The accurate prediction of vessel trajectory is of crucial importance in order to improve navigational efficiency, optimize routes, enhance the effectiveness of search and rescue operations at sea, and ensure maritime safety. However, the spatial interaction among vessels can have a certain impact on the prediction accuracy of the models. To overcome such a problem in predicting the vessel trajectory, this research proposes a novel hybrid methodology incorporating the graph attention network (GAT) and long short-term memory network (LSTM). The proposed GAT-LSTM model can comprehensively consider spatio-temporal features in the prediction process, which is expected to significantly improve the accuracy and robustness of the trajectory prediction. The Automatic Identification System (AIS) data from the surrounding waters of Xiamen Port is collected and utilized as the empirical case for model validation. The experimental results demonstrate that the GAT-LSTM model outperforms the best baseline model in terms of the reduction on the average displacement error and final displacement error, which are 44.52% and 56.20%, respectively. These improvements will translate into more accurate vessel trajectories, helping to minimize route deviations and improve the accuracy of collision avoidance systems, so that this research can effectively provide support for warning about potential collisions and reducing the risk of maritime accidents. 

Vessel Trajectory Prediction for Enhanced Maritime Navigation Safety: A Novel Hybrid Methodology

This study integrates the array sensing module and the flow leakage algorithm. In this study, a real-time monitoring deep-sea pipeline damage sensing system is designed to provide decision-making parameters such as damage coordinates and damage area. The array sensor module is composed of multiple YF-S201 hall sensors and controllers. YF-S201 hall sensors are arranged inside the pipeline in an array. The flow signal in the deep-sea pipeline can be transmitted to the electronic control interface to analyze the leakage position and leakage flowrate of the pipeline. The theory of this system is based on the conservation of mass. Through the flow of each sensor, it is judged whether the pipeline is damaged. When the pipeline is not damaged, the flowrate of each sensor is almost the same. When the pipeline is damaged, the flowrate will drop significantly. When the actual size of leakage in the pipeline is 5.28 cm2, the size calculated by the flowrate of hall sensors is 2.58 cm2 in average, indicating the error between experimental data and theoretical data is 46%. When the actual size of leakage in the pipeline is 1.98 cm2, the size calculated by the flowrate of hall sensors is 1.31 cm2 in average, indicating the error between experimental data and theoretical data is 21%. This can accurately confirm the location of the broken pipeline, which is between sensor A and sensor B, so that the AUV/ROV can accurately locate and perform pipeline maintenance in real time. It is expected to be able to monitor the flowrate through the array magnetic sensing module designed in this study. It can grasp the status of deep-sea pipelines, improve the quality of deep-sea extraction and pipeline maintenance speed.

https://www.mdpi.com/1424-8220/22/18/6846/pdf?version=1663145088

Design and Testing of Real-Time Sensing System Used in Predicting the Leakage of Subsea Pipeline

Metal magnetic memory testing (MMMT) is a nondestructive testing technique that can detect early signs of damage in components. Many scholars have studied the effect of uniaxial stress on the self-magnetic-leakage field (SMLF)’s strength. Nevertheless, there is still insufficient research on the combined action of bending and shear. We studied the law of distribution of the magnetic signal, ΔHSF(y), at different stress parts of a steel box girder and the quantitative relationship between the magnetic characteristic parameters and the external load. The results showed that the MMMT could accurately detect the early stress concentration zone (SCZ) and predict the final buckling zone of steel box girders. It could be judged that the corresponding parts of the steel box girder had entered the elastic-plastic working stage by the reverse change of the  ΔHSF(y)-F and |HSF(y)|a -F curve trends, this feature could be used as an early warning sign before the steel box girder was deformed or destroyed. The fitted |HSF(y)|ave -F linear expression could be used as the expression between the magnetic signal and the shear capacity. All the evaluation methods were expected to provide a basis for effectively evaluating the stress state of steel box girders with the MMMT method.

/pdf/magnetic-signal-characteristics-in-critical-yield-state-of-1g41e7l1.pdf

Magnetic Signal Characteristics in Critical Yield State of Steel Box Girder Based on Metal Magnetic Memory Inspection

Developments and Applications of Electromagnetic Tomography in Process Engineering

A highly efficient adaptive geomagnetic signal filtering approach using CEEMDAN and salp swarm algorithm

The magnetic tomography method (MTM) is a non-contact external inspection method for detecting metal magnetic memory signals. It has great potential for application in long-distance oil pipeline and subsea pipeline inspection. However, the spatial distribution characteristics and propagation laws of magnetic signals are not yet clear, which makes the MTM passive detection. In this study, a three-dimensional mathematical model of the magnetic field distribution of the stress concentration zone outside the pipe was established based on the boundary conditions. For the two cases in which the stress concentration zone was located at the top and bottom of the inner wall of the pipe, the model was solved by finite element analysis. The variation law of the magnetic signal outside the pipe was analyzed, and experiments were designed to verify the model. The results show that the shape of the magnetic memory signal remained unchanged after passing through the pipe wall. As the magnetic permeability of the pipe medium is much larger than that of air, the magnetic memory signal is significantly attenuated after penetrating the pipe wall. As the detection height increases, the magnetic induction outside the tube decays exponentially. The results also prove that the magnetic tomography method can detect the stress concentration zone at any position of the pipeline, and the detection accuracy is higher when it is located at the top of the pipeline.

/pdf/propagation-characteristics-of-magnetic-tomography-method-1jifmo86.pdf

Propagation Characteristics of Magnetic Tomography Method Detection Signals of Oil and Gas Pipelines Based on Boundary Conditions

Based on Automatic Identification System (AIS) data in maritime settings, this paper explores the limitations of traditional Recurrent Neural Networks in extracting features from complex vessel trajectory sequences. We propose a fusion model, namely CNN-RGRU-Attention, for vessel trajectory prediction. The model integrates Convolutional Neural Network (CNN), Attention Mechanism, and Gated Recurrent Unit (GRU). The effectiveness of the model is validated using actual AIS data, demonstrating significant improvements in metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The CNN-RGRU-Attention model provides crucial theoretical support for enhancing the safety management of maritime traffic services.

Research on Ship Trajectory Prediction Method Based on CNN-RGRU-Attention Fusion Model

Magnetic tomographic is a new and nondestructive testing technology based on the metal magnetic memory method, which can perform noncontact testing outside a pipeline. To identify the type of defect from the characteristics of the magnetic field signal detected by this method, we performed basic research on the propagation mechanism of a magnetic memory signal outside the pipe. In this paper, a magnetic field model of the stress concentration zone was first discussed based on the magnetic dipole theory. A 3-D formulation of the spatial propagation of the magnetic memory signals detected by the magnetic tomographic method was built, based on the Biot–Savart law and the magnetic vector potential. The reliability of the model was verified by comparing the field testing signal with the model-calculated values. The model can well describe the propagation law of the magnetic memory signal for the stress concentration zone generated on the inner wall of a pipeline.

Spatial Propagation Law of Magnetic Memory Signals Detected by Using Magnetic Tomography Method (***)

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Propagation Characteristics of Magnetic Tomography Method Detection Signals of Oil and Gas Pipelines Based on Boundary Conditions

Research on Ship Trajectory Prediction Method Based on CNN-RGRU-Attention Fusion Model

Spatial Propagation Law of Magnetic Memory Signals Detected by Using Magnetic Tomography Method (***)