Online detection and classification of disasters by a multiple-input/ single-output sensor for a home security system

Tsukasa Ishigaki, Tomoyuki Higuchi, Kajiro Watanabe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Conventional sensors have been designed to minimize noise effects. Any sensor that is designed to detect a certain physical variable is influenced to a certain degree by other physical variables. This suggests that any sensor is potentially capable of detecting multiple physical variables. In the present study, we consider sensing devices that are easily influenced by several physical variables and make full use of their multi-sensing characteristics through statistical signal processing and machine learning techniques with a wide variety of prior Information. The proposed sensor design approach is completely different from the conventional approach with respect to system design and has advantages in terms of cost and system simplification compared to existing approaches. This new idea can be realized by developing a novel multiple-input/single-output sensor that can detect various variables such as pressure, acceleration, temperature and light emission by a single device. The sensor is applied to monitor the symptoms of fire, earthquake and break-in for the purpose of home security. The proposed security system consists of the following three steps: (1) Detection of disaster by a probabilistic outlier detection procedure using an auto-regressive model, (2) Disaster feature extraction by Kaiman filter on a state space model, and (3) Disaster classification by multiclass support vector machine.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages3136-3143
Number of pages8
Publication statusPublished - 2006 Dec 1
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 2006 Jul 162006 Jul 21

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
Country/TerritoryCanada
CityVancouver, BC
Period06/7/1606/7/21

ASJC Scopus subject areas

  • Software

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