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- W4285343910 abstract "Nowadays, energy saving and emission reduction have aroused widespread concern in the maritime industry. Energy efficiency optimization is one of the effective ways to save energy and reduce emissions. Ship energy efficiency is synthetically influenced by multiple factors including environmental and operational profile. The randomness of these factors results in very complex interrelation among the environment and ship engine speed as well as ship energy efficiency. It is difficult for the semi-empirical or theoretical modeling methods to describe such interrelation accurately. The rapid development of big data analysis makes it possible to obtain the association rules of various environmental factors and ship energy efficiency as well as engine speed. In this paper, big data analyses in terms of Parallel Distributed K-Means Clustering (PDKC) algorithm and Parallel Frequent Pattern Mining (PFPM) algorithm are adopted to achieve the clustering analysis of ship energy efficiency related data, and to obtain the interrelation among ship energy efficiency and environmental factors as well as engine speed. On this basis, the knowledge base of ship energy efficiency accounting for various environmental factors is established. The constructed knowledge base can be used for exploring optimization methods of ship energy efficiency, promoting decision-making system development and contributing to energy saving and emission reduction of ships." @default.
- W4285343910 created "2022-07-14" @default.
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- W4285343910 date "2021-10-22" @default.
- W4285343910 modified "2023-09-28" @default.
- W4285343910 title "Knowledge Base Construction Of Ship Energy Efficiency Accounting For Various Environmental Factors Based On Big Data Analysis" @default.
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- W4285343910 doi "https://doi.org/10.1109/ictis54573.2021.9798619" @default.
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