Ship trajectory prediction is among the key technologies used for maritime traffic management to avoid collisions and guarantee navigation safety. However, the complicated coupling effect resulting from ship maneuvering behaviors during multi-ship encounters is usually ignored and difficult to quantify reasonably. Therefore, we propose a novel ship trajectory prediction framework based on generative adversarial networks with a multilayer perceptron (MLP) and a multi-head probsparse self-attention mechanism (GAN-MM) to predict ship trajectories under the coupled effect of multiple ships. The method uses the distance to closest point of approach (DCPA) and the time to closest point of approach (TCPA) to determine the neighboring ships affecting the target ship. On this basis, the MLP is adopted to quantify the multi-ship coupling effect caused by multi-ship encounters to extract the potential navigation feature vector of the target ship. Then, bidirectional long short-term neural network (Bi-LSTM) is employed as the encoder and decoder in the generator to capture the bidirectional time series features of the trajectories. Moreover, a multi-head probsparse self-attention mechanism is embedded between the encoder and decoder to efficiently mine correlations among hidden representations. Finally, a discriminator based on Bi-LSTM and an MLP is adopted to determine the authenticity of the predicted trajectory to evaluate the performance and accuracy of the proposed framework. To verify the effectiveness of the proposed framework, numerical experiments are conducted. The experimental results show that the proposed GAN-MM is able to quantify the coupling effect caused by ship maneuvering behaviors under multi-ship encounters and greatly improve prediction accuracy. The comparison experiments also indicate that the GAN-MM is obviously superior to the other comparison methods.

Identifying marine traffic behaviour patterns is important for intelligent maritime traffic management systems. The introduction of Automatic Identification System (AIS), which can record speed over ground (SOG) and course over ground (COG) data, makes these patterns accessible. Based on that, a novel adaptive marine traffic behaviour pattern recognition algorithm is proposed to explore potential traffic behaviour patterns. In the proposed recognition algorithm, multidimensional dynamic time warping is introduced to measure the similarity among trajectories; this process considers both the spatial and motion characteristics of trajectories. However, the computational complexity of multidimensional dynamic time warping is high. To reduce the number of required computations, a data simplification algorithm considering ship behaviours is adopted to compress trajectories and, therefore, to achieve an improved computation ability. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to classify marine traffic behaviour patterns. Due to the poor adaptation of the DBSCAN algorithm, we design a local mean density to evaluate the trajectory density distribution in the given dataset and then determine the optimal parameters in DBSCAN according to the local mean density gradient to improve the adaptivity of the algorithm. The proposed approach is illustrated by numerical experiments involving U.S. east coastal waters. The experimental results indicate that our approach is more accurate and adaptive in terms of recognizing hidden traffic behaviour patterns than other methods.

This study jointly optimizes ocean shipping routes and sailing speed by considering the influence of sea states, such as wind and wave states, on sailing speed. A joint optimization model for ocean shipping routes and sailing speed is established for ocean-going vessels to determine the optimal ocean shipping route and corresponding optimal sailing speed while minimizing fuel consumption, and the involuntary and voluntary speed losses caused by ocean environments and time window constraints are considered. To implement the proposed joint optimization model, a heuristic method based on a difference algorithm is designed to obtain an optimal solution. The proposed model and designed algorithm are applied to a real case study. The results show that the optimization model can efficiently reduce fuel consumption. The maximum and minimum fuel consumption reduction ratios are 5.04% and 0.21%, respectively, and the optimal ocean shipping routes and corresponding optimal sailing speeds that minimize fuel consumption can be determined. Furthermore, comparative experiments are conducted. The results indicate that the fuel consumption savings ratio achieved by the proposed algorithm is considerably higher than the ratios obtained by other algorithms.

Automatic identification systems (AIS) are on-board compulsory devices on ships that can provide a large amount of dynamic vessel information, such as position, course and speed information, which is meaningful for marine management. This paper explores the potential to utilize AIS data to recognize ship manoeuvring hot zones and analyse their characteristics to improve marine management efficiency. First, considering manoeuvring behaviours such as turning, acceleration and deceleration, a novel approach based on MiniBatchKMeans that recognizes ship turning hot zones, acceleration hot zones and deceleration hot zones is proposed according to course and speed variation. Second, joint probability density functions for speed over ground (SOG) and course over ground (COG) are built taking into account the combined influences of motion characteristics, which are beneficial for determining the ship traffic rules in manoeuvring hot zones and utilized in marine management. Finally, numerical and comparison experiments are conducted to test the feasibility and effectiveness of the proposed method.

