Telediagnosis and maintenance of the ship from land, using principal component analysis to compress acquired data offshore

I. Sotés, J. L. Larrabe, Miguel A. Gomez, F. J. Alvarez, M. C. Rey-Santano, V. Mielgo, E. Gastiasoro
{"title":"Telediagnosis and maintenance of the ship from land, using principal component analysis to compress acquired data offshore","authors":"I. Sotés, J. L. Larrabe, Miguel A. Gomez, F. J. Alvarez, M. C. Rey-Santano, V. Mielgo, E. Gastiasoro","doi":"10.1109/ISKE.2010.5680826","DOIUrl":null,"url":null,"abstract":"At sea, voice and data communications have virtually global coverage via satellite. Satellite communications are increasing the bandwidth and lowering the cost, but are still far from levels that are in earth. The Principal components analysis (PCA) is a statistical technique used for lossy or non exact compression data; is a common tool for the search of pattern of multidimensional data sets. The assertion of this study was the possibility of using the PCA theory to compress, with sufficient accuracy, the large amount of data that are collected on board, and then send them all via satellite, in a cheaper or/and faster way than traditionally. This strategy is appropriate for decision making about telediagnosis and maintenance of the ship using ground equipment. Consequently, significant savings are achieved in telecommunication costs and telecommunication times.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"1 1","pages":"470-473"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISKE.2010.5680826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

At sea, voice and data communications have virtually global coverage via satellite. Satellite communications are increasing the bandwidth and lowering the cost, but are still far from levels that are in earth. The Principal components analysis (PCA) is a statistical technique used for lossy or non exact compression data; is a common tool for the search of pattern of multidimensional data sets. The assertion of this study was the possibility of using the PCA theory to compress, with sufficient accuracy, the large amount of data that are collected on board, and then send them all via satellite, in a cheaper or/and faster way than traditionally. This strategy is appropriate for decision making about telediagnosis and maintenance of the ship using ground equipment. Consequently, significant savings are achieved in telecommunication costs and telecommunication times.
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
利用主成分分析方法对海上采集的数据进行压缩,实现船舶远程诊断和维护
在海上,语音和数据通信通过卫星几乎覆盖全球。卫星通信正在增加带宽和降低成本,但与地球上的水平仍有很大差距。主成分分析(PCA)是一种用于有损或非精确压缩数据的统计技术;是多维数据集模式搜索的常用工具。本研究的断言是使用PCA理论压缩的可能性,具有足够的准确性,船上收集的大量数据,然后通过卫星发送它们,以比传统更便宜或/和更快的方式。该策略适用于船舶地面设备远程诊断和维修决策。因此,大大节省了电信成本和电信时间。
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
自引率
0.00%
发文量
0
期刊最新文献
Applying B and ProB to a Real-world Data Validation Project A Method of Point Cloud Processing in Transformer Substation Computational Task Offloading Scheme based on Deep Learning for Financial Big Data A Feasible System of Automatic Flame Detection and Tracking for Fire-fighting Robot Design of Parallel Algorithm of Transfer Learning based on Weak Classifier
×
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