본 연구는 생물생산시설의 안전구조설계기준 마련을 위한 기초자료를 제공하기 위하여, 우리나라의 60개 지역에 대한 기상자료를 사용하여 설계하중 산정에 필요한 재현기간별 설계풍속 및 설계 적설심을 구하고 등풍속선도 및 등적설심선도를 작성하였으며, 얻어진 결과를 요약하면 다음과 같다. 1. 년최고치 계열의 최대풍속 및 최대적설심 자료에 대한 Type-I 극치분포의 적정 확률분포형 검정을 위해 Chi-Square검정을 실시한 결과 대부분 지역에서 적합성이 인정되었다. 2. 적정 확률분포형으로 설정된 Type-I 극치분포에 의하여 재현기간별 표본 수에 따른 빈도계수를 구하고, 각 지역의 설계풍속 및 설계적설심을 구하였으며 결과는 Table 5와 Table 6, 그리고 Fig.3 및 Fig.4와 같다. 3. 최대풍속의 최빈풍향 분석결과 최대풍속의 약 46.4 %가 일정한 방향인 것으로 나타났으며, 지형이나 환경상의 문제가 없을 경우 최빈풍향을 고려해 주는 시설배치로 구조적 안전성을 향상시킬 수 있을 것으로 생각된다. 4. 우리나라의 풍속 및 적설심을 고려해 볼 때 울릉도 및 영동지방은 시설재배지역으로 매우 불리하며, 충남과 전북 서해안 지역은 적설에, 서남해안 지역은 바람에 강한 구조로 설계하는 것이 바람직하다는 것을 확인할 수 있었다.

This paper is about a fall inducement system for guiding like a real fall. Reliable fall data can be used as an essential element in developing effective fall protection devices. We can get this data if the induced fall is very realistic. The proposed system analyzes gait characteristics and determines when to fall based on the pedestrian's biometric data. To estimate the fall inducement time, an active estimation algorithm was proposed using different biometric values for each pedestrian. The proposed algorithm is designed to response actively to the ratio of gait cycle and a stance period. To verify this system, an experimental environment was implemented using a multi-rail treadmill equipped with a ground reaction force measurement device. An experiment was conducted to induce falls to pedestrians using a fall inducement system. By comparing the experimental scene to the video of the actual fall, it has been confirmed that the proposed system can induce a reliable fall.

In this paper, we introduce a target position reasoning system based on Bayesian network that selects destinations of robots on a map to explore compound disaster environments. Compound disaster accidents have hazardous conditions because of a low visibility and a high temperature. Before firefighters enter the environment, the robots notify information in advance, such as victim’s positions, number of victims, and status of debris of building. The problem of the previous system is that the system requires a target position to operate the robots and the firefighter need to learn how to use the robot. However, selecting the target position is not easy because of the information gap between eyewitness accounts and map coordinates. In addition, learning the technique how to use the robots needs a lot of time and money. The proposed system infers the target area using Bayesian network and selects proper x, y coordinates on the map based on image processing methods of the map. To verify the proposed system, we designed three example scenarios based on eyewetinees testimonies and compared time consumption between human and the system. In addition, we evaluate the system usability by 40 subjects.