial role not only in understanding ships’ energy efficiency but also in gaining insights into their emissions. However, enhancing the accuracy of these predictions poses significant challenges due to data limitations and the methods employed. Due to these factors, such as data variability and equipment characteristics, ship fuel consumption exhibits certain fluctuations under specific conditions. Previous fuel consumption prediction methods primarily generate a single specific value, making it difficult to capture the volatility of and variability in fuel consumption. To overcome this limitation, this paper proposes a novel method that integrates Gaussian process prediction with quantile regression theory to perform interval predictions of ship fuel consumption, providing a range of possible outcomes. Through comparative analyses with traditional methods, the possibility of using the method is verified and its results are validated. The results indicate the following: (1) at a 95% confidence level, the proposed method achieves a prediction interval coverage probability of 0.98 and a prediction interval normalized average width of 0.123, which are significantly better than those of the existing backpropagation neural network (BPNN) and gradient boosting decision tree (GBDT) quantile regression models; (2) the prediction accuracy of the proposed method is 92% for point forecasts; and (3) the proposed method is applicable to main datasets, including both noon report and sensor datasets. These findings provide valuable insights into interval predictions of ship fuel consumption and highlight their potential applications in related fields, emphasizing the importance of accurate interval predictions in intelligent energy efficiency optimization. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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20 pages, 6186 KiB

Open AccessArticle

Optimizing Ship Draft Observation with Wave Energy Attenuation and PaddlePaddle-OCR in an Anti-Fluctuation Device

by Yaoming Wei, Huan Du, Qinyou Hu and Hu Wang

J. Mar. Sci. Eng. 2024, 12(10), 1865; https://doi.org/10.3390/jmse12101865 - 18 Oct 2024

Cited by 1 | Viewed by 1440

Abstract

With the development and application of artificial intelligence (AI) in the shipping industry, using AI to replace traditional draft survey methods in bulk carriers can significantly reduce manpower, lower the risks associated with visual observations, improve measurement accuracy, and minimize the impact of [...] Read more.

With the development and application of artificial intelligence (AI) in the shipping industry, using AI to replace traditional draft survey methods in bulk carriers can significantly reduce manpower, lower the risks associated with visual observations, improve measurement accuracy, and minimize the impact of human subjective factors. Ultimately, the integration of software and hardware technologies will replace human visual observations with automated draft measurement calculations. A similar anti-fluctuation device described in this article has been used in ship draft observation based on AI-assisted proving, which can ease the fluctuation of the wave inside the pipe. Observers can directly read the water surface inside the pipe and compare it to the ship’s draft mark to obtain the final draft, effectively improving draft observation accuracy. However, some surveyors refuse to accept the readings obtained from this device, citing a lack of theoretical basis or the absence of accreditation from relevant technical authorities, leading to the rejection of results. To address these issues, this paper integrates wave energy attenuation theory with PaddlePaddle-OCR recognition to further validate the anti-fluctuation device for accurate ship draft observation. The experimental results are as follows: first, the pipe effectively suppresses the amplitude of external water surface fluctuations by 75%, explaining the fundamental theory that wave heights within the anti-fluctuation device are consistent with external swell heights. When taking a draft measurement, the system dynamically adjusts the position of the main tube in response to the ship’s movements, maintaining the stability of the measurement section and significantly reducing the difficulty of observations. Due to the reduction in fluctuation amplitude, there is a noticeable improvement in observation accuracy. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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25 pages, 3120 KiB

Open AccessArticle

A New Efficient Ship Detection Method Based on Remote Sensing Images by Device–Cloud Collaboration

by Tao Liu, Yun Ye, Zhengling Lei, Yuchi Huo, Xiaocai Zhang, Fang Wang, Mei Sha and Huafeng Wu

J. Mar. Sci. Eng. 2024, 12(8), 1422; https://doi.org/10.3390/jmse12081422 - 17 Aug 2024

Viewed by 1266

Abstract

Fast and accurate detection of ship objects in remote sensing images must overcome two critical problems: the complex content of remote sensing images and the large number of small objects reduce ship detection efficiency. In addition, most existing deep learning-based object detection models [...] Read more.

