Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Ship performance and navigation data compression and communication under autoencoder system architecture

Modern vessels are designed to collect,store and communicate large quantities of ship performance and navigation information through complex onboard data handling processes.That data should be transferred to shore based data centers for further analysis and storage.However,the associated transfer cost in large-scale data sets is a major challenge for the shipping industry,today.The same cost relates to the amount of data that are transferring through various communication networks(i.e.satellites and wireless networks),i.e.between vessels and shore based data centers.Hence,this study proposes to use an autoencoder system architecture(i.e.a deep learning approach)to compress ship performance and navigation parameters(i.e.reduce the number of parameters)and transfer through the respective communication networks as reduced data sets.The data compression is done under the linear version of an autoencoder that consists of principal component analysis(PCA),where the respective principal components(PCs)represent the structure of the data set.The compressed data set is expanded by the same data structure(i.e.an autoencoder system architecture)at the respective data center requiring further analyses and storage.A data set of ship performance and navigation parameters in a selected vessel is analyzed(i.e.data compression and expansion)through an autoencoder system architecture and the results are presented in this study.Furthermore,the respective input and output values of the autoencoder are also compared as statistical distributions and sample number series to evaluate its performance.

Case study on wave-current interaction and its effects on ship navigation

The East China Sea, where both the strong Kuroshio Current and powerful low pressures exist, is an inevitable ocean area for various ships sailing between Japan and other Asian and European countries. The safety and economics of such shipping behaviors are often affected by the strong dynamics of the environmental matrix. The wave conditions are usually significant under high ocean winds, leading to interaction between waves and currents. In this study, the third generation wave model SWAN are used to study the wave propagation and wave-current interaction, following by its effects on the ship navigation discussed. Significant interaction between the strong Kuroshio Current and high ocean waves as well as its effects on ship safety have been found by calculations of certain wave parameters, such as significant wave height（SWH）, average wave period（AWP）, mean wave direction（MWD）, wave length（WLEN）, frequency and directional spreading.

Remote Sensing Monitoring Method Based on BDS-Based Maritime Joint Positioning Model

Complicated sea conditions have a serious impact on ship navigation safety and even maritime accidents.Accordingly,this paper proposes a remote sensing monitoring method based on the Beidou Navigation Satellite System(BDS)maritime joint positioning model.This method is mainly based on the BDS and multiple Global Navigation Satellite Systems(GNSS)to build a data fusion model,which can capture more steady positioning,navigation,and timing(PNT)data.Compared with the current Global Positioning System(GPS)and Global Navigation Satellite System(GLONASS)mandatory used by the International Maritime Organization(IMO),this model has the characteristics of more accurate positioning data and stronger stability.The static and dynamic measurement show that such a model works for maritime ships and maritime engineering.Combined with the Ship’s Automatic Identification System(AIS)and Geographic Information System(GIS),a BDS-based remote sensing monitoring method can cover the world,serve maritime ships and construct maritime engineering.

Research on Weather Routing Based on the Mobile Internet

The combination and application of the mobile internet techniques with the weather radar monitoring data and the numerical weather pre-diction data were introduced, and the smart phone weather routing application software for both land and aquatic traffic safety, which is equipped with the function of analysis and warning of disastrous weather, was developed to reduce potential weather risks encountered during the journey as much as possible.

Ship behavior prediction via trajectory extraction-based clustering for maritime situation awareness

This study presents a method in which historical AIS data are used to predict the future trajectory of a se-lected vessel.This is facilitated via a system intelligence-based approach that can be subsequently utilized to provide enhanced situation awareness to navigators and future autonomous ships,aiding proactive col-lision avoidance.By evaluating the historical ship behavior in a given geographical region,the method applies machine learning techniques to extrapolate commonalities in relevant trajectory segments.These commonalities represent historical behavior modes that correspond to the possible future behavior of the selected vessel.Subsequently,the selected vessel is classified to a behavior mode,and a trajectory with respect to this mode is predicted.This is achieved via an initial clustering technique and subsequent tra-jectory extraction.The extracted trajectories are then compressed using the Karhunen-Loéve transform,and clustered using a Gaussian Mixture Model.The approach in this study differs from others in that tra-jectories are not clustered for an entire region,but rather for relevant trajectory segments.As such,the extracted trajectories provide a much better basis for clustering relevant historical ship behavior modes.A selected vessel is then classified to one of these modes using its observed behavior.Trajectory predic-tions are facilitated using an enhanced subset of data that likely correspond to the future behavior of the selected vessel.The method yields promising results,with high classification accuracy and low prediction error.However,vessels with abnormal behavior degrade the results in some situations,and have also been discussed in this study.