Ports and Shipping Companies Have Outstanding Performances

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),

The Impact of Optimizing Trim on Reducing Fuel Consumption

The shipping industry is one of the biggest industries throughout the ages. Maritime transport plays a vital role in world economy; whereas competition between maritime companies is fierce [1], at the same time agreements of co-operation have taken different forms including alliances and mergers between companies to increase their market share. But competitions still stand despite all alliances even in same market. This intense competition drives companies to attain high level of competitiveness, by monitoring ship＇s operating performance and operating cost, emphasis on improving performance and reduce cost. On other hand new environmental regulations come to light, expansion of ECA （emission control areas）, which lead to significant higher fuel cost when using low sulfur fuel. Since the fuel cost is the largest portion of the operating cost of the vessel, a saving in fuel usage can result in considerable saving in operational costs. Furthermore, fuel saving has environmental benefits in the reduction of greenhouse gas emissions. The aim of this paper is to investigate the role of trim optimization which considers one of the easiest and cheapest methods for ship performance optimization and fuel consumption reduction trim optimization.

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.

SHIP HYDRODYNAMICS BASIC RESEARCH IN THE UNITED STATES

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.