Pub Date : 2024-02-14DOI: 10.1177/14750902241228628
P. Georgiev, Y. Garbatov
This work analyses the operational characteristics, including the fuel consumption and carbon emissions of self-reported data of container vessels performing regular short sea voyages in the Black Sea. The data are extracted from the EMSA THETIS-MRV database covering the period of 2018–2021. The main particulars of container ships that visited six Black Sea ports were collected from the Marine Traffic platform and Equasis database. Several conclusions about the status of carbon emissions from container shipping in the Black Sea are presented, which may support the short shipping companies in developing measures to incentivise decarbonisation in the region. It has been identified that the correlation between ship deadweight and total fuel consumption and CO2 emissions is weaker than the correlation between distance travelled and transport work. The decrease in CO2 emissions in 2021 can be attributed to reduced ship activity and emission-reduction measures. The ratios of CO2-related indicators to ship deadweight are more diminutive, showing a lack of correlation. An approach based on the confidence ellipses is employed in performing an innovative analysis of the CO2 emissions over the years for container ships operating in the Black Sea. This is important due to the new requests for using EEXI and CII.
{"title":"Carbon emissions statistical analysis for container shipping in the Black Sea","authors":"P. Georgiev, Y. Garbatov","doi":"10.1177/14750902241228628","DOIUrl":"https://doi.org/10.1177/14750902241228628","url":null,"abstract":"This work analyses the operational characteristics, including the fuel consumption and carbon emissions of self-reported data of container vessels performing regular short sea voyages in the Black Sea. The data are extracted from the EMSA THETIS-MRV database covering the period of 2018–2021. The main particulars of container ships that visited six Black Sea ports were collected from the Marine Traffic platform and Equasis database. Several conclusions about the status of carbon emissions from container shipping in the Black Sea are presented, which may support the short shipping companies in developing measures to incentivise decarbonisation in the region. It has been identified that the correlation between ship deadweight and total fuel consumption and CO2 emissions is weaker than the correlation between distance travelled and transport work. The decrease in CO2 emissions in 2021 can be attributed to reduced ship activity and emission-reduction measures. The ratios of CO2-related indicators to ship deadweight are more diminutive, showing a lack of correlation. An approach based on the confidence ellipses is employed in performing an innovative analysis of the CO2 emissions over the years for container ships operating in the Black Sea. This is important due to the new requests for using EEXI and CII.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1177/14750902241228628
P. Georgiev, Y. Garbatov
This work analyses the operational characteristics, including the fuel consumption and carbon emissions of self-reported data of container vessels performing regular short sea voyages in the Black Sea. The data are extracted from the EMSA THETIS-MRV database covering the period of 2018–2021. The main particulars of container ships that visited six Black Sea ports were collected from the Marine Traffic platform and Equasis database. Several conclusions about the status of carbon emissions from container shipping in the Black Sea are presented, which may support the short shipping companies in developing measures to incentivise decarbonisation in the region. It has been identified that the correlation between ship deadweight and total fuel consumption and CO2 emissions is weaker than the correlation between distance travelled and transport work. The decrease in CO2 emissions in 2021 can be attributed to reduced ship activity and emission-reduction measures. The ratios of CO2-related indicators to ship deadweight are more diminutive, showing a lack of correlation. An approach based on the confidence ellipses is employed in performing an innovative analysis of the CO2 emissions over the years for container ships operating in the Black Sea. This is important due to the new requests for using EEXI and CII.