Compared with the traditional scalar tracking structure, the vector tracking structure of the satellite navigation receiver has a better signal tracking sensitivity in high dynamic and other situations. However, due to the limited accuracy, it is really hard for a vector tracking loop to maintain the lock of carrier phase. In order to solve this problem, a carrier phase tracking method for vector tracking loops is proposed. First, three common vector tracking structures are analyzed. Second, in the structure of double vector loops, phase compensation is estimated and used to improve the tracking accuracy of the carrier phase. Finally, simulations have been carried out to analyze and verify the feasibility of the proposed scheme. This method estimates and reduces the phase tracking error and has a better tracking effect than the traditional scalar structure. It provides a reference for the implementation of the vector tracking structure and the ultra-tight integrated navigation system.

This paper designs a maritime anomaly detection algorithm based on a support vector machine (SVM) that considers the spatiotemporal and motion features of trajectories. Since trajectories are two-dimensional, it is difficult to present their motion features. To accurately describe trajectory features, a novel trajectory feature extraction method based on statistical theory is proposed in this paper. This method maps trajectories onto a high-dimensional space, which can account for both the spatiotemporal features and motion features of the trajectories. With the proposed feature extraction method, the density-based spatial clustering of applications with noise algorithm is employed to recognize vessel traffic patterns by simultaneously considering the spatiotemporal and motion features. Then, an improved SVM is designed by employing a weighted hybrid kernel function and differential operator to detect anomalous behaviours from recognized vessel traffic patterns that include the spatiotemporal and motion characteristics. Compared with standard SVM, it can adaptively determine the optimal kernel function according to sample set. Finally, a numerical example based on automatic identification system data from the waters off Chengshan Jiao is fulfilled to verify the proposed algorithm effectiveness and accuracy.

为提高复杂水域船舶自动生成路径的安全性与经济性,将海洋气象环境因素考虑在内,以船舶避开障碍物为前提,设计了以航行时间最短为目标的路径规划算法.在建立环境模型的基础上采用改进MAKLINK图生成可行路径,根据矢量合成及拟合模型分析海流及风浪对船舶航速的影响,从而确定路径权值,通过Dijkstra算法进行初始路径规划,采用改进粒子群算法进一步优化及平滑初始路径.以一艘集装箱船通过规划海域为例验证算法的有效性,并对风向和风级进行了敏感性分析.结果表明:考虑海洋气象环境影响生成的路径既可安全避开障碍物,又可节省航行时间,改进粒子群算法在缩短路径航行时间的同时可提高路径的平滑性.

This paper proposes a new sailing speed optimization problem considering sea conditions such as wind and waves for a container shipping company. Since wind and waves, as exterior factors, can reduce speed, more power is employed as compensation to maintain regular service frequency and avoid delay, resulting in additional bunker consumption and higher cost. Hence, it is necessary to optimize the sailing speed according to the wind and waves. A power model was built to determine the reduced speed caused by the wind and waves for quantifying the speed reduction. On the basis of the proposed model, we developed a mixed-integer nonlinear programming model for a sailing speed optimization problem considering the wind and waves. In view of the complexity of the function for determining the speed reduction, a discrete method is proposed to transform the proposed mixed-integer nonlinear programming model into a mixedinteger linear programming model. Finally, a numerical experiment was conducted to verify and validate the applicability.

Dempster–Shafer (D-S) evidence theory plays an important role in multisource data fusion. Due to the nature of the Dempster combination rule, there can be counterintuitive results when fusing highly conflicting evidence data. To date, conflict management in D-S evidence theory is still an open issue. Inspired by evidence modification considering internal indeterminacy and external support, a novel method for conflict data fusion is proposed based on an improved belief divergence, evidence distance, and belief entropy. First, an improved belief divergence measure is defined to characterize the discrepancy and conflict between bodies of evidence (BOEs). Second, evidence credibility is generated to describe the external support based on the complementary advantages of the improved belief divergence and evidence distance. Third, belief entropy is utilized to quantify the internal indeterminacy and further determine evidence weight. Lastly, the classical Dempster combination rule is applied to fuse the BOEs modified by their credibility degrees and weights. As the results of numerical examples and an application show, the proposed divergence measure can overcome the invalidity of the existing measures in some special cases. Additionally, the proposed fusion method recognizes the correct target with the highest belief value of 98.96%, which outperforms other related methods in conflict management. The proposed fusion method also displays better convergence, validity, and robustness.

