Our research team has developed a gamma ray detector which can be distributed over large area through air transport. Multiple detectors (9 devices per 1 set) are distributed to measure environmental radiation, and information such as the activity and location of the radiation source can be inferred using the measured data. Generally, radiation is usually measured by pointing the detector towards the radioactive sources for efficient measurement. However, the detector developed in this study is placed on the ground by dropping from the drone. Thus, it does not always face toward the radiation source. Also, since it is a remote measurement system, the user cannot know the angle information between the source and detector. Without the angle information, it is impossible to correct the measured value. The most problematic feature is when the backside of the detector (opposite of the scintillator) faces the radiation source. It was confirmed that the measurement value decreased by approximately 50% when the backside of the detector was facing towards the radiation source. To calibrate the measured value, we need the information that can indicate which part of the detector (front, side, back) faces the source. Therefore, in this study, we installed a small gamma sensor on the backside of the detector to find the direction of the detector. Since this sensor has different measurement specifications from the main sensor in terms of the area, type, efficiency and measurement method, the measured values between the two sensors are different. Therefore, we only extract approximate direction using the variation in the measured value ratio of the two sensors. In this study, to verify the applicability of the detector structure and measurement method, the ratio of measured values that change according to the direction of the source was investigated through MCNP simulation. The radioactive source was Cs-137, and the simulation was performed while moving in a semicircular shape with 15 degree steps from 0 degree to 180 degrees at a distance of 20 cm from the center point of the main sensor. Since the MCNP result indicates the probability of generating a pulse for one photon, this value was calculated based on 88.6 μCi to obtain an actual count. Through the ratio of the count values of the two sensors, it was determined whether the radioactive source was located in the front, side, or back of the probe.

  Recently, the control chart is developed for monitoring processes with normal short production runs by the coefficient of variation(CV) characteristic for a normal distribution. This control chart does not work well in non-normal short production runs.

This paper presents accelerated life tests for Type I censoring data under probabilistic stresses. Probabilistic stress, S, is the random variable for stress influenced by test environments, test equipments, sampling devices and use conditions. The hazard rate, θ is a random variable of environments and a function of probabilistic stress. In detail, it is assumed that the hazard rate is linear function of the stress, the general stress distribution is a gamma distribution and the life distribution for the given hazard rate, θis an exponential distribution. Maximum likelihood estimators of model parameters are obtained, and the mean life in use stress condition is estimated. A hypothetical example is given to show its applicability.

본 연구는 EBT3 필름을 이용하여 감마나이프 퍼펙션 모델의 3차원적인 선량분포 측정하고 기준값과 비 교 분석하여 표준화된 측정방법의 기초로 활용하고자 한다. 2개 종합병원에 설치된 감마나이프 퍼펙션 모 델의 선량 분포를 EBT3 필름을 이용하여 정확도와 정밀도를 평가하였다. 정확도 평가를 위해 4 ㎜ 콜리메터를 사용하여 기계적인 중심축과 선량중심축의 일치도를 측정하였다. A병원 0.098 ㎜, 0.195 ㎜ 이며 B 병원 0.229 ㎜, 0.223 ㎜ 로 허용 오차 0.3 ㎜ 이하로 측정되었다. 정밀도 평가는 4, 8, 16 ㎜ 콜리메터(collimater) 각각의 x, y, z 3차원면 에서의 반치폭(FWHM : Full Width at Half Maximum)을 이미지-제이 프로그램을 이용하여 평가하였다. A 병원은 –0.283∼0.583 ㎜, B 병원은 –0.857∼ 0.810 ㎜로 50%선 ± 1 ㎜ 이하의 기준에 적합하였다. 이미지−제이 프로그램을 이용한 선량 분포 분석의 경우 측정자 간의 오차가 발생 가능함으로 측정점에 대한 명확한 기준을 확립할 필요가 있으며, 감마나이프 방사선 수술이 시행되어지는 고선량 영역에서 사용 가능한 선량영역이 높은 필름을 이용한 치료계획과 실제 치료 조사면의 비교가 필요하다고 생각된다.

Clark 모형의 매개변수 추정방법을 개선하기 위하여, 순간단위도를 gamma 분포형 함수로 가정하여 매개변수를 추정한 후 이를 Clark 모형의 매개변수로 전환하는 간접 추정방법을 제안하였다. 이 방법은 전통적인 Clark 모형의 매개변수 추정방법의 개연적인 부정확성을 개선하는 특징과 더불어 Nash 모형과의 상관성을 파악할 수 있는 장점을 갖고 있다. 제안된 매개변수 추정방법을 위천 유역에 적용한 결과 만족할 수 있는 수준의 추정값을 얻을 수 있었으