Fast and accurate detection of ship objects in remote sensing images must overcome two critical problems: the complex content of remote sensing images and the large number of small objects reduce ship detection efficiency. In addition, most existing deep learning-based object detection models require vast amounts of computation for training and prediction, making them difficult to deploy on mobile devices. This paper focuses on an efficient and lightweight ship detection model. A new efficient ship detection model based on device–cloud collaboration is proposed, which achieves joint optimization by fusing the semantic segmentation module and the object detection module. We migrate model training, image storage, and semantic segmentation, which require a lot of computational power, to the cloud. For the front end, we design a mask-based detection module that ignores the computation of nonwater regions and reduces the generation and postprocessing time of candidate bounding boxes. In addition, the coordinate attention module and confluence algorithm are introduced to better adapt to the environment with dense small objects and substantial occlusion. Experimental results show that our device–cloud collaborative approach reduces the computational effort while improving the detection speed by 42.6% and also outperforms other methods in terms of detection accuracy and number of parameters. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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14 pages, 7566 KiB

Open AccessArticle

Ship Segmentation via Combined Attention Mechanism and Efficient Channel Attention High-Resolution Representation Network

by Xiaoyi Li

J. Mar. Sci. Eng. 2024, 12(8), 1411; https://doi.org/10.3390/jmse12081411 - 16 Aug 2024

Cited by 2 | Viewed by 1158

Abstract

Ship segmentation with small imaging size, which challenges ship detection and visual navigation model performance due to imaging noise interference, has attracted significant attention in the field. To address the issues, this study proposed a novel combined attention mechanism and efficient channel attention [...] Read more.

Ship segmentation with small imaging size, which challenges ship detection and visual navigation model performance due to imaging noise interference, has attracted significant attention in the field. To address the issues, this study proposed a novel combined attention mechanism and efficient channel attention high-resolution representation network (CA2HRNET). More specially, the proposed model fulfills accurate ship segmentation by introducing a channel attention mechanism, a multi-scale spatial attention mechanism, and a weight self-adjusted attention mechanism. Overall, the proposed CA2HRNET model enhances attention mechanism performance by focusing on the trivial yet important features and pixels of a ship against background-interference pixels. The proposed ship segmentation model can accurately focus on ship features by implementing both channel and spatial fusion attention mechanisms at each scale feature layer. Moreover, the channel attention mechanism helps the proposed framework allocate higher weights to ship-feature-related pixels. The experimental results show that the proposed CA2HRNET model outperforms its counterparts in terms of accuracy (

A c c_{s}

), precision (

P_{c}

), F1-score (

F 1_{s}

), intersection over union (IoU), and frequency-weighted IoU (

F_{IoU}

). The average

A c c_{s}

,

P_{c}

,

F 1_{s}

, IoU, and

F_{IoU}

for the proposed CA2HRNET model were 99.77%, 97.55%, 97%, 96.97%, and 99.55%, respectively. The research findings can promote intelligent ship visual navigation and maritime traffic management in the smart shipping era. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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12 pages, 3922 KiB

Open AccessArticle

Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model

by Yanguo Huang, Sishuo Zhao and Shuling Zhao

J. Mar. Sci. Eng. 2024, 12(8), 1372; https://doi.org/10.3390/jmse12081372 - 12 Aug 2024

Cited by 5 | Viewed by 1831

Abstract

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance [...] Read more.

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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21 pages, 2103 KiB

Open AccessArticle

New Exploration of Emission Abatement Solution for Newbuilding Bulk Carriers

by Shunquan Huang and Yuyang Li

J. Mar. Sci. Eng. 2024, 12(6), 973; https://doi.org/10.3390/jmse12060973 - 10 Jun 2024

Cited by 2 | Viewed by 1446

Abstract

With the implementation of the International Maritime Organization’s (IMO) sulfur cap 2020, shipowners have had to choose suitable sulfur oxide emission abatement solutions to respond to this policy. The use of Very Low Sulfur Fuel Oil (VLSFO) and the installation of scrubbers are [...] Read more.

With the implementation of the International Maritime Organization’s (IMO) sulfur cap 2020, shipowners have had to choose suitable sulfur oxide emission abatement solutions to respond to this policy. The use of Very Low Sulfur Fuel Oil (VLSFO) and the installation of scrubbers are the main response solutions for bulk carriers today. In recent years, the epidemic has gradually improved, and the options facing shipowners may change. Based on the Clarkson Shipping Intelligence Network, this paper collects data related to newbuilding bulk carriers after the implementation of this policy, considers several factors affecting shipowners’ decision, and adopts a machine learning approach for the first time to build a model and make predictions on emission abatement solutions to provide some reference for shipowners to choose a more suitable solution. The results of the study show that the Extreme Gradient Boosting (XGBoost) model is more suitable for the problem studied in this paper, and the highest prediction accuracy of about 84.25% with an Area Under the Curve (AUC) value of 0.9019 is achieved using this model with hyperparameter adjustment based on a stratified sampling divided data set. The model makes good predictions for newbuilding bulk carriers. In addition, the deadweight tonnage and annual distance traveled of a ship have a greater degree of influence on the choice of its option, which can be given priority in the decision making. In contrast to traditional cost–benefit analyses, this study incorporates economic and non-economic factors and uses machine learning methods for effective classification, which have the advantage of being fast, comparable, and highly accurate. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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22 pages, 3867 KiB

