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

作     者：Lokukaluge P.Perera B.Mo 

作者机构：UiT The Arctic University of NorwayTromsoNorway SINTEF OceanTrondheimNorway 

出 版 物：《Journal of Ocean Engineering and Science》 (海洋工程与科学（英文）)

年 卷 期：2018年第3卷第2期

页      面：133-143页

核心收录：

学科分类：0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0809[工学-电子科学与技术（可授工学、理学学位）] 0908[农学-水产] 08[工学] 0707[理学-海洋科学] 0815[工学-水利工程] 0824[工学-船舶与海洋工程] 

基　　金：This work has been conducted under the project of“SFI Smart Maritime(237917/O30)-Norwegian Centre for im-proved energy-efficiency and reduced emissions from the mar-itime sector”that is partly funded by the Research Council of Norway An initial version of this paper is presented at the 35th International Conference on Ocean,Offshore and Arc-tic Engineering(OMAE 2016),Busan,Korea,June,2016,(OMAE2016-54093). 

主　　题：Autoencoder Ship performance and navigation information Ship energy efficiency Data compression Data communication Principal component analysis 

摘      要：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.