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Author:

Ai, Min (Ai, Min.) | Tian, Rui (Tian, Rui.)

Indexed by:

CPCI-S EI

Abstract:

Fire accidents in rail vehicles often cause unpredictable catastrophic losses due to high population density and closed environment. At present, existing smart fire prevention schemes are mostly based on the emergency treatments after the fire. Since it takes time for firefighters arriving at the fire, the fire may already become disastrous at that time. This paper proposes a detection framework and also detailed sensing and data processing technologies, in order to detect volatile flammable liquid in closed spaces such as rail vehicle carriages. The proposed mechanism is designed to eliminate potential fire disaster based on gas vapor sensor network. Experiment results shows the proposed surveillant system can detect gasoline vapor components in small space with high sensitivity while maintaining very low false detection rates to external interferences.

Keyword:

Outlier detection Fire alarming Sensor network Gas vapor

Author Community:

  • [ 1 ] [Ai, Min]China Railway Signal & Commun Shanghai Engn Bur G, Shanghai 200436, Peoples R China
  • [ 2 ] [Tian, Rui]Beijing Univ Technol, Beijing Engn Res Ctr IoT Software & Syst, Informat Dept, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Tian, Rui]Beijing Univ Technol, Beijing Engn Res Ctr IoT Software & Syst, Informat Dept, Beijing 100124, Peoples R China

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Source :

AD HOC NETWORKS, ADHOCNETS 2019

ISSN: 1867-8211

Year: 2019

Volume: 306

Page: 302-313

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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