A Time-Domain Numerical Simulation for Free Motion Responses of Two Ships Advancing in Head Waves

The constant panel method within the framework of potential flow theory in the time domain is developed for solving the hydrodynamic interactions between two parallel ships with forward speed.When solving problems within a time domain framework,the free water surface needs to simultaneously satisfy both the kinematic and dynamic boundary conditions of the free water surface.This provides conditions for adding artificial damping layers.Using the Runge−Kutta method to solve equations related to time.An upwind differential scheme is used in the present method to deal with the convection terms on the free surface to prevent waves upstream.Through the comparison with the available experimental data and other numerical methods,the present method is proved to have good mesh convergence,and satisfactory results can be obtained.The constant panel method is applied to calculate the hydrodynamic interaction responses of two parallel ships advancing in head waves.Numerical simulations are conducted on the effects of forward speed,different longitudinal and lateral distances on the motion response of two modified Wigley ships in head waves.Then further investigations are conducted on the effects of different ship types on the motion response.

Multipoint Heave Motion Prediction Method for Ships Based on the PSO-TGCN Model

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.

Review on the Progress and Issues in Liquid Tank Sloshing of Ships

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.

Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

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.

Unmanned Ship Identification Based on Improved YOLOv8s Algorithm

Aiming at defects such as low contrast in infrared ship images,uneven distribution of ship size,and lack of texture details,which will lead to unmanned ship leakage misdetection and slow detection,this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm(R_YOLO).The algorithm incorporates the Efficient Multi-Scale Attention mechanism(EMA),the efficient Reparameterized Generalized-feature extraction module(CSPStage),the small target detection header,the Repulsion Loss function,and the context aggregation block(CABlock),which are designed to improve the model’s ability to detect targets at multiple scales and the speed of model inference.The algorithm is validated in detail on two vessel datasets.The comprehensive experimental results demonstrate that,in the infrared dataset,the YOLOv8s algorithm exhibits improvements in various performance metrics.Specifically,compared to the baseline algorithm,there is a 3.1%increase in mean average precision at a threshold of 0.5(mAP(0.5)),a 5.4%increase in recall rate,and a 2.2%increase in mAP(0.5:0.95).Simultaneously,while less than 5 times parameters,the mAP(0.5)and frames per second(FPS)exhibit an increase of 1.7%and more than 3 times,respectively,compared to the CAA_YOLO algorithm.Finally,the evaluation indexes on the visible light data set have shown an average improvement of 4.5%.

Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.

Structural Characteristics and Evolution of a Weighted Sino-US Container Shipping Network

This study selected the Sino-US route data from the top 30 global container liner companies between December 1,2019,and December 29,2019,as the data source utilizing the complex network research methodology.It constructs a Sino-US container shipping network through voyage weighting and analyzes the essential structural characteristics to explore the network’s complex structural fea-tures.The network’s evolution is examined from three perspectives,namely,time,space,and event influence,aiming to comprehens-ively explore the network’s evolution mechanism.The results revealed that:1)the weighted Sino-US container shipping network exhib-its small-world and scale-free properties.Key hub ports in the United States include NEW YORK NY,SAVANNAH GA,LOS ANGELES CA,and OAKLAND CA,whereas SHANGHAI serving as the hub port in China.The geographical distribution of these hub ports is uneven.2)Concerning the evolution of the weighted Sino-US container shipping network,from a temporal perspective,the evolution of the regional structure of the entire Sino-US region and the Inland United States is in a stage of radiative expansion and de-velopment,with a need for further enhancement in competitiveness and development speed.The evolution of the regional structure of southern China and Europe is transitioning from the stage of radiative expansion and development to an advanced equilibrium stage.The shipping development in Northern China,the Western and Eastern United States,and Asia is undergoing significant changes but faces challenges of fierce competition and imbalances.From a spatial perspective,the rationality and effectiveness of the improved weighted Barrat-Barthelemy-Vespignani(BBV)model are confirmed through theoretical derivation.The applicability of the improved evolution model is verified by simulating the evolution of the weighted Sino-US container shipping network.From an event impact per-spective,the Corona Virus Disease 2019(COVID-19)pandemic has not fundamentally affected the spatial pattern of the weighted Sino-US container shipping network but has significantly impacted the network’s connectivity.The network lacks sufficient resilience and stability in emergency situations.3)Based on the analysis of the structural characteristics and evolution of the weighted Sino-US con-tainer shipping network,recommendations for network development are proposed from three aspects:emphasizing the development of hub ports,focusing on the balanced development of the network,and optimizing the layout of Chinese ports.

Improved Scatter Search Algorithm for Multi-skilled Personnel Scheduling of Ship Block Painting

This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.

Decoding RoPax Ship Capsizes and Development of an ISO Ship Safety Standard

Marine accidents often result in significant losses of human life, environmental damage, and property destruction. Additionally, ships and offshore plants are large-scale and complex systems, making safety assessments challenging. However, the advent of onboard electronic systems has made it possible to monitor and respond more effectively. These new technologies can enhance safety levels while reducing the workload on crews. In this paper, authors analyze recent accidents involving ships with high structures above the water, such as car carriers or RoPax vessels, and propose preventive safety indicators to help prevent similar accidents from recurring.

