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      상황인지 센서를 활용한 지능형 산불 이동 예측 및 탐지 알고리즘에 관한 연구 = A Study on forest fires Prediction and Detection Algorithm using Intelligent Context-awareness sensor

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      https://www.riss.kr/link?id=A101325434

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire in complex situations. In addition, it is possible to differential management of intensive fire detection and prediction for required dividing the state of fire zone. Therefore we propose an algorithm to determine the prediction and detection from the fire parameters as an temperature, humidity, Co2 and the flame in real-time by using a context awareness sensor and also suggest algorithm that provide the path of fire diffusion and service the secure safety zone prediction.
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      In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it i...

      In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire in complex situations. In addition, it is possible to differential management of intensive fire detection and prediction for required dividing the state of fire zone. Therefore we propose an algorithm to determine the prediction and detection from the fire parameters as an temperature, humidity, Co2 and the flame in real-time by using a context awareness sensor and also suggest algorithm that provide the path of fire diffusion and service the secure safety zone prediction.

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      참고문헌 (Reference)

      1 S. Kewei, "Using Wireless Sensor Networks for Fire Rescue Applications: Requirements and Challenges" 2006

      2 M. Holmes, "The Application of Data Mining Tools and Statistical Techniques to Identify Patterns and Changes in Fire Events" The University of Auckland 2009

      3 M. A. Parisien, "Spatial Patterns of Forest Fires in Canada, 1980~1999" 15 : 361-374, 2006

      4 A Zhao, "Research on Electronic-nose Application Based on Wireless Sensor Networks" 2006

      5 "Numerical Recipes Example Book" Cambridge University Press 1992

      6 Andrews, P. L., "Fire modeling and information system technology" 10 : 343-352, 2001

      7 Turgay Celik, "Fast and Efficient Method for Fire Detection Using Image Processing" 한국전자통신연구원 32 (32): 881-890, 2010

      8 Hirsch, K. G, "Canadian Forest Fire Behavior Prediction (FBP) System: user's guide" Canadian Forest Service 121-, 1996

      9 Li Cunbin, "Analysis of Forest Fire Spread Trend Surrounding Transmission Line Based on Rothermel Model and Huygens Principle" 9 (9): 51-60, 2014

      10 Lloret, J., "A wireless sensor network deployment for rural and forest fire detection and verification" 9 (9): 8722-8747, 2009

      1 S. Kewei, "Using Wireless Sensor Networks for Fire Rescue Applications: Requirements and Challenges" 2006

      2 M. Holmes, "The Application of Data Mining Tools and Statistical Techniques to Identify Patterns and Changes in Fire Events" The University of Auckland 2009

      3 M. A. Parisien, "Spatial Patterns of Forest Fires in Canada, 1980~1999" 15 : 361-374, 2006

      4 A Zhao, "Research on Electronic-nose Application Based on Wireless Sensor Networks" 2006

      5 "Numerical Recipes Example Book" Cambridge University Press 1992

      6 Andrews, P. L., "Fire modeling and information system technology" 10 : 343-352, 2001

      7 Turgay Celik, "Fast and Efficient Method for Fire Detection Using Image Processing" 한국전자통신연구원 32 (32): 881-890, 2010

      8 Hirsch, K. G, "Canadian Forest Fire Behavior Prediction (FBP) System: user's guide" Canadian Forest Service 121-, 1996

      9 Li Cunbin, "Analysis of Forest Fire Spread Trend Surrounding Transmission Line Based on Rothermel Model and Huygens Principle" 9 (9): 51-60, 2014

      10 Lloret, J., "A wireless sensor network deployment for rural and forest fire detection and verification" 9 (9): 8722-8747, 2009

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2018-01-01 평가 등재학술지 선정 (계속평가) KCI등재
      2017-12-01 평가 등재후보로 하락 (계속평가) KCI등재후보
      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2011-11-23 학술지명변경 외국어명 : THE JOURNAL OF The KOREAN Institute Of Maritime information & Communication Science -> Journal of the Korea Institute Of Information and Communication Engineering KCI등재
      2011-11-16 학회명변경 영문명 : International Journal of Information and Communication Engineering(IJICE) -> The Korea Institute of Information and Communication Engineering KCI등재
      2011-11-14 학회명변경 한글명 : 한국해양정보통신학회 -> 한국정보통신학회
      영문명 : 미등록 -> International Journal of Information and Communication Engineering(IJICE)
      KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2004-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2002-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.23 0.23 0.27
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.24 0.22 0.424 0.11
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