Neural network and pattern recognition techniques for characterizing ultrasonic transducers

M. Obaidat, D. Abu-Saymeh
{"title":"Neural network and pattern recognition techniques for characterizing ultrasonic transducers","authors":"M. Obaidat, D. Abu-Saymeh","doi":"10.1109/PCCC.1992.200513","DOIUrl":null,"url":null,"abstract":"A system in which transducers are characterized and classified using both pattern recognition and neural network algorithms is presented. Various techniques are investigated, compared, and analyzed. The hardware of the system used in collecting and measuring the characteristics of the transducers, and the parameters used in classifying the transducers into classes are described. The neural network and pattern recognition algorithms used to classify the transducers are introduced. The results of the classification are presented and the various algorithms are analyzed and compared.<<ETX>>","PeriodicalId":250212,"journal":{"name":"Eleventh Annual International Phoenix Conference on Computers and Communication [1992 Conference Proceedings]","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eleventh Annual International Phoenix Conference on Computers and Communication [1992 Conference Proceedings]","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PCCC.1992.200513","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

A system in which transducers are characterized and classified using both pattern recognition and neural network algorithms is presented. Various techniques are investigated, compared, and analyzed. The hardware of the system used in collecting and measuring the characteristics of the transducers, and the parameters used in classifying the transducers into classes are described. The neural network and pattern recognition algorithms used to classify the transducers are introduced. The results of the classification are presented and the various algorithms are analyzed and compared.<>
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
表征超声换能器的神经网络和模式识别技术
提出了一种利用模式识别和神经网络算法对换能器进行表征和分类的系统。研究、比较和分析了各种技术。介绍了采集和测量传感器特性的硬件系统,以及对传感器进行分类的参数。介绍了用于分类换能器的神经网络和模式识别算法。给出了分类结果,并对各种算法进行了分析和比较。
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
Statistical evaluation of data allocation optimization techniques with application to computer integrated manufacturing Pragmatic trellis coded modulation: a simulation using 24-sector quantized 8-PSK Performance of bidirectional serial search PN acquisition in direct sequence spread spectrum system A user friendly specification environment for FDT and its application to LOTOS Location transparent connection management: a survey of protocol issues
×
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