During ship operations,frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading.The existing heave compensation systems suffer from issues such as dead...During ship operations,frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading.The existing heave compensation systems suffer from issues such as dead zones and control system time lags,which necessitate the development of reasonable prediction models for ship heave movements.In this paper,a novel model based on a time graph convolutional neural network algorithm and particle swarm optimization algorithm(PSO-TGCN)is proposed for the first time to predict the multipoint heave movements of ships under different sea conditions.To enhance the dataset's suitability for training and reduce interference,various filter algorithms are employed to optimize the dataset.The training process utilizes simulated heave data under different sea conditions and measured heave data from multiple points.The results show that the PSO-TGCN model predicts the ship swaying motion in different sea states after 2 s with 84.7%accuracy,while predicting the swaying motion in three different positions.By performing a comparative study,it was also found that the present method achieves better performance that other popular methods.This model can provide technical support for intelligent ship control,improve the control accuracy of intelligent ships,and promote the development of intelligent ships.展开更多
With the development of large liquid cargo ships,liquid tank sloshing has gradually become a hot research topic in the area of shipping and ocean Engineering.Liquid tank sloshing,characterized by strong nonlinearity a...With the development of large liquid cargo ships,liquid tank sloshing has gradually become a hot research topic in the area of shipping and ocean Engineering.Liquid tank sloshing,characterized by strong nonlinearity and randomness,not only affects the stability of the ship but also generates a huge impact force on the wall of the tank.To further investigate liquid tank sloshing,a comprehensive review is given on the research process of the most focused subjects of liquid sloshing.Summarizing the existing research will help to identify issues in the current field and provide useful references.The methods for investigating sloshing,the research progress and the situations worldwide are discussed.The advantages and defects of experiments and numerical simulations are also explored.The problems which need to be explored in the future are subsequently proposed.展开更多
In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficien...In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.展开更多
At present,the commercial operation of tankers on spot freight market is focused on the maximization of the daily earnings by careful selection of factors such as speed and utilization,while it is subject to market fr...At present,the commercial operation of tankers on spot freight market is focused on the maximization of the daily earnings by careful selection of factors such as speed and utilization,while it is subject to market freight rates and bunker prices.With the expected introduction of carbon tax,the daily earnings will be affected.This will have an effect on the way a ship is employed and operated.While the exact form of carbon tax is not yet known,it can be shown that the optimum speed will be reduced,and the level of reduction of optimum speed will depend on factors related to the freight and bunker markets as well as on the technical characteristics of the ship.The effect of reducing the emission index such as𝐶𝐼𝐼(carbon intensity indicator)below a desirable level is also examined.It is shown that all related factors will play a role,the extent of which depends on their fundamental relation to total emissions and ton miles.The article also examines the speed reduction needed to bring the𝐶𝐼𝐼to desirable level after it has exceeded it due to previous Charterer’s trading pattern.展开更多
基金financially supported by the National Key Research and Development Program of China (Grant No.2022YFE010700)the National Natural Science Foundation of China (Grant No.52171259)+1 种基金the High-Tech Ship Research Project of Ministry of Industry and Information Technology (Grant No.[2021]342)Foundation of State Key Laboratory of Ocean Engineering in Shanghai Jiao Tong University (Grant No.GKZD010086-2)。
文摘During ship operations,frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading.The existing heave compensation systems suffer from issues such as dead zones and control system time lags,which necessitate the development of reasonable prediction models for ship heave movements.In this paper,a novel model based on a time graph convolutional neural network algorithm and particle swarm optimization algorithm(PSO-TGCN)is proposed for the first time to predict the multipoint heave movements of ships under different sea conditions.To enhance the dataset's suitability for training and reduce interference,various filter algorithms are employed to optimize the dataset.The training process utilizes simulated heave data under different sea conditions and measured heave data from multiple points.The results show that the PSO-TGCN model predicts the ship swaying motion in different sea states after 2 s with 84.7%accuracy,while predicting the swaying motion in three different positions.By performing a comparative study,it was also found that the present method achieves better performance that other popular methods.This model can provide technical support for intelligent ship control,improve the control accuracy of intelligent ships,and promote the development of intelligent ships.
基金financially supported by the National Natural Science Foundation of China(Grant No.52271271)the National Key Research and Development Program of China(Grant No.2022YFE0104500)+1 种基金“Pioneer”and“Leading Goose”R&D Program of Zhejiang Province(Grant No.2022C03023)Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ17E090003)。
文摘With the development of large liquid cargo ships,liquid tank sloshing has gradually become a hot research topic in the area of shipping and ocean Engineering.Liquid tank sloshing,characterized by strong nonlinearity and randomness,not only affects the stability of the ship but also generates a huge impact force on the wall of the tank.To further investigate liquid tank sloshing,a comprehensive review is given on the research process of the most focused subjects of liquid sloshing.Summarizing the existing research will help to identify issues in the current field and provide useful references.The methods for investigating sloshing,the research progress and the situations worldwide are discussed.The advantages and defects of experiments and numerical simulations are also explored.The problems which need to be explored in the future are subsequently proposed.
文摘In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.
文摘At present,the commercial operation of tankers on spot freight market is focused on the maximization of the daily earnings by careful selection of factors such as speed and utilization,while it is subject to market freight rates and bunker prices.With the expected introduction of carbon tax,the daily earnings will be affected.This will have an effect on the way a ship is employed and operated.While the exact form of carbon tax is not yet known,it can be shown that the optimum speed will be reduced,and the level of reduction of optimum speed will depend on factors related to the freight and bunker markets as well as on the technical characteristics of the ship.The effect of reducing the emission index such as𝐶𝐼𝐼(carbon intensity indicator)below a desirable level is also examined.It is shown that all related factors will play a role,the extent of which depends on their fundamental relation to total emissions and ton miles.The article also examines the speed reduction needed to bring the𝐶𝐼𝐼to desirable level after it has exceeded it due to previous Charterer’s trading pattern.
