원자력발전소 지진 확률론적 안전성 평가인 PSA(Probabilistic Safety Assessment)는 오랜 기간에 걸쳐 확고히 구축되어 왔다. 반면 에 다양한 공정 기반의 산업시설물의 경우 화재, 폭발, 확산(유출) 재난에 대해 주로 연구되어 왔으며, 지진에 대해서는 상대적으로 연 구가 미미하였다. 하지만, 플랜트 설계 당시와 달리 해당 부지가 지진 영향권에 들어갈 경우 지진 PSA 수행은 필수적이다. 지진 PSA 를 수행하기 위해서는 확률론적 지진 재해도 해석(Probabilistic Seismic Hazard Analysis), 사건수목 해석(Event Tree Analysis), 고장수 목 해석(Fault Tree Analysis), 취약도 곡선 등을 필요로 한다. 원자력 발전소의 경우 노심 손상 방지라는 최우선 목표에 따라 많은 사고 시나리오 분석을 통해 사건수목이 구축되었지만, 산업시설물의 경우 공정의 다양성과 최우선 손상 방지 핵심설비의 부재로 인해 일 반적인 사건수목 구축이 어렵다. 따라서, 본 연구에서는 산업시설물 지진 PSA를 수행하기 위해 고장수목을 바탕으로 확률론적 시각 도구인 베이지안 네트워크(Bayesian Network, BN)로 변환하여 리스크를 평가하는 방법을 제안한다. 제안된 방법을 이용하여 임의로 생성된 가스플랜트 Plot Plan에 대해 최종 BN을 구축하고, 다양한 사건 경우에 대한 효용성있는 의사결정과정을 보임으로써 그 우수 성을 확인하였다.

Concerns about North Korea’s 7th nuclear test have been rising recently, and it is a significant threat to the situation around the Korean Peninsula. Amidst these threats, the Korean government also shows a strong will for denuclearizing the Korean Peninsula, referring to the “Audacious Initiative.” For denuclearization negotiations with North Korea, it is essential first to understand North Korea’s nuclear capabilities. However, since access to information is complicated and contains many uncertainties, many studies have been conducted to estimate it. Among them, Von Hippel surveyed to estimate the total amount of uranium ore based on information on uranium mining, which is relatively widely known throughout North Korea’s nuclear fuel cycle, and the amounts of HEU and Pu suggested by many experts. KINAC has conducted a study on a methodology that can narrow the estimation range and improve reliability through the Bayesian Network based on Von Hippel’s research results. However, in this study, the probability distribution is assumed to be the simplest form of uniform distribution, and the estimation formula for the amount of Pu produced compared to the amount of uranium loaded in the core is used as it is, which is an error in Von Hippel’s study. Improvement is needed. This study proposes a more reliable BN model by supplementing this and attempts to estimate the amount of uranium ore that North Korea produces or possesses. Of course, the data used as the basic structure of the model is insufficient, and the estimation formula is straightforward, so it is somewhat unreliable to trust the estimate for uranium ore. However, it is expected to be a suitable methodology that can narrow the scope of North Korea’s nuclear material production estimate or compensate for the uncertainty of the nuclear material production estimation model being developed at KINAC.

Efforts for nuclear non-proliferation have continued since the development of nuclear weapons and the conclusion of the NPT Treaty. Nuclear proliferation requires materials, facilities, and human resources to make nuclear weapons, and it takes a medium to long-term time. There are many restrictions in the current system to obtain nuclear materials and facilities, so it is often done through illegal means, black markets, or confidential transactions. Methods have been developed to evaluate the nuclear non-proliferation regime to strengthen the non-proliferation and solve the problems. The IAEA and the United States DOE initiated the proliferation resistance evaluation in 1980. The DOE conducted the assessment in three main evaluation categories: materials, technical characteristics of facilities, and institutional barriers. In another nuclear non-proliferation evaluation study, some researchers evaluated three main types: current capacity, political situation, and international situation. Detailed indicators include economic capacity, industrial capacity, nuclear capacity, leader’s intentions, political structure, competitive relations, alliances, and international norms. Most of these evaluations are based on the situation at the time of assessment at the national level. Historical examples of nuclear proliferation are rare, and verification is also challenging. The Bayesian probability is widely used when the data is small, experiments are impossible, and the causal relationship is unclear. A Bayesian network is a combination of Bayesian probability and graphics. It is used throughout the industry because it can easily derive results according to causal relationships and weights of various variables, evaluate the risk for decision-making, and obtain changed results through data updates. In particular, to evaluate the proliferation of nuclear weapons, Freeman developed the Freeman network in 2008 and the Freeman-Mella network in 2014. Freeman explained in detail only the process of deriving variables, correlations, and probabilities of factors related to factors such as motivation, intention, and resources. It isn’t easy to view as an objective result value because it does not describe the academic background for path selection, motivation list, intention, and resource variable selection. However, the research was meaningful because he first used the Bayesian network for nuclear proliferation. Although some studies have been done at the macro level, there is no case of applying it in export controls, which is the beginning of the actual spread. Also, there is no quantitative value for factors for risk assessment. There is little data, and verification of causality is difficult, so if the Bayesian network is applied to export control and applied to actual implementation, it will help make decisions such as export license or export denial.

