Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6784764, 8 pages
https://doi.org/10.1155/2017/6784764
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.

Linked References

  1. J. H. Cai, Information Processing of The Fire Detection Algorithm, vol. 6, South china university of technology, 2012.
  2. Z. F. Shi and Y. L. Jiang, “Research of Multi. Sensor information fusion fire detection system,” Techniques of Automalkm & Applications, vol. 35, no. 9, pp. 8–11, 2016. View at Google Scholar
  3. Z. H. Tang, S. Wang, and T. Chen, “Application of multisensor/multicriteria detector in fire detection,” Journal of Transducer Technology, vol. 20, no. 3, pp. 33–35, 2001. View at Google Scholar
  4. X. Zhang, J. X. Sui, and Y. Zhang, “Study on the application of information fusion technology in fire detection,” China Safety Science Journal, vol. 21, no. 6, pp. 95–98, 2011. View at Google Scholar
  5. C. Xu, X. C. Li, L. Zhang, and H. Yang, “Multi-sensor fire early-warning system based on zigbee,” Journal of Xihua University (Natural Science), vol. 31, no. 6, pp. 73–76, 2012. View at Google Scholar
  6. Z. G. Hu and M. H. Zhao, “A wireless fire detection and alarm system based on the information fusion technology,” Electronic Sci. & Tech, vol. 25, no. 10, pp. 36–39, 2012. View at Google Scholar
  7. Q. F. Yu, Research on Electrical Fire Forecast System and Its Application Based on Wavelet Analysis and Data Fusion, Yanshan University, Qinhuangdao, China, 2013.
  8. H. Yan, T. C. Wang, X. X. Hu, and Y. Z. Xie, “Fire detection algorithm based on extension neural network,” Transducer and Microsystem Technologies, vol. 35, no. 6, pp. 113–116, 2016. View at Google Scholar
  9. B. Jin, W. Cui, and Z. G. Jin, “Research on early warning of fire data fusion of bayesian network based on normal distribution,” Application Research of Computers, vol. 33, no. 5, pp. 1473–1476, 2016. View at Google Scholar
  10. S. Verstockt, S. Van Hoecke, P. De Potter et al., “Multi-modal time-of-flight based fire detection,” Multimedia Tools and Applications, vol. 69, no. 2, pp. 313–338, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. O. S. da Penha and E. F. Nakamura, “Fusing light and temperature data for fire detection,” in Proceedings of the 15th IEEE Symposium on Computers and Communications, pp. 107–112, Riccione, Italy, June 2010. View at Publisher · View at Google Scholar
  12. S. Q. Yang, J. F. Ning, and D. G. He, “Identification of varieties of rice based on sparse representation,” Transactions of the CSAE, vol. 27, no. 3, pp. 191–195, 2011. View at Google Scholar
  13. S. B. Chen, L. Zhao, and B. Luo, “Kernel fisher discrimination dictionary learning for sparse representation classification,” Journal of Optoelectronics Laser, vol. 25, no. 10, pp. 89–93, 2014. View at Google Scholar
  14. B. X. Wang, B. J. Zhao, and L. B. Tan, “Robust visual tracking algorithm based on bidirectional sparse representation,” Acta Automatica Sinica, vol. 63, 234201-1-234201-11, no. 23, 2014. View at Google Scholar
  15. Q.-S. Lian, B.-S. Shi, and S.-Z. Chen, “Research advances on dictionary learning models, algorithms and applications,” Acta Automatica Sinica, vol. 41, no. 2, pp. 240–260, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. H. J. Qi, Y. G. Wang, J. Ding, and H. G. Liu, “SAR target recognition based on multi—information dictionary learning and sparse representation,” Systems Engineering and Electronics, vol. 37, no. 6, pp. 1280–1287, 2015. View at Google Scholar
  17. P. O. Hoyer, “Non-negative matrix factorization with sparseness constraints,” Journal of Machine Learning Research, vol. 5, no. 3, pp. 1457–1469, 2004. View at Google Scholar · View at MathSciNet
  18. Z. Jiang, Z. Lin, and L. S. Davis, “Label consistent K-SVD: learning a discriminative dictionary for recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2651–2664, 2013. View at Publisher · View at Google Scholar
  19. M. Elad, “Sparse and Redundant Representations,” in Theory to Applications in Signal and Image Processing, Springer, New York, NY, USA, 2010. View at Google Scholar
  20. J. Jiang, Sparse Representation Based Research on Classification Problems, Huazhong University of Science & Technology, Wuhan, China, 2014.
  21. E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: universal encoding strategies,” IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406–5425, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  22. H. Zhang, Y. Wang, X. Y. Chang, and Z. B. Xu, “L1/2 regularization,” Science China E, vol. 40, no. 3, pp. 412–42, 2010. View at Google Scholar
  23. “Representative of L1/2 regularization among Lq (0<q ≤1) regularizations: an Experimental Study Based on Phase Diagram,” Acta Automatica Sinica, vol. 31, no. 8, pp. 1225–1228, 2012.
  24. D. L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, “Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit,” Institute of Electrical and Electronics Engineers Transactions on Information Theory, vol. 58, no. 2, pp. 1094–1121, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. X. L. Zhao, Face Recognition Based on Sparse Representation in Security Protection System, Northwest University, Xian, China, 2014.
  26. L. B. Wu, J. Fang, and Q. Y. Xie, Fire Detection and Information Processing, Chemical industry press, Beijing, China, 2006.
  27. R. C. Luo and K. L. Su, “Autonomous fire-detection system using adaptive sensory fusion for intelligent security robot,” IEEE/ASME Transactions on Mechatronics, vol. 12, no. 3, pp. 274–281, 2007. View at Publisher · View at Google Scholar · View at Scopus