{"title":"Carbon emissions statistical analysis for container shipping in the Black Sea","authors":"P. Georgiev, Y. Garbatov","doi":"10.1177/14750902241228628","DOIUrl":"https://doi.org/10.1177/14750902241228628","url":null,"abstract":"This work analyses the operational characteristics, including the fuel consumption and carbon emissions of self-reported data of container vessels performing regular short sea voyages in the Black Sea. The data are extracted from the EMSA THETIS-MRV database covering the period of 2018–2021. The main particulars of container ships that visited six Black Sea ports were collected from the Marine Traffic platform and Equasis database. Several conclusions about the status of carbon emissions from container shipping in the Black Sea are presented, which may support the short shipping companies in developing measures to incentivise decarbonisation in the region. It has been identified that the correlation between ship deadweight and total fuel consumption and CO2 emissions is weaker than the correlation between distance travelled and transport work. The decrease in CO2 emissions in 2021 can be attributed to reduced ship activity and emission-reduction measures. The ratios of CO2-related indicators to ship deadweight are more diminutive, showing a lack of correlation. An approach based on the confidence ellipses is employed in performing an innovative analysis of the CO2 emissions over the years for container ships operating in the Black Sea. This is important due to the new requests for using EEXI and CII.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1177/14750902241227255
Jae-Yong Kim, Suk-Yoon Hong, Jee-hun Song, H. Kwon
As the technological development of modern weapons systems progresses, attack and defense against the weapons systems of the past can become useless. In the case of underwater weapon systems, improvements in sonar and torpedo technology necessitate enhancements in submarine stealth performance. The stealth performance of a submarine is analyzed through the acoustic target strength (TS), which is indicated by the degree of sound wave reflection in the sonar equation. Various methods have been researched to analyze the TS of submarines, but methods considering the Anechoic coating attached to submarines are rare. Coating-related research has mainly focused on the absorption characteristics of coatings attached to flat surfaces. Therefore, there is an increasing need for research to analyze the sound absorption performance of anechoic coatings considering the curved surface of a submarine. In this study, we propose a method to verify the stealth performance of a submarine by analyzing the TS according to the azimuth angle by applying periodic inclusion of anechoic coating (PIAC) to the submarine. The PIAC based on the meta-structure is designed to form a bandgap in the low-frequency region by considering the resonance of the cylindrical Inclusion and the air cavity, applying the Bloch Theorem and the Brillouin zone, as well as the periodic arrangement characteristics. The PIAC was attached to the submarine model and modeled, and the absorption and reflection coefficients according to the azimuth angle were calculated using the finite element equation. The TS analysis included the process of determining the effective area by combining Geometrical Optics (GO) based on ray tracing and Physical Optics (PO) using the Kirchhoff approximation. The stealth performance was verified by comparing the TS analysis results according to the azimuth angle of the submarine equipped with the existing Alberich coating and the submarine equipped with PIAC at the same frequency.
{"title":"Target strength analysis using reflection coefficient of a submarine with low-frequency anechoic coatings","authors":"Jae-Yong Kim, Suk-Yoon Hong, Jee-hun Song, H. Kwon","doi":"10.1177/14750902241227255","DOIUrl":"https://doi.org/10.1177/14750902241227255","url":null,"abstract":"As the technological development of modern weapons systems progresses, attack and defense against the weapons systems of the past can become useless. In the case of underwater weapon systems, improvements in sonar and torpedo technology necessitate enhancements in submarine stealth performance. The stealth performance of a submarine is analyzed through the acoustic target strength (TS), which is indicated by the degree of sound wave reflection in the sonar equation. Various methods have been researched to analyze the TS of submarines, but methods considering the Anechoic coating attached to submarines are rare. Coating-related research has mainly focused on the absorption characteristics of coatings attached to flat surfaces. Therefore, there is an increasing need for research to analyze the sound absorption performance of anechoic coatings considering the curved surface of a submarine. In this study, we propose a method to verify the stealth performance of a submarine by analyzing the TS according to the azimuth angle by applying periodic inclusion of anechoic coating (PIAC) to the submarine. The PIAC based on the meta-structure is designed to form a bandgap in the low-frequency region by considering the resonance of the cylindrical Inclusion and the air cavity, applying the Bloch Theorem and the Brillouin zone, as well as the periodic arrangement characteristics. The PIAC was attached to the submarine model and modeled, and the absorption and reflection coefficients according to the azimuth angle were calculated using the finite element equation. The TS analysis included the process of determining the effective area by combining Geometrical Optics (GO) based on ray tracing and Physical Optics (PO) using the Kirchhoff approximation. The stealth performance was verified by comparing the TS analysis results according to the azimuth angle of the submarine equipped with the existing Alberich coating and the submarine equipped with PIAC at the same frequency.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1177/14750902231225694
Farnaz Fereidoonian, S. Sadjadi, Mahdi Heydari, S. M. Mirzapour Al-e-Hashem
In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships’ loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers’ flow time, trucks’ emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks’ technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem’s NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time.