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

This paper is intended to assess a dynamic system reliability. Bayesian networks, however, have difficulties in their application for assessing the system reliability especially when the system consists of dependent components and the probability of failu

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.

댐 위험도 해석시 수문학적 변량(강수, 유출 및 수위)들의 상호관계를 고려한 체계적인 분석과정이 요구된다. 그러나 기존 댐 위험도 해석 연구에서는 변량간의 체계적인 관계 평가를 수행하는데 있어서 한계점을 나타내고 있다. 이러한 점에서, 본 연구에서는 수리·수문학적 변량간의 관계를 효과적으로 평가하고자 Bayesian Network 기반의 댐 위험도 해석 기법을 개발하였다. 실제 댐에 대해서 제안된 모형을 적용한 결과 파괴인자간의 상호관계 규명 및 불확실성을 평가하는데 있어서 기존 연구보다 쉽게 가장 큰 파괴인자를 파악할 수 있는 장점이 있었다. 이와 더불어 다양한 시나리오에 따른 댐의 안정성을 파괴확률 및 예상피해의 함수인 위험도로 평가할 수 있도록 하였다. 즉, 기존 댐 위험도 기법으로 수행한 결과에서는 월류 확률이 도출 되지 않았지만, Copula 함수를 도입하여 댐 초기수위를 고려한 결과 댐 월류 확률이 발생하였 으며, 피해결과 역시 크게 증가하고 있는 것을 확인할 수 있었다. 이러한 결과를 기반으로 향후 댐의 보수보강 등의 우선순위 결정을 위한 도구로서 활용이 가능할 것으로 판단된다.

기후변동성의 증가로 인한 댐의 안정성 확보가 주요 이슈로 부각되고 있으며 세계대댐회(ICOLD)에서는 이러한 점을 고려한 댐의 안정성 평가를 위해 위험도 해석 기반의 해석 절차를 추진하고 있다. 그러나 국내에서는 기존 댐 안정성 평가의 경우 치수적 관점에서 빈도해석기법과 정성적 평가 기반의 상태평가에 중점을 두고 있으며 정략적이며 구체적인 해석 방안에 대한 연구는 미비한 실정이다. 댐의 위험도 분석을 위해서는 구조물 자체적인 문제, 기상조건, 환경적인 요인 등 내부·외부 요인들에 대한 충분한 검토가 필수적으로 요구되며 고려된 요소들 간의 상호작용을 평가할 수 있는 Network 기반의 해석체계 구축이 필요하다. 또한 대부분의 위험도 해석과정에서 요구되는 매개변수들의 불확실성이 매우 크다는 점과 각 매개변수들의 불확실성이 전체 해석결과에 영향을 준다는 점을 고려해야 한다. 이러한 점에서 본 연구에서는 위험도 평가 수행 중 가장 중요한 위험요소들의 분류와 규명, 위험요소들간의 인과관계, 위험요소들의 발생확률 산정 등을 통해서 각각의 위험요소들의 발생경로와 발생확률 등을 체계적으로 추정할 수 있도록 다음과 같이 연구를 진행하였다. 첫째, 댐의 과거 피해사례, 계측 및 외관 조사, 다양한 댐 파괴인자 등을 고려하여 입력자료를 구축한다. 둘째, 도출된 위험 인자들을 토대로 댐 위험도 해석 모형을 개발하기 위해서 주요 파괴경로, 원인 인자와 결과간의 상관성을 고려하여 Bayesian Network 모형을 구축한다. Bayesian Network 노드에는 각 입력자료의 특성에 맞는 확률분포형이 부여되며, 각 노드간 조건부확률의 계산을 통해 댐의 위험도 평가를 수행하였다. 마지막으로 인명피해액 및 경제적인 피해액을 산정하여 댐 파괴시 총 피해액을 산정할 수 있도록 모형을 개발하였다. 본 연구를 통해서 댐 피해의 결과로 보수·보강의 계획 수립시 특정 위험인자에 대해 우선순위를 결정할 수 있으며, 각 댐의 특성에 맞는 효과적인 관리·운영을 할 수 있을 것으로 판단된다.