자율주행 차량이 상용화됨에 따라 연구에 사용할 수 있는 자율주행 차량의 주행궤적 자료를 제공하고 연구하는 기관이 증가하고 있다. 캘리포니아 자동차관리국은 사고 당시 차량의 거동과 주변 환경을 기록한 자율주행 차량 사고 보고서를 제공한다. Waymo는 라이다, 카메라 등을 통해 수집한 자율주행 차량의 실주행 자료를 제공한다. 본 연구에서는 캘리포 니아 자동차관리국에서 제공하는 자율주행 차량 사고 보고서와 Google Street Map을 이용하여 사고 당시의 도로유형과 도로환경요소 및 사고 당시 상황을 파악하고, 베이지안 네트워크(BN)을 통해 자율주행 차량 사고 영향요인을 파악하였 다. 랜덤 포레스트를 통해 앞에서 파악한 자율주행 차량 사고 영향요인들의 변수 중요도를 추출하고 이를 기반으로 자율 주행 차량 주행 시나리오를 도출하였다. 도출한 자율주행 차량 주행 시나리오와 유사한 상황을 보이는 Waymo Open Dataset의 자율주행 차량 실제 주행궤적을 매칭하여 자율주행 차량 주행 행태 기반 사고 위험도 평가 지표를 도출하였 다. 본 연구의 결과는 앞으로 도로환경요소 및 자율주행 차량 주행궤적에 따른 자율주행 차량 주행 안전성 연구의 기반 이 될 것으로 기대된다.

In stable continental regions, selecting appropriate ground motions for seismic design and dynamic response analysis presents significant challenges. This study evaluates the liquefaction potential of the Nakdonggang delta region, South Korea, by generating synthetic ground motion scenarios and applying a scenario-based liquefaction assessment approach. We utilized a hybrid broadband ground motion simulation method proposed by Graves and Pitarka (2010, 2015) to create bedrock ground motions for three hypothetical earthquakes (Mw 6.2 and 6.0) occurring along the Dongrae and Miryang faults. The generated synthetic ground motions were used as input for onedimensional nonlinear site response analyses, incorporating shear wave velocity profiles derived from surface wave inversion. The simulated ground motions demonstrated higher responses at short periods and relatively weaker responses at long periods compared to the Korean design spectra. This amplification of long-period components was attributed to the dynamic response of deep sedimentary layers, while high-frequency components were generally deamplified due to damping effects in shallow silty layers. Liquefaction susceptibility was assessed using surface ground motions derived from the site response analyses, following the SPT-based simplified method proposed by Idriss and Boulanger (2008). Results indicated high liquefaction potential across most sites for the Dongrae earthquake scenario, while liquefaction was unlikely for all sites under the Miryang-1 scenario. For the Miryang-2 scenario, liquefaction was predicted at some sites. Overall, liquefaction is expected at PGA values of approximately 0.13 g or higher, with sites exhibiting lower shear wave velocities being more vulnerable to liquefaction

최근 지구온난화를 동반한 기후변화가 가속화되고 있으며, RCP 8.5 시나리오에 따르면 21세기 말까지 연평균 기온은 4.8℃ 상승할 것으로 예상된다. 고온 환경은 식물체의 생장, 개화시기, 동화산물 함량에 영향을 미치는 것으로 알려졌다. 본 연구는 고온 환경에서 감자의 생장과 대사산물 변화를 Soil-Plant-Atmosphere-Research (SPAR) 챔버와 온도구배하우스를 사용하여 2018년부터 2020년까지 조사하였다. 그 결과, 고온 조건의 SPAR 챔버 및 온도구배하우스에서 개화시기가 약 5∼9일 빨라지는 것을 확인하였다. 고온 조건은 감자 생장을 촉진하며 동화산물 함량에 영향을 줄 것으로 생각되어 개화 3일 전(Days Before Flowering 3, DBF 3)과 개화 21일 후(Days After Flowering 21, DAF 21)의 엽록소, 카로티노이드, 당 및 전분 함량을 분석하였다. 생육 초기인 DBF 3에 엽록소와 카로티노이드 함량은 감소하였고, 자당 및 전분 함량은 유의하게 증가하였다. 하지만 생육 후기인 DAF 21에는 자당 및 전분 함량이 감소한 것으로 나타났다. 또한 개화와 관련 있는 유전자 발현을 qRT-PCR로 분석한 결과, 고온 조건에서 SP6A, PhyB, SP5G, COL1, COL2 유전자 발현이 증가한 것을 확인하였다. 결과적으로, 고온 환경은 개화 전 감자 잎의 자당 및 전분 함량과 개화 관련 유전자의 발현에 영향을 미쳐 감자의 개화시기를 앞당기는 것으로 판단된다.