{"title":"A timely efficient and emissions-aware multiobjective truck-sharing integrated scheduling model in container terminals","authors":"Farnaz Fereidoonian, S. Sadjadi, Mahdi Heydari, S. M. Mirzapour Al-e-Hashem","doi":"10.1177/14750902231225694","DOIUrl":"https://doi.org/10.1177/14750902231225694","url":null,"abstract":"In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships’ loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers’ flow time, trucks’ emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks’ technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem’s NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1177/14750902231225694
Farnaz Fereidoonian, S. Sadjadi, Mahdi Heydari, S. M. Mirzapour Al-e-Hashem
In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships’ loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers’ flow time, trucks’ emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks’ technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem’s NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time.
{"title":"A timely efficient and emissions-aware multiobjective truck-sharing integrated scheduling model in container terminals","authors":"Farnaz Fereidoonian, S. Sadjadi, Mahdi Heydari, S. M. Mirzapour Al-e-Hashem","doi":"10.1177/14750902231225694","DOIUrl":"https://doi.org/10.1177/14750902231225694","url":null,"abstract":"In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships’ loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers’ flow time, trucks’ emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks’ technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem’s NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139795437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1177/14750902241227255
Jae-Yong Kim, Suk-Yoon Hong, Jee-hun Song, H. Kwon
As the technological development of modern weapons systems progresses, attack and defense against the weapons systems of the past can become useless. In the case of underwater weapon systems, improvements in sonar and torpedo technology necessitate enhancements in submarine stealth performance. The stealth performance of a submarine is analyzed through the acoustic target strength (TS), which is indicated by the degree of sound wave reflection in the sonar equation. Various methods have been researched to analyze the TS of submarines, but methods considering the Anechoic coating attached to submarines are rare. Coating-related research has mainly focused on the absorption characteristics of coatings attached to flat surfaces. Therefore, there is an increasing need for research to analyze the sound absorption performance of anechoic coatings considering the curved surface of a submarine. In this study, we propose a method to verify the stealth performance of a submarine by analyzing the TS according to the azimuth angle by applying periodic inclusion of anechoic coating (PIAC) to the submarine. The PIAC based on the meta-structure is designed to form a bandgap in the low-frequency region by considering the resonance of the cylindrical Inclusion and the air cavity, applying the Bloch Theorem and the Brillouin zone, as well as the periodic arrangement characteristics. The PIAC was attached to the submarine model and modeled, and the absorption and reflection coefficients according to the azimuth angle were calculated using the finite element equation. The TS analysis included the process of determining the effective area by combining Geometrical Optics (GO) based on ray tracing and Physical Optics (PO) using the Kirchhoff approximation. The stealth performance was verified by comparing the TS analysis results according to the azimuth angle of the submarine equipped with the existing Alberich coating and the submarine equipped with PIAC at the same frequency.
{"title":"Target strength analysis using reflection coefficient of a submarine with low-frequency anechoic coatings","authors":"Jae-Yong Kim, Suk-Yoon Hong, Jee-hun Song, H. Kwon","doi":"10.1177/14750902241227255","DOIUrl":"https://doi.org/10.1177/14750902241227255","url":null,"abstract":"As the technological development of modern weapons systems progresses, attack and defense against the weapons systems of the past can become useless. In the case of underwater weapon systems, improvements in sonar and torpedo technology necessitate enhancements in submarine stealth performance. The stealth performance of a submarine is analyzed through the acoustic target strength (TS), which is indicated by the degree of sound wave reflection in the sonar equation. Various methods have been researched to analyze the TS of submarines, but methods considering the Anechoic coating attached to submarines are rare. Coating-related research has mainly focused on the absorption characteristics of coatings attached to flat surfaces. Therefore, there is an increasing need for research to analyze the sound absorption performance of anechoic coatings considering the curved surface of a submarine. In this study, we propose a method to verify the stealth performance of a submarine by analyzing the TS according to the azimuth angle by applying periodic inclusion of anechoic coating (PIAC) to the submarine. The PIAC based on the meta-structure is designed to form a bandgap in the low-frequency region by considering the resonance of the cylindrical Inclusion and the air cavity, applying the Bloch Theorem and the Brillouin zone, as well as the periodic arrangement characteristics. The PIAC was attached to the submarine model and modeled, and the absorption and reflection coefficients according to the azimuth angle were calculated using the finite element equation. The TS analysis included the process of determining the effective area by combining Geometrical Optics (GO) based on ray tracing and Physical Optics (PO) using the Kirchhoff approximation. The stealth performance was verified by comparing the TS analysis results according to the azimuth angle of the submarine equipped with the existing Alberich coating and the submarine equipped with PIAC at the same frequency.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1177/14750902231226162
Jian Cen, Jiaxi Li, Xi Liu, Jiahao Chen, Haisheng Li, Weisheng Huang, Linzhe Zeng, Jun-Xi Kang, Silin Ke
With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.