With the establishment of satellite constellations and terrestrial networks of Automatic Identification System (AIS) receivers, an increasing number of ship trajectories have become available, and the data size of trajectories that must be recorded is increasing. As a result, transmitting, processing and storing data have become important issues. At the same time, ship behaviour information is hidden in AIS data. Hence, an effective method is required to not only compress redundant information but also maintain the main characteristic elements included in the trajectory. In this paper, a novel algorithm considering the spatial and motion features of trajectories is designed, which can compress AIS trajectories based on ship behaviour characteristics. The proposed algorithm has two main parts: the Douglas-Peucker (DP) algorithm is employed to simplify trajectories according to spatial features, and a sliding window is adopted to simplify trajectories based on motion features. Furthermore, statistical theory is applied to help determine the thresholds of motion features in sliding window algorithms. The two results are merged to form a trajectory simplification algorithm that considers ship behaviours. To verify the effectiveness of the proposed algorithm, numerical experiments are performed. The results indicate that the proposed algorithm can efficiently simplify trajectories by considering ship behaviour as needed.

Clustering analysis is commonly used for vessel traffic behaviour recognition. The clustering results reflect the characteristics of different vessel traffic behaviours, which can assist authorities in transportation management. However, there are two drawbacks to traditional clustering analysis. First, the similarity measures among different trajectories in a clustering analysis mainly focus on spatial differentiation and frequently do not consider motion features, such as speed and course. Second, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a famous clustering analysis method, is characterized by poor self-adaption, which means that it cannot autonomously determine the best parameters based on different sample sets. These issues limit the efficiency of clustering analysis. To address these problems, a new similarity measure using statistical methods and based on multi-attribute trajectory characteristics is proposed to reflect the similarity among different trajectories. The proposed measure considers the spatial and motion features of trajectories. Furthermore, the Parzen window (a non-parametric estimation method) and the multivariate Gaussian distribution are employed to generate self-adaptive strategies to determine the optimal parameters of the DBSCAN algorithm for a given sample set. Finally, numerical experiments are conducted to verify the effectiveness of the proposed similarity measure and improved DBSCAN algorithm. The clustering analysis results obtained with the proposed method could provide insights towards better monitoring and navigation advice to help improve marine managerial effectiveness and avoid maritime accidents.

The meteorological environment plays an important role when optimizing ship routing. Therefore, with the goal of obtaining meteorological environmental data associated with ship routing, an algorithm that can accurately obtain environmental data around a rhumb route in a standardized common data form is proposed. A Mercator projection is a type of equal-angle projection, and a rhumb route is a straight line on a Mercator chart. Consequently, the linear equation for a rhumb route can be achieved using the point-slope form. Based on this linear equation, points with a regular spacing whose degree is determined by the longitudinal size of the grid can be obtained. Then, the points from the Mercator chart are projected to geodetic coordinates. The extracted grids are traversed by the regularly spaced adjacent points with identical degrees of longitude on the rhumb route; therefore, the meteorological environmental data around the rhumb route can be obtained. Finally, the algorithm is executed and verified using MATLAB. The results of the experiments show that the algorithm proposed in this paper can extract wind grid data and effectively address the extraction of other meteorological environmental data. Thus, the algorithm is useful in the selection of ship routes based on a consideration of meteorological factors.