기후 변화에 의해 해수면 온도 상승, 태풍의 최고 강도 북상, 태풍 강도 증가가 나타나고 있으며, 미래의 태풍 강도 변화가 더 심화될 것으로 예상하고 있다. 본 논문에서는 기후 변화 시나리오에 의해서 발생할 수 있는 한반도 부근의 태풍 강도를 예측하기 위하여 딥러닝 기반 태풍 강도 예측 모델을 개발하였다. 기후 예측정보를 이용하여 미래 기후 변화 환경장 변화에 따른 태풍의 강도를 예측할 수 있도록 과거 환경장을 학습 자료로 사용하였다. 학습자료는 1980년에서 2022년까지의 태풍 발생 빈도가 높은 6~10월의 기상 및 해양 재분 석 월평균 자료와 Best Track 태풍 241개를 입력자료로 사용하였다. 환경장 변화에 따른 태풍 강도 예측을 위해 자료의 공간적인 특징과 시간적인 특징을 함께 고려하는 딥러닝 모델인 ConvLSTM 기반으로 모델을 개발하였다. 태풍 트랙 시퀀스의 각 이동 경로에 대한 월평균 환경장 자료를 모델에 학습하여 태풍의 중심 기압을 예측하였다. 태풍의 공간적 특성을 반영할 수 있도록 범위를 설정하여 입력자료로 학습하였으며, 5°⨉ 5°의 범위일 때 가장 좋은 결과를 보였다. 몬테카를로 방법을 이용한 민감도 실험을 통해 모델 예측에 가장 큰 영향을 미치는 변수는 SST로 확인되었다.

In South Korea, the replacement of steam generators began with Kori Unit 1 in 1995, and to date, 20 steam generators have been replaced and are currently stored in intermediate storage facilities. In the future, additional decommissioned steam generators may arise due to measures like the extension of the lifespan of nuclear power plants. In Korea, technological development for dismantling steam generators is underway, and there is no track record of actual dismantling. Although the replaced decommissioned steam generators are stored in intermediate facilities, for site recycling purposes, steam generators, which have relatively lower radiation doses compared to reactor heads and other primary equipment, should be prioritized for dismantling. While there are various specifications for steam generators, those dismantled and stored domestically are of the Recirculation Type. They can be classified into three types: the Westinghouse type WH-51 used in Kori Unit 1, the Fra-51B used in Han-ul Units 1 and 2, and the OPR-1000 used in Han-ul Units 3 and 4. The quantity of U-Tubes varies depending on the specification, but the radiation is concentrated in the primary side components, the U-Tube and Chamber. Since the parts related to the secondary side are not contaminated, they can be disposed of independently after classification. To dismantle a steam generator, it is of utmost importance to first create a scenario regarding where and how the dismantling will take place. Through the analysis of the advantages and disadvantages of each scenario, the optimal timing, location, and cutting method for dismantling should be researched. Furthermore, based on those findings, the best scenario should be derived through an analysis of worker radiation exposure and dismantling costs. To achieve this, a 3D simulation software developed by Cyclelife Digital Solutions under the French EDF was utilized to conduct simulations based on different dismantling schedules and methods. As a result, the optimal scenario for dismantling the steam generator was derived.