Sending The King Ship, A Tradition Connecting Two Peoples

“The sky is dark,and it is about to rain,”goes a lyric from China’s coastal Minnan(southern Fujian)region.“The king ship is leaving the bay,papa is going out to sea,and mama is sending the ship off.May it bring us wealth,food,and the gods’protection.”The 600-year-old custom is called Ong Chun,Wangchuan,Wangkang,or“Sending the King Ship.”

Ship recognition based on HRRP via multi-scale sparse preserving method

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

Novel method for extraction of ship target with overlaps in SAR image via EM algorithm

The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.

欧盟AUTOSHIP项目

欧洲水域自主航运倡议项目(Autonomous Shipping Initiative for European Waters,AUTOSHIP项目)由意大利Ciaotech S.r.l.公司牵头,联合康士伯等其他欧洲国家合作伙伴共同完成。该项目通过在不同环境下运营的两艘不同类型船舶上安装和测试自主航行设施,加速新一代自主航行船舶发展,并为欧盟实现船舶自主航行制定商业化路线图。

Corporate Social Responsibility and Shipping Supply Chain Risks

This study aims to investigate whether Corporate Social Responsibility(CSR)activities reduce supply chain disruptions by examining the impact of the Suez Canal obstruction on the Ever Given container ship in March 2021.This study conclude that the more responsible companies have higher returns and are less affected by this event than the less responsible companies;the less responsible companies have lower returns.The companies with better CSR have a lower impact on their supply chains when faced with disruptions in the supply chain.

Applying the vantage PDMS to jack-up drilling ships

The plant design management system （PDMS） is an integrated application which includes a database and is useful when designing complex 3-D industrial projects. It could be used to simplify the most difficult part of a subsea oil extraction project-detailed pipeline design. It could also be used to integrate the design of equipment, structures, HVAC, E-ways as well as the detailed designs of other specialists. This article mainly examines the applicability of the Vantage PDMS database to pipeline projects involving jack-up drilling ships. It discusses the catalogue （CATA） of the pipeline, the spec-world （SPWL） of the pipeline, the bolt tables （BLTA） and so on. This article explains the main methods for CATA construction as well as problem in the process of construction. In this article, the authors point out matters needing attention when using the Vantage PDMS database in the design process and discuss partial solutions to these questions.

Development and Management Status of Voluntary Observing Ships in China

Voluntary observing ship （VOS） observations are international obligations that must be fulfilled by China. Currently, the number of Chinese VOSs is showing a decreasing trend, which has decreased from more than one hundred ships in the past to the current number of thirty something ships. Moreover, the observation capabilities have many existing problems, such as relatively outdated observation measures, simple observation parameters, and lack of observation data. Fundamentally speaking, the operation mechanism of VOSs lacks effective systematic assurance and protection. Consequently, these VOSs are unable to have sufficient operational capabilities and cannot effectively fulfill their international obligations.

Numerical Simulation and Experimental Research on Wake Field of Ships Under off-Design Conditions

Different operating conditions （e.g. design and off-design） may lead to a significant difference in the hydrodynamics performance of a ship, especially in the total resistance and wake field of ships. This work investigated the hydrodynamic performance of the well-known KRISO 3600 TEU Container Ship （KCS） under three different operating conditions by means of Particle Image Velocimetry （P/V） and Computational Fluid Dynamics （CFD）. The comparison results show that the use of PIV to measure a ship＇s nominal wake field is an important method which has the advantages of being contactless and highly accurate. Acceptable agreements between the results obtained by the two different methods are achieved. Results indicate that the total resistances of the KCS model under two off-design conditions are 23.88% and 13.92% larger than that under the designed condition, respectively.

Operational Options for Green Ships

Environmental issues and rising fuel prices necessitate better energy-etlicwncy in all sectors, The shipping industry is one of the major stakeholders, responsible for 3% of global CO2 emissions, 14%-15% of global NOx emissions, and 16% of global SOx emissions. In addition, continuously rising fuel prices are also an incentive to focus on new ways for better energy-effectiveness. The green ship concept requires exploring and implementing technology on ships to increase energy-efficiency and reduce emissions. Ship operation is an important topic with large potential to increase cost-and-energy-effectiveness. This paper provided a comprehensive review of basic concepts, principles, and potential of operational options for green ships. The key challenges pertaining to ship crew i.e. academic qualifications prior to induction, in-service training and motivation were discussed. The author also deliberated on remedies to these challenges.

Path following control of underactuated ships based on nonswitch analytic model predictive control

A path following controller is developed for underactuated ships with only surge force and yaw moment available to follow a predefined path.The proposed controller is based on nonswitch analytic model predictive control.It is shown that the optimal control law for a nonlinear path following system with ill-defined relative degree is continuous and nonsingular.The problem of ill-defined relative degree is solved.The path-following ability of the nonlinear system is guaranteed.Numerical simulations are provided to demonstrate the effectiveness of the proposed control law.