Disaster Detection Using SVDD Group Learning for Emergency Rescue Evacuation Support System
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- Wada Tomotaka
- Department of Engineering Science, Kansai University
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- Higuchi Hiroko
- Department of Engineering Science, Kansai University
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- Komaki Ken
- Department of Engineering Science, Kansai University
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- Iwahashi Haruka
- Department of Engineering Science, Kansai University
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- Ohtsuki Kazuhiro
- Graduate School of Intercultural Studies, Kobe University
説明
<p>Many people have got injured and died by sudden disasters such as fires and terrorisms. We have proposed an Emergency Rescue Support System (ERESS) for reducing victims at the time of disaster. ERESS operates under mobile ad-hoc networks (MANET) composed of handheld terminals such as smartphones and tablets. ERESS terminals have disaster detection algorithm and plural sensors such as acceleration, angular velocity, and geomagnetism. ERESS detects the disaster from the behavior analysis of people by the sensors. In this paper, we propose a new disaster detection method by performing the machine learning in the group using a support vector domain description (SVDD). We are able to detect the abnormal behavior of people by using this method. The results of disaster simulation experiments show the validity of the proposed method.</p>
収録刊行物
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- 日本シミュレーション学会英文誌
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日本シミュレーション学会英文誌 3 (1), 79-96, 2016
一般社団法人 日本シミュレーション学会
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詳細情報 詳細情報について
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- CRID
- 1390001205756835456
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- NII論文ID
- 130005261059
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- ISSN
- 21885303
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可