Open AccessArticle

Routing a Fleet of Drones from a Base Station for Emission Detection of Moving Ships by Genetic Algorithm

by Xiaoqiong Bao, Zhi-Hua Hu and Yanling Huang

J. Mar. Sci. Eng. 2024, 12(6), 891; https://doi.org/10.3390/jmse12060891 - 27 May 2024

Cited by 2 | Viewed by 1146

Abstract

A fleet of drones is considered in the routing problems with an offshore drone base station, considering the simultaneous movements of drones and ships. A model, entitled meeting model, between a drone and a moving ship is devised, and an extended model is [...] Read more.

A fleet of drones is considered in the routing problems with an offshore drone base station, considering the simultaneous movements of drones and ships. A model, entitled meeting model, between a drone and a moving ship is devised, and an extended model is developed based on the vehicle routing problem model. A genetic algorithm based on a sequential insert heuristic (SIH) is designed to solve the model as a holistic framework with two strategies to determine the sequential assignments of ships to drones, namely, the

D r o n e B y D r o n e,

and

S h i p B y S h i p

strategies. The proposed models and solution algorithms are demonstrated and verified by experiments. Numerical studies show that the

D r o n e B y D r o n e

strategy can overperform the

S h i p B y S h i p

strategy regarding traveling distances. In addition, when considering the simultaneous movement of the ship and drone, improving the drone flying speeds can reduce the flying time of drones rather than optimizing the ship’s moving speed. The managerial implications and possible extensions are discussed based on modeling and experimental studies. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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22 pages, 6700 KiB

Open AccessArticle

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

by Liang Chen and Jiayu Liu

J. Mar. Sci. Eng. 2024, 12(5), 810; https://doi.org/10.3390/jmse12050810 - 13 May 2024

Cited by 1 | Viewed by 1472

Abstract

In the feature identification of maritime VHF radio communication signals, shipborne VHF communication technology follows the same international technical standards formulated by IMO, uses analog communication technology and uses the same communication channel in the same area, and cannot effectively achieve signal feature [...] Read more.

In the feature identification of maritime VHF radio communication signals, shipborne VHF communication technology follows the same international technical standards formulated by IMO, uses analog communication technology and uses the same communication channel in the same area, and cannot effectively achieve signal feature identification by adding feature elements in the process of signal modulation. How to effectively identify the ship using VHF radio has always been a technical difficulty in the field of ship perception. In this paper, based on the convolutional neural network, combined with the feasibility of CAM feature extraction and BiLSTM feature extraction in non-cooperative signal recognition, a deep learning recognition model of shipborne VHF radio communication signals is established, and the deep learning approach is employed to discern the features of VHF signals, thereby accomplishing the identification and classification of transmitting VHF radio stations. Several experiments are designed according to the characteristics of ship communication scenes at sea. The experimental data show that the method proposed in this paper can provide a new feasible path for ship target perception in terms of radio signal characteristics and identification. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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16 pages, 3214 KiB

Open AccessArticle

Research on the Deployment of Professional Rescue Ships for Maritime Traffic Safety under Limited Conditions

by Minghui Shao, Biao Wu, Yan Li and Xiaoli Jiang

J. Mar. Sci. Eng. 2024, 12(3), 497; https://doi.org/10.3390/jmse12030497 - 17 Mar 2024

Cited by 1 | Viewed by 1304

Abstract

This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the jurisdictional waters of [...] Read more.

This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the jurisdictional waters of the Beihai Rescue Bureau. Models including the coverage quality of the jurisdictional waters, the coverage quality in high-risk areas, the maximum coverage of jurisdictional areas, and the maximum coverage of high-risk areas are constructed and solved using 0–1 integer programming. The optimal plan for eight standby points and their corresponding deployment plans for rescue vessels are obtained. A comparison with the current site selection plan of the Beihai Rescue Bureau validates the superiority of the proposed deployment plan for rescue vessel standby points in this paper. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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13 pages, 7014 KiB

Open AccessArticle

Ship Global Traveling Path Optimization via a Novel Non-Dominated Sorting Genetic Algorithm

by Shuling Zhao and Sishuo Zhao

J. Mar. Sci. Eng. 2024, 12(3), 485; https://doi.org/10.3390/jmse12030485 - 14 Mar 2024

Cited by 8 | Viewed by 2205

Abstract

Due to the intensification of economic globalization and the impact of global warming, the development of methods to reduce shipping costs and reduce carbon emissions has become crucial. In this study, a multi-objective optimization algorithm was designed to plan the optimal ship route [...] Read more.