Nuclear Material Accountancy (NMA) system quantitatively evaluates whether nuclear material is diverted or not. Material balance is evaluated based on nuclear material measurements based on this system and these processes are based on statistical techniques. Therefore, it is possible to evaluate the performance based on modeling and simulation technique from the development stage. In the performance evaluation, several diversion scenarios are established, nuclear material diversion is attempted in a virtual simulation environment according to these scenarios, and the detection probability is evaluated. Therefore, one of the important things is to derive vulnerable diversion scenario in advance. However, in actual facilities, it is not easy to manually derive weak scenario because there are numerous factors that affect detection performance. In this study, reinforcement learning has been applied to automatically derive vulnerable diversion scenarios from virtual NMA system. Reinforcement learning trains agents to take optimal actions in a virtual environment, and based on this, it is possible to develop an agent that attempt to divert nuclear materials according to optimal weak scenario in the NMA system. A somewhat simple NMA system model has been considered to confirm the applicability of reinforcement learning in this study. The simple model performs 10 consecutive material balance evaluations per year and has the characteristic of increasing MUF uncertainty according to balance period. The expected vulnerable diversion scenario is a case where the amount of diverted nuclear material increases in proportion to the size of the MUF uncertainty, and total amount of diverted nuclear material was assumed to be 8 kg, which corresponds to one significant quantity of plutonium. Virtual NMA system model (environment) and a divertor (agent) attempting to divert nuclear material were modeled to apply reinforcement learning. The agent is designed to receive a negative reward if an action attempting to divert is detected by the NMA system. Reinforcement learning automatically trains the agent to receive the maximum reward, and through this, the weakest diversion scenario can be derived. As a result of the study, it was confirmed that the agent was trained to attempt to divert nuclear material in a direction with a low detection probability in this system model. Through these results, it is found that it was possible to sufficiently derive weak scenarios based on reinforcement learning. This technique considered in this study can suggest methods to derive and supplement weak diversion scenarios in NMA system in advance. However, in order to apply this technology smoothly, there are still issues to be solved, and further research will be needed in the future.

In order to solve the rapidly increasing domestic delivery volume and various problems in the recent metropolitan area, domestic researchers are conducting research on the development of “Urban Logistics System Using Underground Space” using existing urban railway facilities in the city. Safety analysis and scenario analysis should be performed for the safe system design of the new concept logistics system, but the scenario analysis techniques performed in previous studies so far do not have standards and are defined differently depending on the domain, subject, or purpose. In addition, it is necessary to improve the difficulty of clearly defining the control structure and the omission of UCA in the existing STPA safety analysis. In this study, an improved scenario table is proposed for the AGV horizontal transport device, which is a key equipment of an urban logistics system using underground space, and a process model is proposed by linking systematic STPA safety analysis and scenario analysis, and UCA and Control Structure Guidelines are provided to create a safety analysis.

This study introduces the Three-Memory-Model (Cherry, 2019) in education into Maritime Simulator- based training in Sri Lanka and conducts empirical research. In simulator-based education what is disseminated as knowledge during the Briefing, Scenario and Debriefing phases must be transferred from short-term, across working memory to long-term-memory. Working memory gained during the scenario phase, could be encoded into long-term-memory through rehearsal probes. But the number of probes which could be tolerated by the participants of simulator-based training has not undergone empirical investigation. Thus, selecting the Open Sea scenario phase as its setting the research questions aim to identify tolerance limits in the participants for the number of freezes and the number of probes introduced during each freeze. The methodology selects a population of seafarers (n = 60). Through stratified random sampling this population was subdivided based on experience at sea as Group A (n = 30): Mean of 2 years and Group B (n = 30): Mean of 13.6 years of sea experience. The duration of the open sea scenario phase is 35 minutes with freezes at 10-minute intervals. The number of probes were given a range of 7 to. Data analysis utilized SPSS. The highest percentage mean value was obtained for three freezes for the Open Sea scenario phase while two freezes had the next highest percentage mean value. The mean value of the tolerance limits for questions during one freeze is approximately 9 and 6 probes for Group A and B respectively. Citing prior research on working memory, visuo-spatial vs. verbal working memory, reaction time and age this study raises a counter argument against the findings: the self-declared tolerance limits of the number of questions the participants feel comfortable to answer during each freeze. The findings of this research are valuable to maritime Simulator-based instructional designers outside and within Sri Lanka.

