• Title/Summary/Keyword: hazard mitigation

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Development of a deep-learning based tunnel incident detection system on CCTVs (딥러닝 기반 터널 영상유고감지 시스템 개발 연구)

  • Shin, Hyu-Soung;Lee, Kyu-Beom;Yim, Min-Jin;Kim, Dong-Gyou
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.19 no.6
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    • pp.915-936
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    • 2017
  • In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

Study on Standardization of the Environmental Impact Evaluation Method of Extremely Low Frequency Magnetic Fields near High Voltage Overhead Transmission Lines (고압 가공송전선로의 극저주파자기장 환경영향평가 방법 표준화에 관한 연구)

  • Park, Sung-Ae;Jung, Joonsig;Choi, Taebong;Jeong, Minjoo;Kim, Bu-Kyung;Lee, Jongchun
    • Journal of Environmental Impact Assessment
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    • v.27 no.6
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    • pp.658-673
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    • 2018
  • Social conflicts with extremely low frequency magnetic field(ELF-MF) exposures are expected to exacerbate due to continued increase in electric power demand and construction of high voltage transmission lines(HVTL). However, in current environmental impact assessment(EIA) act, specific guidelines have not been included concretely about EIA of ELF-MF. Therefore, this study conducted a standardization study on EIA method through case analysis, field measurement, and expert consultation of the EIA for the ELF-MF near HVTL which is the main cause of exposures. The status of the EIA of the ELF-MF and the problem to be improved are derived and the EIA method which can solve it is suggested. The main contents of the study is that the physical characteristics of the ELF-MF affected by distance and powerload should be considered at all stages of EIA(survey of the current situation - Prediction of the impacts - preparation of mitigation plan ? post EIA planning). Based on this study, we also suggested the 'Measurement method for extremely low frequency magnetic field on transmission line' and 'Table for extremely low frequency magnetic field measurement record on transmission line'. The results of this study can be applied to the EIA that minimizes the damage and conflict to the construction of transmission line and derives rational measures at the present time when the human hazard to long term exposure of the ELF-MF is unclear.