earticle

영어

An acoustic signal-based, tunnel-incident detection system was developed and evaluated. The system was comprised of three components: algorithm, acoustic signal collector, and server system. The algorithm, which was based on nonnegative tensor factorization and a hidden Markov model, processes the acoustic signals to attenuate noise and detect incident-related signals. The acoustic signal collector gathers the tunnel sounds, digitalizes them, and transmits the digitalized acoustic signals to the center server. The server system issues an alert once the algorithm identifies an incident. The performance of the system was evaluated thoroughly in two steps: first, in a controlled tunnel environment using the recorded incident sounds, and second, in an uncontrolled tunnel environment using real-world incident sounds. As a result, the detection rates ranged from 80 to 95% at distances from 50 to 10 m in the controlled environment, and 94 % in the uncontrolled environment. The superiority of the developed system to the existing video image and loop detector-based systems lies in its instantaneous detection capability with less than 2 s.

한국어

본 연구에서는 음향신호 처리기반 터널 돌발상황 탐지시스템을 개발하고 평가하였다. 개발 시스템은 알고리즘, 음향신호 수집기, 서버시스템 세 가지 구성 요소로 구성된다. 비음수 텐서 분해와 은닉 마코프 모델을 이용하여 돌발상황음(충돌, 스키드)을 검출한다. 개발시스템 성능 은 제한된 환경과 실제 운영환경에서 평가되었다. 그 결과, 제한된 환경 평가에서 거리별로 8 0～95%의 검지성능을 보였고, 실제 운영환경에서는 94% 검지성능을 보였다. 기존의 터널 돌 발상황 검지기술인 영상 및 루프검지기 기반 시스템 성능과 비교한 결과, 본 개발 기술의 장점 은 신속한 검지시간(2초 이내)인 것으로 나타났다.