Maritime Computing Transportation, Environment, and Development: Trends of Data Visualization and Computational Methodologies

T. Chaichana
{"title":"Maritime Computing Transportation, Environment, and Development: Trends of Data Visualization and Computational Methodologies","authors":"T. Chaichana","doi":"10.46604/aiti.2023.10419","DOIUrl":null,"url":null,"abstract":"This research aims to characterize the field of maritime computing (MC) transportation, environment, and development. It is the first report to discover how MC domain configurations support management technologies. An aspect of this research is the creation of drivers of ocean-based businesses. Systematic search and meta-analysis are employed to classify and define the MC domain. MC developments were first identified in the 1990s, representing maritime development for designing sailboats, submarines, and ship hydrodynamics. The maritime environment is simulated to predict emission reductions, coastal waste particles, renewable energy, and engineer robots to observe the ocean ecosystem. Maritime transportation focuses on optimizing ship speed, maneuvering ships, and using liquefied natural gas and submarine pipelines. Data trends with machine learning can be obtained by collecting a big data of similar computational results for implementing artificial intelligence strategies. Research findings show that modeling is an essential skill set in the 21st century.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Technology Innovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46604/aiti.2023.10419","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

This research aims to characterize the field of maritime computing (MC) transportation, environment, and development. It is the first report to discover how MC domain configurations support management technologies. An aspect of this research is the creation of drivers of ocean-based businesses. Systematic search and meta-analysis are employed to classify and define the MC domain. MC developments were first identified in the 1990s, representing maritime development for designing sailboats, submarines, and ship hydrodynamics. The maritime environment is simulated to predict emission reductions, coastal waste particles, renewable energy, and engineer robots to observe the ocean ecosystem. Maritime transportation focuses on optimizing ship speed, maneuvering ships, and using liquefied natural gas and submarine pipelines. Data trends with machine learning can be obtained by collecting a big data of similar computational results for implementing artificial intelligence strategies. Research findings show that modeling is an essential skill set in the 21st century.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
海事计算运输、环境与发展:数据可视化和计算方法的发展趋势
本研究旨在描述海事计算(MC)运输、环境和发展领域。这是第一份发现MC域配置如何支持管理技术的报告。这项研究的一个方面是创建基于海洋的企业的驱动因素。采用系统搜索和荟萃分析对MC领域进行分类和定义。MC的发展最早出现在20世纪90年代,代表了设计帆船、潜艇和船舶流体力学的海事发展。模拟海洋环境以预测减排、沿海废物颗粒、可再生能源,并设计机器人来观察海洋生态系统。海上运输侧重于优化船舶速度、操纵船舶以及使用液化天然气和海底管道。机器学习的数据趋势可以通过收集类似计算结果的大数据来实现人工智能策略。研究结果表明,建模是21世纪的一项基本技能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Advances in Technology Innovation
Advances in Technology Innovation Energy-Energy Engineering and Power Technology
CiteScore
1.90
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊最新文献
Synthesis and Characterization of Phase Change Microcapsules Containing Nano-Graphite Challenges and Solutions to Criminal Liability for the Actions of Robots and AI Selection of Elevation Models for Flood Inundation Map Generation in Small Urban Stream: Case Study of Anyang Stream Efficient Object Detection and Intelligent Information Display Using YOLOv4-Tiny The Prediction of Low-Rise Building Construction Cost Estimation Using Extreme Learning Machine
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1