Citation: | XIE Tian-hua, LIN Yan, YANG Zu-yao, JIN Liang-an. Bayesian network model for intelligent recognition of typical compartment fire[J]. Journal of Traffic and Transportation Engineering, 2016, 16(2): 90-99. doi: 10.19818/j.cnki.1671-1637.2016.02.011 |
[1] |
LEBLANC D. Fire environments typical of navy ships[D]. London: Worcester Polytechnic Institute, 1998.
|
[2] |
CALABRESE F, CORALLO A, MARGHERITA A, et al. A knowledge-based decision support system for shipboard damage control[J]. Expert Systems with Applications, 2012, 39(9): 8204-8211. doi: 10.1016/j.eswa.2012.01.146
|
[3] |
OWRUTSKY J C, STEINHURST D A, MINOR C P, et al. Long wavelength video detection of fire in ship compartments[J]. Fire Safety Journal, 2006, 41(4): 315-320. doi: 10.1016/j.firesaf.2005.11.011
|
[4] |
GOTTUK D T, LYNCH J A, ROSE-PEHRSSON S L, et al. Video image fire detection for shipboard use[J]. Fire Safety Journal, 2006, 41(4): 321-326. doi: 10.1016/j.firesaf.2005.12.007
|
[5] |
ROSE-PEHRSSON S L, SHAFFER R E, HART S J, et al. Multi-criteria fire detection systems using a probabilistic neural network[J]. Sensors and Actuators B: Chemical, 2000, 69(3): 325-335. doi: 10.1016/S0925-4005(00)00481-0
|
[6] |
KUO H C, CHANG H K. A real-time shipboard fire-detection system based on grey-fuzzy algorithms[J]. Fire Safety Journal, 2003, 38(4): 341-363. doi: 10.1016/S0379-7112(02)00088-7
|
[7] |
ROSE-PEHRSSON S L, HART S J, STREET T T, et al. Early warning fire detection system using a probabilistic neural network[J]. Fire Technology, 2003, 39(2): 147-171. doi: 10.1023/A:1024260130050
|
[8] |
WANG S J, JENG D L, TSAI M T. Early fire detection method in video for vessels[J]. Journal of Systems and Software, 2009, 82(4): 656-667. doi: 10.1016/j.jss.2008.09.025
|
[9] |
WILKINS D C, SNIEZEK J A, TETEM PA, et al. The DC-SCS supervisory control system for ship damage control: volume 1—design overview[R]. Washington DC: Naval Research Laboratory, 2001.
|
[10] |
ROSE-PEHRSSON S L, MINOR C P, STEINHURST D A, et al. Volume sensor for damage assessment and situational awareness[J]. Fire Safety Journal, 2006, 41(4): 301-310. doi: 10.1016/j.firesaf.2005.12.005
|
[11] |
MINOR C P, JOHNSON K J, ROSE-PEHRSSON S L. A full-scale prototype multisensor system for fire detection and situational awareness[C]//SPIE. Proceedings of SPIE 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications. Bellingham: SPIE, 2007: 11-12.
|
[12] |
李卡麟. 基于二叉树的LS-WSVM模型在早期火灾分类上的研究[D]. 汕头: 汕头大学, 2010.
LI Ka-lin. Research on LS-WSVM based on binary tree in early fire multi-class classification[D]. Shantou: Shantou University, 2010. (in Chinese)
|
[13] |
庄哲民, 李卡麟, 张新蜂, 等. 用于早期火灾分类的非线性决策树支持向量机[J]. 火灾科学, 2009, 18(4): 206-211. doi: 10.3969/j.issn.1004-5309.2009.04.004
ZHUANG Zhe-min, LI Ka-lin, ZHANG Xin-feng, et al. Nonlinear decision tree support vector machine for early fire classification[J]. Fire Safety Science, 2009, 18(4): 206-211. (in Chinese) doi: 10.3969/j.issn.1004-5309.2009.04.004
|
[14] |
孙福志, 于军琪, 杨柳. 火灾识别中RS-SVM模型的应用[J]. 计算机工程与应用, 2010, 46(3): 198-200. doi: 10.3778/j.issn.1002-8331.2010.03.061
SUN Fu-zhi, YU Jun-qi, YANG Liu. Application of RS-SVM model for fire identification[J]. Computer Engineering and Application, 2010, 46(3): 198-200. (in Chinese) doi: 10.3778/j.issn.1002-8331.2010.03.061
|
[15] |
赵亚琴. 基于模糊神经网络的火灾识别算法[J]. 计算机仿真, 2015, 32(2): 369-373. doi: 10.3969/j.issn.1006-9348.2015.02.080
ZHAO Ya-qin. Forest fire recognition algorithm based on fuzzy neural network[J]. Computer Simulation, 2015, 32(2): 369-373. (in Chinese) doi: 10.3969/j.issn.1006-9348.2015.02.080
|
[16] |
KIM J H, LATTIMER B Y. Real-time probabilistic classification of fire and smoke using thermalimagery for intelligent firefighting robot[J]. Fire Safety Journal, 2015, 72: 40-49. doi: 10.1016/j.firesaf.2015.02.007
|
[17] |
AKHTAR M J, UTNE I B. Human fatigue's effect on the risk of maritime groundings—a Bayesian network modeling approach[J]. Safety Science, 2014, 62: 427-440. doi: 10.1016/j.ssci.2013.10.002
|
[18] |
GROIS E, WILKINS D C, EARMAN I, et al. The DC-SCS supervisory control system for ship damage control: volume4—intelligent reasoning[R]. Washington DC: Naval Research Laboratory, 2001.
|
[19] |
BAKSH A A, KHAN F, GADAG V, et al. Network based approach for predictive accident modelling[J]. Safety Science, 2015, 80: 274-287. doi: 10.1016/j.ssci.2015.08.003
|
[20] |
刘志军, 纪卓尚, 林焰. 基于贝叶斯网络的船舶机舱火灾风险分析研究[J]. 中国造船, 2010, 51(3): 199-205. doi: 10.3969/j.issn.1000-4882.2010.03.024
LIU Zhi-jun, JI Zhuo-shang, LIN Yan. Fire risk analysis in ship engine room based on Bayesian networks[J]. Shipbuilding of China, 2010, 51(3): 199-205. (in Chinese) doi: 10.3969/j.issn.1000-4882.2010.03.024
|
[21] |
WILLIAMS F W, SCHEFFEY J L, HILL S A, et al. Postflashover fires in shipboard compartments aboard ex-USS Shadwell: phase V—fire dynamics[R]. Washington DC: Naval Research Laboratory, 1999.
|
[22] |
WILLIAMS F W, TATEM P A, XUAN N, et al. Results of1998DC-ARM/ISFE demonstration tests[R]. Washington DC: Naval Research Laboratory, 2000.
|
[23] |
HOOVER J B, WHITEHURST C L, CHANG E B, et al. Final report on fire performance of shipboard electronic space materials[R]. Washington DC: Naval Research Laboratory, 2006.
|
[24] |
HOOVER J B, BAILEY J L, WILLAUER H D, et al. Evaluation of submarine hydraulic system explosion and fire hazards[R]. Washington DC: Naval Research Laboratory, 2005.
|
[25] |
WONG J T, GOTTUK D T, ROSE-PETHRSSON S L, et al. Results of multi-criteria fire detection system tests[R]. Washington DC: Naval Research Laboratory, 2000.
|