基于稀疏逼近方法的脉冲源信号建模

A. Kim, O. Lukovenkova, Y. Marapulets, A. Tristanov
{"title":"基于稀疏逼近方法的脉冲源信号建模","authors":"A. Kim, O. Lukovenkova, Y. Marapulets, A. Tristanov","doi":"10.1109/SCM.2015.7190469","DOIUrl":null,"url":null,"abstract":"The work is devoted to modeling and classification of pulse signals based on sparse approximation method. The described models and classification algorithms are tested on geoacoustic emission signals.","PeriodicalId":106868,"journal":{"name":"2015 XVIII International Conference on Soft Computing and Measurements (SCM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Modeling of signals of pulse origin on the basis of sparse approximation scheme\",\"authors\":\"A. Kim, O. Lukovenkova, Y. Marapulets, A. Tristanov\",\"doi\":\"10.1109/SCM.2015.7190469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The work is devoted to modeling and classification of pulse signals based on sparse approximation method. The described models and classification algorithms are tested on geoacoustic emission signals.\",\"PeriodicalId\":106868,\"journal\":{\"name\":\"2015 XVIII International Conference on Soft Computing and Measurements (SCM)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 XVIII International Conference on Soft Computing and Measurements (SCM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCM.2015.7190469\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 XVIII International Conference on Soft Computing and Measurements (SCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCM.2015.7190469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

研究了基于稀疏逼近方法的脉冲信号建模与分类。所描述的模型和分类算法在地声发射信号上进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Modeling of signals of pulse origin on the basis of sparse approximation scheme
The work is devoted to modeling and classification of pulse signals based on sparse approximation method. The described models and classification algorithms are tested on geoacoustic emission signals.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The maintenance of structures Modeling of signals of pulse origin on the basis of sparse approximation scheme Inertialess adaptive algorithms for control of technical objects with limited uncertainty Decision support system based on 4-valued logic with multi-interpretations Evolutionary algorithms for digital electronic printed circuit board design
×
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