Fire Seat Intelligent Identification System

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Abstract:

Current fire alarm system can only send fire alarms but failed to report accurately ignition point. So a fire seat intelligent identification system was designed based on wireless sensor networks. The system collects data from wireless sensors using ZigBee wireless communication technology, and carries on cluster analysis on the data set using the improved fuzzy kernel clustering algorithm, and gets accurate clustering results. Finally the ignition source location information is reported to the fire alarm control center. The experimental results show that, compared with other methods, this system realizes real-time monitoring and automatic identification of fire seats, which has virtues of high sensitivity and accuracy and high-speed data transfer.

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421-425

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April 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Gang Zhang, Lu Yang and Yanhua Zhang: Design and Implementation of Ultrasonic Testing System Combining Double Characteristics – Time and Amplitude. Computer Measurement & Control, Vol. 20(2012), pp.567-569.

Google Scholar

[2] Zheng Fang, Chenglin Wen: An Approach for the Detection of Sensor Network with Limited Band width. Bulletin of Science and Technology. Vol. 27(2011), pp.62-63.

Google Scholar

[3] Zhangxue Zhang, Huanqing Cui: Localization in 3D Sensor Networks Using Stochastic Particle Swarm Optimization. Wuhan University Journal of Natural Sciences, 2012, (6). 153-156.

DOI: 10.1007/s11859-012-0884-6

Google Scholar

[4] Dan Liu, Guiying Li: Research on Energy Saving Algorithm in Wireless Sensor Net Work For Forest Fire Forecast. Computer Applications and Software. Vol. 29(2012), pp.141-144.

Google Scholar

[5] Xiaobo Wang: Application of Wireless Sensor Networks in Mine Geophysical Instruments. Coal Geology & Exploration. Vol. 40(2012), pp.75-77.

Google Scholar

[6] Bo Sun, Suixiang Gao: An Optimization Model for Maximizing the Lifetime of Clusters in Wireless Sensor Networks. Computer simulation. Vol. 25, pp.116-120.

Google Scholar

[7] Yangcheng He, Shitong Wang and Nan Jiang: Mercer-kernel based mixed C-means fuzzy clustering algorithm with attributes weights in feature space.Computer Engineering and Applications. Vol. 47 (2011), p.159—163.

Google Scholar