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

Journal of Ocean Engineering and Science 2018年第2期3卷 133-143页

作者： lokukaluge p.perera B.Mo UiT The Arctic University of Norway TromsoNorway SINTEF Ocean TrondheimNorway

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.***.satellites and wireless networks),i.***.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.***.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.***.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.***.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.

关键词： Autoencoder Ship performance and navigation information Ship energy efficiency Data compression Data communication principal component analysis

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Ship speed power performance under relative wind profiles in relation to sensor fault detection

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Journal of Ocean Engineering and Science 2018年第4期3卷 355-366页

作者： lokukaluge p.perera B.Mo UiT The Arctic University of Norway TromsoNorway SINTEF Ocean TrondheimNorway

Statistical data analysis and visualization approaches to identify ship speed power performance under relative wind(i.***.apparent wind)profiles are considered in this study.Ship performance and navigation data of a selected vessel are analyzed,where various data anomalies,i.***.sensor related erroneous data conditions,are identified.Those erroneous data conditions are investigated and several approaches to isolate such situations are also presented by considering appropriate data visualization methods.Then,the cleaned data are used to derive various relationships among ship performance and navigation parameters that have been visualized in this study,appropriately.The results show that the wind profiles along ship routes can be used to evaluate vessel performance and navigation conditions by assuming the respective sea states relate to their wind conditions.Hence,the results are useful to derive appropriate mathematical models that represent ship performance and navigation conditions.Such mathematical models can be used for weather routing type applications(i.***.voyage planning),where the respective weather forecast can be used to derive optimal ship routes to improve vessel performance and reduce fuel consumption.This study presents not only an overview of statistical data analysis of ship performance and navigation data but also the respective challenges in data anomalies(i.***.erroneous data intervals and sensor faults)due to onboard sensors and data handling systems.Furthermore,the respective solutions to such challenges in data quality have also been presented by considering data visualization approaches.

关键词： Speed power performance Data anomaly detection Sensor fault identification Weather routing Statistical data analysis Ship wind profile.

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

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Journal of Ocean Engineering and Science 2022年第1期7卷 1-13页

作者： Brian Murray lokukalugeprasad perera UiT The Arctic University of Norway TromsøNorway

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.

关键词： Maritime situation awareness Ship navigation Trajectory prediction Collision avoidance Machine learning Unsupervised learning AIS

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