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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6784764, 8 pages
Research Article

Composite Fire Detection System Using Sparse Representation Method

1School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
2School of Safety Engineering, Shenyang Aerospace University, Shenyang 110136, China
3School of Automation, Shenyang Aerospace University, Shenyang 110136, China

Correspondence should be addressed to Na Qu

Received 25 September 2017; Accepted 20 November 2017; Published 11 December 2017

Academic Editor: Aimé Lay-Ekuakille

Copyright © 2017 Na Qu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes that fire parameter data of smoke, temperature, and CO is fused by sparse representation algorithm. It designs a kind of overcomplete dictionary and obtains the sparse solution of fire recognition through norm, norm, norm, and norm, respectively, in order to select more suitable norm type. A comprehensive classification method is proposed for fire identification. The simulation results show that norm and norm are used to obtain the solution with remarkable sparsity and high accuracy. The comprehensive classification method is more effective than minimum residual method and sum of weight coefficients method. This paper uses DSP TMS320F28022 as the core chip, TC72 as the temperature sensor, MQ-7 as the CO gas sensor, and MQ-9 as the smoke sensor to design the hardware of fire detection system. Code Composer Studio (CCS) software is used to compile and debug the program. Proteus software is used to load the program into the hardware circuit for joint simulation. The simulation results show that system design is feasible.