随着全球航运量的增加和海上运输系统的复杂化，船舶轨迹预测成为提高海上安全的重要工具。然而，现有的船舶轨迹预测方法大多只关注单一特征，无法融合高维特征。为了解决这些问题，本文基于自动识别系统（AIS）数据，提出了具有混合注意机制（AM）的 CNN-GRU 模型。首先提出了卷积神经网络（CNN）来提取轨迹数据的时空信息。然后设计一个门控递归单元（GRU）来提取轨迹的时间关系。最后，引入 AM 来学习深层特征并预测船只轨迹。为了验证模型的有效性，我们在三个真实的 AIS 数据集上进行了实验。与其他模型相比，该方法具有较高的轨迹预测精度。
{"title":"A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU","authors":"Jian Cen, Jiaxi Li, Xi Liu, Jiahao Chen, Haisheng Li, Weisheng Huang, Linzhe Zeng, Jun-Xi Kang, Silin Ke","doi":"10.1177/14750902231226162","DOIUrl":"https://doi.org/10.1177/14750902231226162","url":null,"abstract":"With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1177/14750902231226162
Jian Cen, Jiaxi Li, Xi Liu, Jiahao Chen, Haisheng Li, Weisheng Huang, Linzhe Zeng, Jun-Xi Kang, Silin Ke
With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.
随着全球航运量的增加和海上运输系统的复杂化，船舶轨迹预测成为提高海上安全的重要工具。然而，现有的船舶轨迹预测方法大多只关注单一特征，无法融合高维特征。为了解决这些问题，本文基于自动识别系统（AIS）数据，提出了具有混合注意机制（AM）的 CNN-GRU 模型。首先提出了卷积神经网络（CNN）来提取轨迹数据的时空信息。然后设计一个门控递归单元（GRU）来提取轨迹的时间关系。最后，引入 AM 来学习深层特征并预测船只轨迹。为了验证模型的有效性，我们在三个真实的 AIS 数据集上进行了实验。与其他模型相比，该方法具有较高的轨迹预测精度。
{"title":"A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU","authors":"Jian Cen, Jiaxi Li, Xi Liu, Jiahao Chen, Haisheng Li, Weisheng Huang, Linzhe Zeng, Jun-Xi Kang, Silin Ke","doi":"10.1177/14750902231226162","DOIUrl":"https://doi.org/10.1177/14750902231226162","url":null,"abstract":"With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-30DOI: 10.1177/14750902241227301
Honggen Zhou, Chaojie Bao, Bo Deng, Lei Li
Due to the advantages such as high efficiency, high precision, and the ability to reduce welding distortion, the block assembly method in shipbuilding possesses currently holds a dominant position in shipbuilding engineering. However, some key issues including low adjustment precision and slow control response speed urgently need to be resolved for the block assembly adjustment technology. This paper committed to solving the problems of inaccurate tracking of target displacement and slow control response speed in the vertical motion axis of the ship block joining equipment. A docking equipment control method based on the RBF neural network coupled with adaptive sliding mode algorithm was proposed. Firstly, an overview of the overall mechanics and control architecture of the ship block joining equipment was provided. Subsequently, a mathematical model for the transmission at the lifting mechanism was established. A sliding mode controller based on position control for the ship block joining equipment was designed for the transmission system. Then, the RBF neural network was employed to adjust the switching gain of the sliding mode controller and develop a self-adaptive sliding mode controller. Finally, simulations and verifications were conducted for multiple sets of input trajectories with different types. The results demonstrated that the combination of the neural network algorithm and the sliding mode control algorithm model presented in this paper reduces the system response time by 28.125% and improves the average motion tracking accuracy by 30.76%.
