Optimization of stowage plans for RoRo ships

International vehicle transportation is primarily conducted using Roll-on/Roll-off (RoRo) ships, which are specialized to transport cargo on wheels such as cars, farming equipment, and military equipment. RoRo ships operate by going between different regions of the world according to predefined plans. In this work we focus on operational decisions that are required when operating a fleet of RoRo ships: given a ship set to travel according to a given route, we consider decisions such as which cargoes to carry, how many vehicles to carry from each cargo, and how to stow the vehicles carried during the voyage. A mathematical model is made describing the problem, and both a standard MIP solver and a specially designed heuristic method are used to solve the problem. Computational tests are conducted to analyze the difficulty of solving several variations of the problem. For certain types of instances the MIP solver works well, while for other types the heuristic is very fast and more accurate than the MIP solver.     

内河水路交通流参数视觉测量系统

内河水路交通流参数获取是当前发展内河水路交通智能的关键问题之一。提出一种基于视觉测量的水路交通流参数获取方法,采用视觉传感器获取航道全景图像,提取感兴趣对象（船舶）,建立视觉测量模型,获取多视角下船舶几何特征参数,建立CCD视觉传感器获取图像信息和基于知识库的广义传感器信息融合模型,计算和决策水路交通参数,如船舶吨位、船舶类型、船舶速度、航道占有率、通航密度、船舶安全状态评估等重要水路交通基础参数,为航运交通管理部门进行水路交通管理、规划和决策提供依据。实验结果表明,系统可实现对水路交通流参数的可靠获取,满足工程应用要求。     

An optimization-based decision support system for ship scheduling

The bulk carriers in the world merchant fleet typically operate full between a loading and discharging port, then run empty until they reach the next loading port. The shipping rates of bulk trades are set on supply/demand bases and fluctuate considerably. Thus the proper scheduling of ships in bulk trade has the great potential of improving the owner's profit and economic performance of shipping. This paper considers an optimization-based Decision Support System for ship scheduling. The typical optimization models for scheduling the ships are briefly reviewed and classified by the underlying idea. Then a prototype MoDiSS(Model-based SS in Ship Scheduling) which is based on a set-packing model has been developed on PC base with proper GUI. The performance of the system has been tested and examined using various ship scheduling scenarios and thereby the effectiveness of the system is validated satisfactorily.     

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

In recent years, computer vision finds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture. Automatic 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 technique. Furthermore, classified ships are again segmented with deep learning based auto-encoder model. The proposed model, in terms of classification, provides successful 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 accurate and can be implemented for automatic ship detection in water bodies considering remote sensing satellite images as input to the computer vision system.     

Dynamic rolling strategy for multi-vessel quay crane scheduling

This paper focuses on the container loading and unloading problem with dynamic ship arrival times. Using a determined berth plan, in combination with the reality of a container terminal production scheduling environment, this paper proposes a scheduling method for quay cranes that can be used for multiple vessels in a container terminal, based on a dynamic rolling-horizon strategy. The goal of this method is to minimize the operation time of all ships at port and obtain operation equilibrium of quay cranes by establishing a mathematical model and using a genetic algorithm to solve the model. Numerical simulations are applied to calculate the optimal loading and unloading order and the completion time of container tasks on a ship. By comparing this result with the traditional method of quay crane loading and unloading, the paper verifies that the quay crane scheduling method for multiple vessels based on a dynamic rolling-horizon strategy can provide a positive contribution to improve the efficiency of container terminal quay crane loading and unloading and reduce resource wastage.     

高分辨率遥感图像投影分析的靠岸舰船检测

目的 高分辨率遥感图像中,靠岸舰船检测有着广泛的应用前景,其主要难点在于舰船与港口陆地在空间上紧邻,在颜色和纹理特征上相似,舰船与港口陆地难以分割。针对这种情况,利用港口岸线平直的几何特点和靠岸舰船多为舷靠的停泊特点,提出一种基于投影分析的靠岸舰船检测方法。方法 首先,对原始图像进行预处理,利用K-means聚类算法与区域生长算法相结合的方式得到海陆分割图像,利用Sobel算子与Otsu分割结合的方式获取边缘图像;然后,通过改进的Hough变换提取直线特征,结合港岸几何特性定位港口岸线;再将海陆分割后的二值图像向沿岸线和垂直岸线两个方向进行投影,根据沿岸线方向投影形态确定和分离并靠舰船,根据垂直岸线方向的投影形态定位舰船目标;最后,利用舰船尺寸、长宽比、最小外接矩形占空比特征去除虚警。结果 在15个港口场景不同分辨率的遥感图像测试集上,本文方法整体检测率达到85.4%,虚警率达17.2%;限定分辨率范围在24 m的情形下,检测率提高到93.5%,虚警率降低至5.3%。结论 本文方法简单有效,无需港口先验信息,适用于多尺度和多方向的靠岸舰船目标检测任务,对不同类型舰船形态差异具有鲁棒性,且能够分离并靠舰船。     