고령운전자의 인지 및 신체 기능 저하로 인한 교통사고 증가는 점점 심각한 사회적 문제가 되고 있으며, 이 로 인해 면허증을 자진하여 반납하는 제도가 운영되고 있으나 보다 객관적인 평가 방법이 필요하다. 본 연 구에서는 다양한 주행 상황에서 발생하는 정적 및 동적 시각 자극에 대하여 운전자의 시각적 행동을 평가 할 수 있는 운전 시뮬레이션 주행 환경과 시나리오를 구현하는 것이 목표이다. 이를 위해 고령운전자의 주 행 특성 분석에 활용된 운전 시뮬레이션 시나리오를 기존의 연구 문헌들로부터 수집하였고, 정적 및 동적으 로 구분된 표적 자극에 따른 텍스트 네트워크 분석을 통해 주행환경과 시나리오를 재분류하였다. 또한, 유 사한 유형의 시나리오들은 보다 발생 빈도가 높고 표적 자극이 다양한 주행환경으로 병합하였다. 연구 결과 로 신호교차로, 비신호 교차로, 왕복 2차로, 왕복 4차로 주행환경에서의 전체 12종의 시나리오로 구성된 운 전 시뮬레이션 콘텐츠가 구현되었다. 신호등, 정차된 차량, 표지판 등으로 구성된 정적 시각 자극과 주행 중 인 차량, 무단 횡단하는 보행자 등의 동적인 시각 자극이 제시되며, 이에 대한 시선 탐색(Visual Detection), 시지각(Visual Perception), 시각운동기능(Visuomotor Function)의 정량적 측정을 통해 운전자의 시각 행동 평가가 이루어진다. 본 연구에서 제안하는 시나리오 구성 방법은 운전 시뮬레이션 콘텐츠를 구현하기 위한 새로운 접근 방식으로써 시각 자극에 따른 운전 능력 검사 환경 구축에 관한 기초 자료로 활용될 수 있을 것으로 기대된다.

Purpose: This study aims to develop a simulation module equipped with scenario-based core nursing skills and test the effects after applying the simulation education based on a developed scenario. Method: This was a nonequivalent control group pre-/posttest design study, and 114 nursing students participated from April 1 to August 30, 2018. The applied scenario-based core nursing skills simulation module was developed in the order of planning, development, application, and evaluation according to the Dick and Carye Model’s program development process. Knowledge, self-efficacy, stress, and nursing practice were measured before and after intervention in two groups: an experimental group that performed a simulation after applying the scenario-based core nursing skills, and a control group that performed a simulation after applying core nursing skills. Results: Knowledge (F=23.19, p<.001), self-efficacy (F=25.83, p<.001), and nursing practice (t=9.51 p<.001) increased in the group that performed a simulation after applying the scenario-based core nursing skills, whereas stress (F=40.41, p<.001) decreased. Conclusion: Various education methods should be applied to increase the education effect of the simulation, Simulation performance can be used as an alternative to improve nursing practice during simulation education.

Purpose: This study was conducted to explore Virtual Reality (VR) utilization strategies in scenario-based nursing simulation training. Method: This was an integrative review for the identification of scenario-based VR simulation training applied to nursing undergraduates. The existing literature was searched in electronic databases using RISS, PubMed, and Pro-Quest and the key words were “Scenario based,” “Simulation,” “Virtual reality,” “Virtual training,” and “Nursing.” Finally, five studies were analyzed. Results: All the studies were conducted from 2016 to 2019. One RCT, two quasi-experimental studies, and two mixed method studies were identified. The topics of the scenarios were all different; acute myocardial infarction, management of respiratory system disease with hypoxia, postoperative nursing with appendicitis, teamwork and communication in outpatient and emergency situations, and disaster situation training . The outcome variables that were significant statistically were performance, self-confidence, and learning satisfaction. Conclusion: The findings suggest that virtual simulation in nursing education can potentially improve knowledge, performance, and learning confidence and can increase satisfaction with learning experience among nursing undergraduates. Multidisciplinary cooperation and investment are needed to develop diverse content applying VR in nursing simulation education. The review of the side effects also needs to be performed.

The paper presents the damage estimation of bridge structures in Daegu city based on the scenario-based earthquakes. Since the fragility curves for domestic bridge strucures are limited, the Hazus methodology is employed to derive the fragility curves and estimate the damage. A total of four earthuquake scenarios near Daegu city are assumed and structure damage is investigated for 81 bridge structures. The seismic fragility function and damage level of each bridge had adopted from the analytical method in HAZUS and then, the damage probability using seismic fragility function for each bridge was evaluated. It was concluded that the seismic damage to bridges was higher when the magnitude of the earthquake was large or nearer to the epicenter.