Research and Realization of Apron Protection System Based on Internet of Things

Abstract:

The airport perimeter intrusion prevention system is the first defending line of airport security system, and its position is very important. Based on the correlation of air defense, anti-technology and anti-intrinsic, paper compares the advantage and the disadvantage of domestic airport in the selection of the electronic fence pattern. Through technical analysis of the use of acceleration transducer in networking technology for the prevention of airport apron, combined with the actual situation of the Hangzhou Xiaoshan International Airport in airport apron prevention, the ability of electronic fence used in tracking protection and discrimination for location has been improved through system optimization, expanding the application of the electronic fence at the airport apron prevention system. The paper also prospects the application future for internet of things used in the apron protection system

• Periodical: Advanced Materials Research (Volumes 912-914)
• Pages: 1339-1344
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.912-914.1339
• Online since: April 2014
• Authors: Yun Huang*, Ya Jie Zhu, Heng Hui Lian
• Keywords: Acceleration Transducer, Airport Perimeter, Electronic Fence, Internet of Things (IOT)

References

[1] YUXIN-WEI, MU QING-WU. KFDA-waveletcluster based intrusion detection technology [C]. Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition. Berjing, (2007).

DOI: 10.1109/icwapr.2007.4421766

[2] J.C. Juarez, E.W. Maier, Kyoo Nam Choi, etal. Distributed fiberoptic intrusion sensor system [J]. Lightwave Technol. 2005, 23(6).

DOI: 10.1109/jlt.2005.849924

[3] BehzadRazavi. Design of Integrated Circuits for Opti-cal Communication [M]. Singapore: McGraw-Hill Company, 2003: 18-20.

[4] Peleg, S., etal. Multiple resolution texture analysis and classification . Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(6): 518-523.

DOI: 10.1109/tpami.1984.4767557

[5] JOO D，HONG T，HAN I．The neural network models for IDS based on the asymmetric costs off alse negative errors and false positive errors [J]．Expert Systems with Applications 2003, 25(1)：69-75.

DOI: 10.1016/s0957-4174(03)00007-1

[6] Wang J C, Lu W X. Multiobjective optimization of neural network [J]. Science in China (Series B), 1995, 38(8): 971-978.

[7] Jeff Bush, Carol A. Davis, Pepe G. Davis, etal. Buried Fiber Intrusion Detection Sensor with Minimal False Alarm Rates[J]. SPIE, (1998).

DOI: 10.1117/12.323429

[8] N. Otsu. A threshold selection method from Gray-Level histograms. IEEE Trans System, Man and Cybernetics. 1979, 9(1).

DOI: 10.1109/tsmc.1979.4310076

[9] Y Yusoff, W Christmas. J Kittler. Video shot cut detection using adaptive thresholding [A]. British Machine Vision Conference 2000[C]. Bristol: 2000. 340-349.

DOI: 10.5244/c.14.37