Event Detection and Classification for Fiber Optic Perimeter Intrusion Detection System

Xiaohua Gu, Tian Wang, Jun Peng, Hongjin Wang, Qinfeng Xia and Du Zhang
Additional contact information
Xiaohua Gu: Chongqing University of Science & Technology, Chongqing, China
Tian Wang: Chongqing University of Science & Technology, Chongqing, China
Jun Peng: Chongqing University of Science & Technology, Chongqing, China
Hongjin Wang: Sinopec Chongqing Fuling Shale Gas Exploration and Development Co., Chongqing, China
Qinfeng Xia: Sinopec Chongqing Fuling Shale Gas Exploration and Development Co., Chongqing, China
Du Zhang: Macau University of Science and Technology, Macau, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2019, vol. 13, issue 4, 39-55

Abstract: A perimeter intrusion detection system (PIDS) is critical for the security of a shale gas field. Among many technologies, the fiber optic sensor-based method is the most widely used, due to its passive, low-cost, long-life, and strong anti-interference ability and strong environmental adaptability. This article proposes an event detection and classification method for a fiber optic PIDS. In general, three types of features are extracted for an improved double-threshold method to improve the probability of detection. Also, the detected intrusion events are distinguished by a support vector machine with wavelet features to reduce the nuisance alarm rate. Experiments on the PIDS in Chongqing Fuling's shale gas field show that detection algorithms based on the feature of short-time energy and short-time wavelet coefficient energy are much better, and the performance of event classification is satisfactory.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJCINI.2019100102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:13:y:2019:i:4:p:39-55

Access Statistics for this article

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) is currently edited by Kangshun Li

More articles in International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().