In this study, we propose a real-time ship anomaly detection method driven by Automatic Identification System (AIS) data. The method uses ship trajectory clustering classes as a normal model and a deep learning algorithm as an anomaly detection tool. The method is divided into three main steps: (1) quality maintenance of the original AIS data, (2) extraction of normal ship trajectory clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), in which a segmented improved Dynamic Time Warping (DTW) algorithm is used to measure the degree of trajectory similarity, (3) the clustering results are used as a normative model to train a Bi-directional Gated Recurrent Unit (BiGRU) recurrent neural network, which is used as a trajectory predictor to achieve real-time ship anomaly detection. Experiments were conducted using real AIS data from the port of Tianjin, China. The experimental results are manifold. Firstly, the data pre-processing process effectively improves the quality of raw AIS data. Secondly, the ship trajectory clustering model can accurately classify the traffic flow of different modes in the sea area. Moreover, the trajectory prediction result of the BiGRU model has the smallest error with the actual ship trajectory and has a better trajectory prediction performance compared with the Long Short-Term Memory Network model (LSTM) and Gated Recurrent Unit (GRU). In the final anomaly detection experiment, the detection accuracy and timeliness of the BiGRU model are also higher than LSTM and GRU. Therefore, the proposed method can achieve effective and timely detection of ship anomalous behaviors in terms of position, heading and speed during ship navigation, which provides insight to enhance the intelligence of marine traffic supervision and improve the safety of marine navigation.

In order to improve the accuracy of the freefall of lifeboat motion simulation in a ship life-saving simulation training system, a mathematical model using the strip theory and Kane’s method is established for the freefall of the lifeboat into the water from a ship. With the boat moving on a skid, the model of the ship’s maneuvering mathematical group (MMG) is used to model the motion of the ship in the waves. Based on the formula of elasticity and friction theory, the forces of the skid acting on the boat are calculated. When the boat enters the water, according to the analytical solution theory of slamming, the slamming force of water entry is solved. The simulation experiments are carried out by the established model. The results of the numerical simulation are compared with the calculation results of the hydrodynamics software Star CCM+ at water entry under initial condition A in the paper. The position and velocity of the center of gravity of the boat, the angle, and velocity and acceleration of pitch calculated by the two methods are in good agreement. There is a little difference between the values of translation acceleration calculated by the two methods, which is acceptable. This shows that our numerical algorithm has good accuracy. A qualitative analysis is performed to find the safe point of water entry under the condition of different wave heights and two situations of a ship encountering waves. Finally, the model is applied to the ship life-saving training system. The model can meet the system requirements and improve the accuracy of the simulation.

This study addresses the integrated scheduling problem of dry bulk cargo terminal yards, which includes three components: transportation planning, yard selection optimization, and equipment scheduling. Additionally, the research integrates safety considerations and addresses the complexities of dynamic transportation planning. This work presents two innovations. Firstly, this study develops a sophisticated modeling framework that integrates graph structures for precise yard mapping with mixed-integer programming to enforce operational constraints. This integrated approach facilitates a more accurate and comprehensive representation of yard operations, capturing diverse operational aspects while maintaining model clarity and computational efficiency. Secondly, this study proposes an advanced solution methodology that employs a reinforcement learning technique integrating a Dueling Deep Q-Network and Double Deep Q-Network. This hybrid algorithm significantly enhances optimization performance and accelerates the learning process, thereby improving the efficiency of the solutions. The experimental results demonstrate that the proposed model effectively manages the integrated scheduling of bulk material ingress, storage, and egress within the yard. The operational plans generated by the approach outperform traditional first-come, first-served strategies, showcasing substantial improvements in port operational efficiency and reliability. This comprehensive solution underscores the potential for significant advancements in the overall management and performance of dry bulk cargo ports.

The dry bulk shipping network is an important carrier of global bulk commodity flow. To better understand the structural characteristics and future development trends of the global dry bulk shipping network (GDBSN), this study proposes a framework for characteristics analysis and link prediction based on complex network theory. The study integrates large-scale heterogeneous data, including automatic identification system data and port geographic information, to construct the GDBSN. The findings reveal that the network exhibits small-world properties, with the Port of Singapore identified as the most influential node. Link prediction results indicate that many potential new shipping routes exist within regions or between neighboring countries, exhibiting clear regional clustering characteristics. The added links mainly influence the local structure, with minimal impact on the overall network topology. This study provides valuable insights for shipping companies in route planning and for port authorities in developing strategic plans.

For the design of ship equipment and crew training, it would be useful to develop software for the three-dimensional simulation of a totally enclosed lifeboat. To improve the simulation accuracy and immersion of the software, we present a multibody dynamics model for a lifeboat lowered from a ship, accounting for the coupled motion among the ship, lifeboat slings, a cable-pulley system, and the lifeboat. The equations of the whole system are formulated using Kane's method. The model of ship manoeuvring mathematical group is used to calculate the forces and moments acting on the hull of the ship. The hydrodynamic and wind loads on the boat are modeled using the strip theory. The impact force between the ship and the boat colliding is estimated using the contact theory of Hertz and an elastoplastic model. The method of lumped mass is used to model the lifeboat slings. For the cable-pulley system, we present an efficient model for the dynamics of the pay-out/reel-in process based on the framework of Kane's method. The local load of each cable segment between two pulleys is calculated by the model of a linear spring on the basis of the amount of cable passages over pulleys and the variation of the pulley positions, conversely, the cable segment exerts force and moment on the pulleys. The motion equations of the whole system are solved using fourth-order Runge-Kutta. The model can simulate the lowering of the lifeboat, and obtain the three-dimensional motion parameters of the ship, the lifeboat, slings and pulleys, and the local tension load of the cable. The results show that the simulation curves are near the ones of the model experiment, and their trends are coincident. Thus, it can be concluded that our model is feasible. According to our model, the motion of the ship has a significant effect on the magnitude of the lifeboat's oscillation when the sea state is above level 4; it is safe when the initial clear distance is greater than 1.5 times the width of the boat and the sea conditions are below level 5. Finally, the model is applied to the software for three-dimensional simulation.