基于混凝土块体夹杂物实验数据集的探地雷达特征提取与选择

F. Queiroz, D. Vieira, X. L. Travassos, M. F. Pantoja
{"title":"基于混凝土块体夹杂物实验数据集的探地雷达特征提取与选择","authors":"F. Queiroz, D. Vieira, X. L. Travassos, M. F. Pantoja","doi":"10.1109/ICMLA.2012.139","DOIUrl":null,"url":null,"abstract":"Ground Penetrating Radar systems have been successfully used to access concrete structures conditions. Moreover, inclusions in concrete can be discriminated by simple models based on traces obtained by GPR. In this work, concrete blocks with different inclusions were probed in controlled conditions. Some features were extracted from Ascans of this experimental data set. To get efficient models, raw data were submitted to features selection and space reduction methods. Without complex data pre-processing, good accuracy and more explainable models with less computational burden were obtained.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"119 1","pages":"48-53"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Feature Extraction and Selection in Ground Penetrating Radar with Experimental Data Set of Inclusions in Concrete Blocks\",\"authors\":\"F. Queiroz, D. Vieira, X. L. Travassos, M. F. Pantoja\",\"doi\":\"10.1109/ICMLA.2012.139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ground Penetrating Radar systems have been successfully used to access concrete structures conditions. Moreover, inclusions in concrete can be discriminated by simple models based on traces obtained by GPR. In this work, concrete blocks with different inclusions were probed in controlled conditions. Some features were extracted from Ascans of this experimental data set. To get efficient models, raw data were submitted to features selection and space reduction methods. Without complex data pre-processing, good accuracy and more explainable models with less computational burden were obtained.\",\"PeriodicalId\":74528,\"journal\":{\"name\":\"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications\",\"volume\":\"119 1\",\"pages\":\"48-53\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2012.139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2012.139","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

探地雷达系统已成功地用于获取混凝土结构状况。此外,根据探地雷达探测到的迹线,可以用简单的模型对混凝土中的夹杂物进行判别。在这项工作中,在控制条件下对不同夹杂物的混凝土块进行了探测。从该实验数据集的Ascans中提取了一些特征。为了得到有效的模型,将原始数据提交到特征选择和空间约简方法中。无需复杂的数据预处理,可获得精度高、可解释性强、计算量少的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature Extraction and Selection in Ground Penetrating Radar with Experimental Data Set of Inclusions in Concrete Blocks
Ground Penetrating Radar systems have been successfully used to access concrete structures conditions. Moreover, inclusions in concrete can be discriminated by simple models based on traces obtained by GPR. In this work, concrete blocks with different inclusions were probed in controlled conditions. Some features were extracted from Ascans of this experimental data set. To get efficient models, raw data were submitted to features selection and space reduction methods. Without complex data pre-processing, good accuracy and more explainable models with less computational burden were obtained.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信