A ship motion simulation system

In this article, efficient computational models for ship motions are presented. These models are used to simulate ship movements in real time. Compared with traditional approaches, our method possesses the ability to cope with different ship shapes, engines, and sea conditions without the loss of efficiency. Based on our models, we create a ship motion simulation system for both entertainment and educational applications. Our system assists users to learn the motions of a ship encountering waves, currents, and winds. Users can adjust engine powers, rudders, and other ship facilities via a graphical user interface to create their own ship models. They can also change the environment by altering wave frequencies, wave amplitudes, wave directions, currents, and winds. Therefore, numerous combinations of ships and the environment are generated and the learning becomes more amusing. In our system, a ship is treated as a rigid body floating on the sea surface. Its motions compose of 6 degrees of freedom: pitch, heave, roll, surge, sway, and yaw. These motions are divided into two categories. The first three movements are induced by sea waves, and the last three ones are caused by propellers, rudders, currents, and winds. Based on Newton’s laws and other basic physics motion models, we deduce algorithms to compute the magnitudes of the motions. Our methods can be carried out in real time and possess high fidelity. According to ship theory, the net effects of external forces on the ship hull depend on the ship shape. Therefore, the behaviors of the ship are influenced by its shape. To enhance our physics models, we classify ships into three basic types. They are flat ships, thin ships, and slender ships. Each type of ship is associated with some predefined parameters to specify their characteristics. Users can tune ship behaviors by varying the parameters even though they have only a little knowledge of ship theory.     

Multi-layer sparse coding model-based ship detection for optical remote-sensing images

Ship detection plays an important role in remote-sensing image processing. In this article, we propose a multi-layer sparse coding model-based ship detection (MSCMSD) method, integrating bottom-up and top-down mechanisms, for ship detection with high-resolution remote-sensing images. The multi-layer sparse coding model was designed to reveal the way how information is processed by human visual system. It is adopted in MSCMSD to detect candidate regions containing ships before any further processing. To detect ships from candidate regions, an omnidirectional solution is also proposed for deformable parts model-based ship detection. As demonstrated in the experiments, MSCMSD can detect ships from optical remote-sensing images with a higher accuracy than other state-of-the-art algorithms.     

结合深度特征迁移与融合的两阶段船牌定位算法

获取运河过往船只的身份信息具有重要意义,快速、准确地定位船牌是实现船只身份自动化识别的首要任务.为提升对小尺度船牌的检测性能,提出一种结合深度特征迁移与融合的两阶段船牌定位算法.首先在船只检测阶段,通过迁移学习构建船只检测模型,获取图片中船只区域的位置信息;然后在船牌定位阶段,提出基于特征融合策略的多尺度船牌定位网络,在上一阶段的基础上对船牌进行定位.在SLPLOC船牌定位数据集上的实验结果表明,相比其他算法,该算法能够有效地减少误差,提升精度值和召回率.     

A large neighbourhood search heuristic for ship routing and scheduling with split loads

The purpose of this paper is to present and solve a new, important planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies are committed to carrying some contract cargoes and will try to derive additional revenue from optional spot cargoes. In most of the literature on ship routing and scheduling problems a cargo cannot be transported by more than one ship. By introducing split loads this restriction is removed and each cargo can be transported by several ships. In this paper we propose a large neighbourhood search heuristic for the ship routing and scheduling problem with split loads. Computational results show that the heuristic provides good solutions to real-life instances within reasonable time. It is also shown that introducing split loads can yield significant improvements.     

一种船队编队控制的backstepping 方法

针对多船舶之间的协同合作问题,对船舶的编队控制进行了研究.通过运用领航者-跟随者方法,选择在Cartesian坐标系下建立新的船队编队控制模型,基于这种模型,利用反步技术和李亚普诺夫理论设计了一种可使船队按期望队形航行的船队编队控制器.通过考虑领队船舶与跟随船舶的航向角误差,保证了跟随船舶航向角的稳定性,从而避免其在航行过程中不断振荡.最后对所设计的控制方法的正确性及有效性进行了仿真验证.     

A hierarchical expert system for integrated scheduling of ship berthing, discharging and material transport

Abstract: Berthing ships at a port and unloading the stowed materials require a series of scheduling problems: ship berthing, discharging, and material transport. To deal effectively with the scheduling complexity due to mutual interdependence among those problems, this paper proposes a two-level hierarchical architecture for the integrated scheduling of all the activities arising in port. The hierarchical architecture assigns ships to multiple lower level dispatchers, each of which makes its own discharging and material transport schedule independently while satisfying the requirements imposed by the higher level coordinator. If any problem occurs within a dispatcher, the higher level coordinator attempts to resolve the trouble through the coordination of other dispatchers. Based on the hierarchical architecture, a prototype scheduling expert system is developed using G2 for the port scheduling problem at a steelworks. Through the object- oriented interaction of frames, the system is shown to effectively construct integrated schedules from the berth scheduling to the material transport scheduling.     