{"title":"Modeling and controlling of ship general section attitude adjustment process based on RBF neural network coupled with sliding mode algorithm","authors":"Honggen Zhou, Chaojie Bao, Bo Deng, Lei Li","doi":"10.1177/14750902241227301","DOIUrl":"https://doi.org/10.1177/14750902241227301","url":null,"abstract":"Due to the advantages such as high efficiency, high precision, and the ability to reduce welding distortion, the block assembly method in shipbuilding possesses currently holds a dominant position in shipbuilding engineering. However, some key issues including low adjustment precision and slow control response speed urgently need to be resolved for the block assembly adjustment technology. This paper committed to solving the problems of inaccurate tracking of target displacement and slow control response speed in the vertical motion axis of the ship block joining equipment. A docking equipment control method based on the RBF neural network coupled with adaptive sliding mode algorithm was proposed. Firstly, an overview of the overall mechanics and control architecture of the ship block joining equipment was provided. Subsequently, a mathematical model for the transmission at the lifting mechanism was established. A sliding mode controller based on position control for the ship block joining equipment was designed for the transmission system. Then, the RBF neural network was employed to adjust the switching gain of the sliding mode controller and develop a self-adaptive sliding mode controller. Finally, simulations and verifications were conducted for multiple sets of input trajectories with different types. The results demonstrated that the combination of the neural network algorithm and the sliding mode control algorithm model presented in this paper reduces the system response time by 28.125% and improves the average motion tracking accuracy by 30.76%.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1177/14750902241227254
Shen Li, Feargal Brennan
This paper presents a critical review of literature on the emerging technology known as digital twin and its application in structural integrity management for marine structures. The review defines digital twin in relation to structural integrity management as a virtual representation of a physical structure that mirrors the same structural conditions in real time. Twinning is a dynamic process that involves reducing the discrepancy between the virtual representation and physical structure, which is achieved with the aid of monitored data. Regarding the state-of-the-art concerning marine structure applications, all require the creation of a finite element model to represent the physical structure. Several practical schemes for physical to virtual interconnection have been proposed, but few researchers have concentrated on virtual to physical feedback. In addition, most works have focused only on assessing the current states of structures. To address this, a digital twin-based monitoring framework is proposed and three key enabling technologies, namely model updating, real-time simulation, and data-driven forecasting are demonstrated using a numerical case study. Such technologies enable structural diagnostics, as well as prognostics, to support decision making such as inspection/maintenance planning. Based on the case study, the opportunities and associated challenges of digital twin are discussed. For instance, to fully exploit the potential of digital twin, challenges related to monitoring systems such as standardisation, enhanced redundancy for long-term application, and monitored data quality assurance need to be addressed. Further, because digital twin can avail a vast amount of data, a dedicated data mining capability should also be incorporated.
{"title":"Digital twin enabled structural integrity management: Critical review and framework development","authors":"Shen Li, Feargal Brennan","doi":"10.1177/14750902241227254","DOIUrl":"https://doi.org/10.1177/14750902241227254","url":null,"abstract":"This paper presents a critical review of literature on the emerging technology known as digital twin and its application in structural integrity management for marine structures. The review defines digital twin in relation to structural integrity management as a virtual representation of a physical structure that mirrors the same structural conditions in real time. Twinning is a dynamic process that involves reducing the discrepancy between the virtual representation and physical structure, which is achieved with the aid of monitored data. Regarding the state-of-the-art concerning marine structure applications, all require the creation of a finite element model to represent the physical structure. Several practical schemes for physical to virtual interconnection have been proposed, but few researchers have concentrated on virtual to physical feedback. In addition, most works have focused only on assessing the current states of structures. To address this, a digital twin-based monitoring framework is proposed and three key enabling technologies, namely model updating, real-time simulation, and data-driven forecasting are demonstrated using a numerical case study. Such technologies enable structural diagnostics, as well as prognostics, to support decision making such as inspection/maintenance planning. Based on the case study, the opportunities and associated challenges of digital twin are discussed. For instance, to fully exploit the potential of digital twin, challenges related to monitoring systems such as standardisation, enhanced redundancy for long-term application, and monitored data quality assurance need to be addressed. Further, because digital twin can avail a vast amount of data, a dedicated data mining capability should also be incorporated.","PeriodicalId":20667,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139952084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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