Development of ERESS in panic-type disasters: Disaster recognition algorithm by buffering-SVM

K. Mori, Takahumi Nakamura, Jun Fujimura, K. Tsudaka, T. Wada, H. Okada, K. Ohtsuki
{"title":"Development of ERESS in panic-type disasters: Disaster recognition algorithm by buffering-SVM","authors":"K. Mori, Takahumi Nakamura, Jun Fujimura, K. Tsudaka, T. Wada, H. Okada, K. Ohtsuki","doi":"10.1109/ITST.2013.6685569","DOIUrl":null,"url":null,"abstract":"Many people have faced mortal risks due to sudden disasters such as fires, earthquakes, and terrorisms all over the world. To solve this kind of problems, we are developing ERESS (Emergency Rescue Evacuation Support System). This system consists of only mobile terminals with various sensors. In ERESS, these sensor data are exchanged and analyzed using only ad-hoc communication among nearby mobile terminals. ERESS aims to reduce the number of victims by supporting real-time evacuation in the critical situation for less than 30 seconds immediately after disaster outbreaks. The conventional ERESS recognized the disaster outbreaks by behavior analysis of terminal holders from sensor data using SVM (Support Vector Machine). However, there was a problem that it is difficult to obtain high reliability of judging disaster outbreak. In this paper, we propose a new method by buffering judgment results of SVM. We carry out experiments on the emergency evacuation with more than 200 examinees. We investigate the judgment accuracy of the proposed method and the time required for recognition of disaster outbreak.","PeriodicalId":117087,"journal":{"name":"2013 13th International Conference on ITS Telecommunications (ITST)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 13th International Conference on ITS Telecommunications (ITST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITST.2013.6685569","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Many people have faced mortal risks due to sudden disasters such as fires, earthquakes, and terrorisms all over the world. To solve this kind of problems, we are developing ERESS (Emergency Rescue Evacuation Support System). This system consists of only mobile terminals with various sensors. In ERESS, these sensor data are exchanged and analyzed using only ad-hoc communication among nearby mobile terminals. ERESS aims to reduce the number of victims by supporting real-time evacuation in the critical situation for less than 30 seconds immediately after disaster outbreaks. The conventional ERESS recognized the disaster outbreaks by behavior analysis of terminal holders from sensor data using SVM (Support Vector Machine). However, there was a problem that it is difficult to obtain high reliability of judging disaster outbreak. In this paper, we propose a new method by buffering judgment results of SVM. We carry out experiments on the emergency evacuation with more than 200 examinees. We investigate the judgment accuracy of the proposed method and the time required for recognition of disaster outbreak.
基于缓冲支持向量机的灾难识别算法在恐慌型灾害中的发展
世界各地的许多人都面临着突如其来的灾难,如火灾、地震和恐怖主义带来的生命危险。为了解决这类问题,我们正在开发紧急救援疏散支持系统(ERESS)。该系统仅由带有各种传感器的移动终端组成。在ERESS中,这些传感器数据仅使用附近移动终端之间的自组织通信进行交换和分析。ERESS旨在通过在灾难爆发后立即在不到30秒的时间内支持紧急情况下的实时疏散来减少受害者人数。传统的ERESS是利用支持向量机(SVM)对传感器数据中终端持有者的行为进行分析,从而识别灾害的爆发。但是,存在着难以获得高可靠性判断灾害爆发的问题。本文提出了一种缓冲支持向量机判断结果的新方法。我们对200多名考生进行了紧急疏散实验。研究了该方法的判断精度和识别灾害爆发所需的时间。
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 求助全文
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