Due to the intensification of economic globalization and the impact of global warming, the development of methods to reduce shipping costs and reduce carbon emissions has become crucial. In this study, a multi-objective optimization algorithm was designed to plan the optimal ship route for safe cross-ocean navigation under complex sea conditions. Based on the traditional non-dominated sorting genetic algorithm, considering ship stability and complex marine environment interference, a non-dominated sorting genetic algorithm model considering energy consumption was designed with the energy consumption and navigation time of the ship as the optimization objectives. The experimental results show that although the proposed method is 101.23 nautical miles more than the large ring route, and the voyage is increased by 10.1 h, the fuel consumption is reduced by 92.24 tons, saving 6.94%. Compared with the traditional genetic algorithm, the voyage distance and time are reduced by 216.93 nautical miles and 7.5 h, and the fuel consumption is reduced by 58.82 tons, which is almost 4.54%. Through experimental verification, the proposed model can obtain punctual routes, avoid areas with bad sea conditions, reduce fuel consumption, and is of great significance for improving the safety and economy of ship routes. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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16 pages, 9074 KiB

Open AccessArticle

Multi-Parameter Fuzzy-Based Neural Network Sensorless PMSM Iterative Learning Control Algorithm for Vibration Suppression of Ship Rim-Driven Thruster

by Zhi Yang, Xinping Yan, Wu Ouyang, Hongfen Bai and Jinhua Xiao

J. Mar. Sci. Eng. 2024, 12(3), 396; https://doi.org/10.3390/jmse12030396 - 25 Feb 2024

Cited by 2 | Viewed by 1783

Abstract

Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which [...] Read more.

Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which the sliding mode coefficients can be adaptively tuned. Similarly, an iterative learning control (ILC) algorithm is presented to enhance the robustness of the velocity adjustment loop. By continuously learning and adjusting the difference between the actual speed and given speed of RDT motor through ILC algorithm, online compensation for the q-axis given current of RDT motor is achieved, thereby suppressing periodic speed fluctuations during motor running. Fuzzy neural network (FNN) training can be used to optimize the STA-SMO and ILC parameters of RDT control system, while improving speed tracking accuracy. Finally, simulation and experimental verifications have been conducted on the vector control system based on the conventional PI-STA and modified ILC-STA. The results show that the modified algorithm can effectively suppress the estimated speed and torque ripple of RDT motor, which greatly improves the speed tracking accuracy. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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16 pages, 859 KiB

Open AccessArticle

An Attention-Averaging-Based Compression Algorithm for Real-Time Transmission of Ship Data via Beidou Navigation System

by Chunchang Zhang and Ji Zeng

J. Mar. Sci. Eng. 2024, 12(2), 300; https://doi.org/10.3390/jmse12020300 - 8 Feb 2024

Cited by 1 | Viewed by 1258

Abstract

The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered [...] Read more.

The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered by the BeiDou-3 navigation satellite system. While this system presents a more cost-effective solution, its bandwidth is notably insufficient for handling real-time ship status data. This inadequacy necessitates the compression of such data. Therefore, this paper introduces an algorithm tailored for real-time compression of sequential ship status data. The algorithm is engineered to ensure both accuracy and the preservation of valid data range integrity. Our methodology integrates quantization, predictive coding employing an attention-averaging-based predictor, and arithmetic coding. This combined approach facilitates the transmission of succinct messages through the BeiDou Navigation System, enabling the live monitoring of ocean-going vessels. Experimental trials conducted with authentic data obtained from ship monitoring systems validate the efficiency of our approach. The achieved compression rates closely approximate theoretical minimum values. Consequently, this method exhibits substantial promise for the real-time transmission of parameters across various systems. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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Review

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24 pages, 3468 KiB

Open AccessReview

Visual Navigation Systems for Maritime Smart Ships: A Survey

by Yuqing Wang, Xinqiang Chen, Yuzhen Wu, Jiansen Zhao, Octavian Postolache and Shuhao Liu

J. Mar. Sci. Eng. 2024, 12(10), 1781; https://doi.org/10.3390/jmse12101781 - 8 Oct 2024

Cited by 1 | Viewed by 3732

Abstract

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are [...] Read more.

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships. Full article

(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)

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