검색결과

검색조건
좁혀보기
검색필터
결과 내 재검색

간행물

    분야

      발행연도

      -

        검색결과 4

        1.
        2022.10 구독 인증기관·개인회원 무료
        This study was performed to assess the cosmic-ray effect caused by altitude in the aerial gammaray measurement. For the gamma-ray measurement experiment by altitude, the aerial survey system composed of four 4×4×16 inches large volume NaI (Tl) detectors was used. The aerial survey system was installed in a rotor-craft to stably keep its flight altitude and position. In addition, in order to avoid to time-dependent shielding effects with the amount of fuel, a rotor-craft of which the fuel tank is not located beneath the cabin floor was selected. In this study, the ROI (Region Of Interest) was set to the 3~6 MeV range to assess the cosmic-ray contribution to the gamma-ray spectrum that could ignore the contribution of the dominant natural radionuclides. The gamma-ray spectra measured inside and outside of the rotor-craft on the ground were compared to evaluate the shielding effects of the aircraft body. As a result, the count rate of the 40K photo peak was decreased by about 10% when measuring the inside compared to the outside. On the other hand, the total count rate of the 3~6 MeV region was decreased by about 0.7% under the same condition. Therefore, the aircraft body effect was insignificant in 3~6 MeV region considering the relative uncertainty of 0.04~0.78% (1σ). In addition, the count rate in the 3~6 MeV range according to altitude was evaluated to assess the cosmic-ray effect. In order to evaluate the change in the ROI count rate according to the altitude, the gamma-ray spectrum was measured in the range of 300~2,000 m above the sea to avoid the effect of terrestrial radiation. As a result, the relationship between altitude and count rate in the 3~6 MeV range showed a high correlation with the R2 value of 0.99, when the approximate equation was derived in the form of a quadratic polynomial. Also, the count rate of 3~6 MeV at 50~500 m above the ground was estimated using the correlation equation, and this value was compared with the measured count rate. As a result of comparing the average value of estimated count rate and measured count rate, the relative difference is less than 2%. Considering the relative uncertainty of 0.78~4.11% (1σ), it was possible to evaluate the count rate of the 3~6 MeV region relatively accurately. The results of this study could be used for further study on background dose corrections in aerial survey.
        2.
        2020.12 KCI 등재후보 구독 인증기관 무료, 개인회원 유료
        본 연구는 스마트건설 지원을 위한 드론 활용의 활성화를 위해 RTK 드론 기반의 항공측량 정밀도를 분석하고자 GPS만을 사용하는 방식, GCP를 설치하는 방식, RTK 드론을 이용한 방식의 정사영상의 위치정확도를 분석하였고 사업의 목적과 대상지의 형태에 따른 드론 활용의 기준을 제시하였다. 또한 상용 드론을 이용한 체적기반의 토공량 산출을 2.5D 환경에서 산출하여 기존 방법과 비교해서 드론영상을 효율적으로 활용할 수 있는 방법을 제시하였다. 본 연구로 대규모 건설현장의 작업효율 및 드론 활성화가 기대된다.
        4,000원
        4.
        2017.03 KCI 등재 서비스 종료(열람 제한)
        The purpose of this study is to analyze the accuracy of cultivated crop database in agricultural farm business using UAV(Unmanned Aerial Vehicle) and field survey over Daesso-myeon, Umsung-gun, Chungbuk. When comparing with agricultural farm business and cadastral maps, Daeso-myeon crop field shows 29.8%(2,030 parcels out of 6,822 parcels) is either mismatched or missing. It covers almost 19.3%(3.4km2 of 17.6km2) of total farmland. In order to solve these problems, it is necessary to prepare a multifaceted plan including cadastral map. Comparative analysis of the cultivated crop registered in the agricultural farm business and the field survey agreed only in 3,622 parcels in total 6,822 parcels whereas 3200 parcels disagree. Among these disagreed parcels 2,030(29.8%) have been confirmed as unregistered farm business entity. Accuracy of cultivated crop registered in agricultural farm business agreed in 75.6% cases. Especially the paddy field registration is more accurate that other crops. These discrepancies can lead to false payment in agricultural farm business. For exploration and analysis of regional resources, UAV images can be used together with farm business management database and cadastral map to get a clearer grasp over on-site resources and conditions.