一种基于几何特征的舰船与码头目标分割的新方法

论文以可见光卫星图像中码头停靠的舰船目标为研究对象,提出一种新的码头与停靠舰船的分割方法。该方法根据码头区域的平直几何特征,利用改进的Hough变换提取码头轮廓,通过二次分割将停靠舰船与码头分离,从而解决了由于停靠舰船与码头区域连接,无法根据灰度特性将舰船目标与码头区域完整地分割的困难,达到准确识别舰船的目的。     

Development of panel generation system for seakeeping analysis

In this paper, a panel generation system for analyzing seakeeping performance of a ship is developed. Given a set of offset data representing the ship hull, the system first creates a surface model of the ship. From the surface model, the wetted part of the ship is obtained by computing the intersection between the water surface and the hull, which is then processed to generate quadrilateral panels for the hull and the water surface. The system is designed to handle various kinds of ships such as ships with a mono-skeg, a twin-skeg and/or a bulbous bow in either an automatic or an interactive manner. Moreover, it can generate input panels for three different seakeeping analysis methods. Examples are provided to demonstrate the capabilities of the system.     

Ship target tracking based on a low-resolution optical satellite in geostationary orbit

Based on the low-resolution geostationary optical satellite for maritime surveillance, a complete processing algorithm including ship detection and ship tracking is proposed. Potentially ships are first detected by a constant false alarm rate processing and a morphological filter from the area of water. Image coordinates of ships are then corrected and transformed by using rational polynomial coefficients and high-resolution shoreline database. In the ship-tracking procedure, a convenient motion model based on geographical coordinates is established, and the multiple hypothesis tracking method is used to track ships for further removing false targets and getting motion states of targets ultimately. Our algorithm has been tested using GaoFen-4 (GF-4) satellite image sequences and the automatic identification system messages of ships. The results of the experiment show that the proposed algorithm can effectively detect and track ships in complex scenes and estimate ships’ motion information accurately with the help of a low-resolution geostationary optical satellite.     

基于全卷积神经网络的船舶检测和船牌识别系统

船舶检测与识别对于港口智能监控,实现港口资源的有效管理具有重要意义。由于复杂的船舶轮廓、船牌位置不固定、船牌文本类型复杂多样和船牌文字个数不确定等因素,使得船舶的检测和识别非常具有挑战性。本文提出一种基于全卷积神经网络的船舶检测与识别方法：SDR-FCN。SDR-FCN利用本文提出的船舶检测算法SDNet进行船舶检测定位,然后利用本文提出的船牌文本检测算法PDNet进行船牌文字检测,最后利用具备在线自适应性的分类器OA-Classifier进行船牌分类识别。OA-Classifier综合了AIS（船舶自动识别系统）反馈的信息,提高了分类器的识别精度。实际SDR-FCN部署运行表明,它能够以较高的精度可靠地工作,满足实际应用。     

基于单景高分四号卫星多光谱影像的舰船运动特征检测

高分辨率地球同步轨道（GEO）遥感卫星技术能够在短时间内对大范围海域进行高频重复监测，有望近实时地获取海上舰船运动特征。以广东省珠江伶仃洋的南部海域为研究区，基于单景高分四号卫星多光谱影像，使用梯度阈值方法对海上运动舰船进行检测，结果表明：①利用单波段高分四号多光谱影像能够检测出船位与航向，船位平均误差为80.46 m（相当于1.61个图像分辨单元），航向检出率为74.36%，航向绝对误差为8.65°。②利用单景高分四号影像能够检测出航向与船速，航向精度为98.96%，平均绝对误差为8.78°，船速精度为83.0%，平均绝对误差为1.41 m/s。研究表明高分四号卫星能够用于海上舰船近实时遥感监测，在海上舰船运动特征检测方面具有独特优势和巨大的应用潜力。     

动态船舶行驶场景下的实时单目测距算法研究

为了利用单目视觉实时监测船舶行驶过程中与周围船舶之间的距离,首先分析单目视觉测距现状及其成功应用实例,基于小孔成像原理建立单目相机模型,通过几何推导,得到世界坐标系、相机坐标系、图像坐标系及像素坐标系之间三层坐标转换关系.随后通过实验验证pitch俯仰角与yaw水平角对单目视觉测距的影响程度,从而分析出动态船舶行驶场景...     

基于单景高分四号卫星多光谱影像的舰船运动特征检测

高分辨率地球同步轨道（GEO）遥感卫星技术能够在短时间内对大范围海域进行高频重复监测，有望近实时地获取海上舰船运动特征。以广东省珠江伶仃洋的南部海域为研究区，基于单景高分四号卫星多光谱影像，使用梯度阈值方法对海上运动舰船进行检测，结果表明：①利用单波段高分四号多光谱影像能够检测出船位与航向，船位平均误差为80.46 m（相当于1.61个图像分辨单元），航向检出率为74.36%，航向绝对误差为8.65°。②利用单景高分四号影像能够检测出航向与船速，航向精度为98.96%，平均绝对误差为8.78°，船速精度为83.0%，平均绝对误差为1.41 m/s。研究表明高分四号卫星能够用于海上舰船近实时遥感监测，在海上舰船运动特征检测方面具有独特优势和巨大的应用潜力。     

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN-based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data – ‘Noon Data’ – which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.