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딥러닝 기반 광섬유 분포 음향·진동 계측기술을 활용한 장거리 외곽 침입감지 시스템 개발
김희운 ( Huioon Kim ),이주영 ( Joo-young Lee ),정효영 ( Hyoyoung Jung ),김영호 ( Young Ho Kim ),권준혁 ( Jun Hyuk Kwon ),기송도 ( Song Do Ki ),김명진 ( Myoung Jin Kim ) 한국센서학회 2022 센서학회지 Vol.31 No.1
Distributed fiber-optic acoustic·vibration sensing technology is becoming increasingly popular in many industrial and academic areas such as in securing large edifices, exploring underground seismic activity, monitoring oil well/reservoir, etc. Long-range perimeter intrusion detection exemplifies an application that not only detects intrusion, but also pinpoints where it happens and recognizes kinds of threats made along the perimeter where a single fiber cable was installed. In this study, we developed a distributed fiber-optic sensing device that measures a distributed acoustic·vibration signature (pattern) for intrusion detection. In addition, we demontrate the proposed deep learning algorithm and how it classifies various intrusion events. We evaluated the sensing device and deep learning algorithm in a practical testbed setup. The evaluation results confirm that the developed system is a promising intrusion detection system for long-distance and seamless recognition requirements.