To solve unconstrained optimization problems, a random search differential evolution algorithm (RPMDE) is designed based on a modified differential evolution algorithm. The efficiency of an evolutionary algorithm usually depends on its exploration competence and development capability. Considering these characteristics, an effective difference operator called DE/M_pBest-best/1' is designed, which originates from DE/best/1/' and DE/current-pbest/1'. The operator makes use of information from the best population of individuals to generate new solutions for the development of RPMDE and guarantee swarm quality during the later evolution of the algorithm, which improves its searching ability. To prevent the solutions from falling into local optima, RPMDE also adopts random perturbation to update the current solution with a better solution after difference mutation and crossover are competed. Furthermore, a levy distribution is employed to adjust the scale factor as a control parameter. All designed operators are beneficial to improve the exploration competence and the diversity of the whole population. Last, a large number of computational experiments and comparisons are conducted by employing 15 benchmark functions. The experimental results indicate that the designed algorithm, RPMDE, is more effective than other differential evolution variants in dealing with unconstrained optimization problems.

为提高海上监控系统效率,有效预测船舶航行行为,建立了基于极限学习机的船舶航行行为预测模型.该模型针对航行状态的改变(主要为转向或变速),采取自动调整采样周期的方法更精准的训练网络,从而提高对船舶行为的预测精度.最后,利用琼州海峡的船舶自动识别系统(Automatic Identification System,AIS)信息将设计的预测模型与现有的灰色关联和BP模型进行对比.仿真结果表明:设计的算法有效地降低了船舶在转向及变速前后的预测误差;通过曼-惠特尼U检验证明,设计的基于极限学习机的船舶航行行为预测模型相比于传统BP神经网络及灰色关联模型,在预测精度方面具有更大的优势.

针对起重船作业时需根据作业情况调节压载水量以满足起吊大件回转作业要求,本文提出了一种起重船压载水调节数学模型,实现了起重船作业过程中吊臂匀速回转及压载水调节量最小的目的.利用Lingo软件对算例进行计算,结果满足全回转起重船作业时压载水调节量均衡且最小,浮态满足规范要求.研究结果表明:全回转起重船回转作业时压载水调节总量最小且吊臂匀速回转的优化计算合理可行,可有效避免经验法可能导致的危险情况.

自航式半潜维修船是基于集成创新理念设计的集高技术与多功能于一体的新船型.为选择最优的半潜维修船船型方案,以载重量与主尺度比值RRDW、甲板作业面积SWDS和每日作业收益P这3个指标为目标函数,建立半潜维修船船型论证模型,并采用理想点法和多目标遗传算法求解该模型.以一艘5万吨半潜维修船对故障船舶开展维修作业为例进行算例检验.两种方法均能实现对模型的求解,理想点法能提供距理想点更近的船型方案,而多目标遗传算法能提供更丰富的船型方案.计算结果验证了该模型的有效性,表明该模型能够为半潜维修船的论证选型提供决策支持.

This paper proposes an intuitionistic fuzzy decision method based on prospect theory and the evidential reasoning approach, aiming at analyzing multi-attribute decision making problems in which the criteria values are intuitionistic fuzzy numbers and the information of attributes weights is unknown. Firstly, the measures of entropy and cross entropy are defined for intuitionistic fuzzy sets by taking into consideration the preference of decision maker towards hesitancy degree. Secondly, combined with bounded rationality, the prospect decision matrix is calculated in the light of prospect theory and intuitionistic fuzzy distance. Thirdly, the correlational analyses are conducted between the attribute weights and three indicators which are entropy, cross entropy and prospect value, and optimization models for identifying attribute weights are built under the circumstances that the weights are incomplete and unknown. Finally, in order to avoid the loss of decision making information, the evidential reasoning approach is applied to the calculation of comprehensive prospective values for all alternatives. Following the value calculation, the ranking and the optimal alternative are determined based on the comprehensive prospective values. Illustrating examples demonstrate that the proposed method is reasonable and feasible. (C) 2017 Elsevier Ltd. All rights reserved.

To optimize the speed of container ships so as to reduce fuel consumption,considering the disturbance of wind and wave,a model for determining reduced speed is designed according to the power relationship of underway ships.The wind and wave disturbance forces applied to the hull and the deceleration range of ships under different conditions of wind and wave can be obtained by the model.The model is compared with the existing fitting model,which shows that the resultant force of wind and wave from the forward of the beam and abaft the beam hinders the navigation of ships.The speed loss is larger when the wind and wave are from the forward of the beam;the speed loss is lower when the wind and wave are abaft the beam;in the same wind and wave condition,the speed loss becomes lower with the speed being up.The proposed model is more applicable than the fitting model.By the model,the reduced speed can be estimated accurately.