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 bas...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.展开更多
Access Report on Performances of Listed Ports and Shipping Com- panies in 2007,by International Economy Research Institute of Dalian Maritime University(DMU),was issued. According to the report,Shenzhen Chi- wan Wharf...Access Report on Performances of Listed Ports and Shipping Com- panies in 2007,by International Economy Research Institute of Dalian Maritime University(DMU),was issued. According to the report,Shenzhen Chi- wan Wharf Holdings Limited(CWH),展开更多
An overview of basic research on ship hydrodynamics in the United States ispresented. The focus is on leading edge research of high scientific interest bUt with ship hydrodynamics applicability. The research topic are...An overview of basic research on ship hydrodynamics in the United States ispresented. The focus is on leading edge research of high scientific interest bUt with ship hydrodynamics applicability. The research topic areas are briefly discussed, representative highlights from recent research are presented, and scientific hurdles to improved ship hydrodynamics technology are delineated. The purpose of the paper is to stimulate international cooperation in solving the basic research problems of common interest to all hydrodynamicists.展开更多
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 NorwayAn 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).
文摘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.
文摘Access Report on Performances of Listed Ports and Shipping Com- panies in 2007,by International Economy Research Institute of Dalian Maritime University(DMU),was issued. According to the report,Shenzhen Chi- wan Wharf Holdings Limited(CWH),
文摘An overview of basic research on ship hydrodynamics in the United States ispresented. The focus is on leading edge research of high scientific interest bUt with ship hydrodynamics applicability. The research topic areas are briefly discussed, representative highlights from recent research are presented, and scientific hurdles to improved ship hydrodynamics technology are delineated. The purpose of the paper is to stimulate international cooperation in solving the basic research problems of common interest to all hydrodynamicists.
