Laivojen pohjien likaantumisen vaikutus kulkuvastukseen Itämerellä: Chow-Liu-puulla täydennetyn Naiivi Bayes -mallin soveltaminen aluksen kulun analysointiin

Q4 Engineering
Elias Altarriba
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引用次数: 0

Abstract

The objective of the COMPLETE-project is to prevent spreading of harmful and invasive alien species in Baltic Sea region. Significant proliferation has happened with ballast waters but organisms are also being transported to new areas among underwater hull structures. In addition of alien species spreading issues, biofouling increases vessel hydrodynamic resistance affecting straightforward to fuel consumption and carbon dioxide emissions. At present day, this bio-contamination is deducted by regular cleanings of immersed hull structures during summer seasons. However, selection of cleaning intervals is based on experience. Also, the effect of cleaning is often perceived by crew, but normally there are no measurement-based knowledge on its effect on voyage of unique vessel. Nowadays ship systems provides increasingly data flow that can be stored automatically. Conclusion-making from big data requires appropriate tools specially limiting effects of many system mixers. This article explores usability of Chow-Liu-tree augmented Naive Bayes method for analyzing voyage data. The advantages of this method are computational efficiency and ability to produce reliable conclusions about causation relationships prevailing in the studied system, even if available data is quite limited.
波罗的海海底污染对阻力的影响——Naivi-Bayes模型在船流分析中的应用
COMPLETE项目的目标是防止有害和入侵的外来物种在波罗的海地区传播。压载水已经发生了显著的扩散,但生物也被转移到水下船体结构中的新区域。除了外来物种传播问题外,生物淤积还会增加船只的水动力阻力,直接影响燃料消耗和二氧化碳排放。目前,这种生物污染是通过在夏季定期清洁浸入式船体结构来扣除的。然而,清洁间隔的选择是基于经验的。此外,清洁的效果通常由船员感知,但通常没有基于测量的知识来了解其对独特船只航行的影响。如今,船舶系统提供了越来越多的可以自动存储的数据流。从大数据中得出结论需要适当的工具,特别是限制许多系统混合器的效果。本文探讨了周-刘树增广朴素贝叶斯方法在航海数据分析中的可用性。这种方法的优点是计算效率和对所研究系统中普遍存在的因果关系得出可靠结论的能力,即使可用数据非常有限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Rakenteiden Mekaniikka
Rakenteiden Mekaniikka Engineering-Mechanical Engineering
CiteScore
0.50
自引率
0.00%
发文量
2
审稿时长
16 weeks
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