APro, a modularized framework of the process-based total system performance assessment, has been developed by KAERI to simulate the radionuclide transport in geological disposal system considering multi-physics phenomena. However, the target problem including more than 10,000 boreholes and over 100,000 years of simulation time is computationally challenging to deal with numerical solvers provided by COMSOL Multiphysics constituting APro. To alleviate the computational burden, machine learning (ML) techniques have been studied to develop a surrogate model replacing the heavy computation part. In recent studies, attempts have been made to integrate the knowledge of physics and numerical methods into the ML model for partial differential equations (PDEs). Unlike conventional ML approaches solely relying on data-driven method, the integration can help to make the ML model more specialized for solving PDEs. The hybrid neural network (NN) solver method is one of the strategies to develop more efficient PDE solver by interleaving NN with numerical solvers like finite element method (FEM). The hybrid NN model on the premise of numerical solver is easier to train and more stable than the purely data-driven model. For example, one previous study has used the hybrid NN model as a corrector for an incomplete numerical solver for the advection-diffusion problem. In every time step of simulation, NN corrects the error of incomplete solution obtained by a relaxed numerical solver with coarse meshing. The simulation in the next time step starts from the corrected solution, so NN interacts with the numerical solver iteratively. If the corrector is successfully trained, the incomplete but fast solver with corrector can provide reliable results comparable to the original massive solver. This study adopts the hybrid concept to develop a surrogate model for the near-field region, which is the heavy computation part in the simulation of geological disposal system. Various incomplete models such as coarse meshing or emptying the borehole domain are studied to construct a hybrid NN solver. This study also covers how to embed the hybrid NN in COMSOL Multiphysics to train and use it during the simulation.

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.

In this study, the multi-lane detection problem is expressed as a CNN-based regression problem, and the lane boundary coordinates are selected as outputs. In addition, we described lanes as fifth-order polynomials and distinguished the ego lane and the side lanes so that we could make the prediction lanes accurately. By eliminating the network branch arrangement and the lane boundary coordinate vector outside the image proposed by Chougule’s method, it was possible to eradicate meaningless data learning in CNN and increase the fast training and performance speed. And we confirmed that the average prediction error was small in the performance evaluation even though the proposed method compared with Chougule’s method under harsher conditions. In addition, even in a specific image with many errors, the predicted lanes did not deviate significantly, meaningful results were derived, and we confirmed robust performance.

The use of heat exchangers in various applications such as chemical, air conditioning systems, fuel processing, and power industries is increasing. In order to improve the performance of the heat exchanger, the problem of bonding quality of the copper tube, which is a major member, is emerging. However, since the copper tube is in the form of a pipe, it is difficult to identify internal defects with external factors. In this study, a thermal imaging camera was used to develop and verify an algorithm for detecting defects in the brazing part, and in the process, the brazing performance characteristics were analyzed according to the electrode position, and finally, a learning model was developed and performance evaluation was performed. It was confirmed that the method of supplying heat to the base material and melting the filler metal through the heat transfer effect is more effective than supplying heat input to the filler metal in the bonding process of copper tubes through high-frequency induction heating brazing. Thermal image data was used to develop a defect discrimination model, and 80% of training data and 20% of test data were selected, and a neural network-based single-layer copper tube brazing defect discrimination model was developed through k-Flod cross-validation., the prediction accuracy of 95.2% was confirmed as a result of the error matrix analysis.

이 연구는 전염병의 잠재적 확산 가능성이 높은 지역의 탐색을 목적으로, 코로나19 전후의 버스 네트워크 클러스터의 시공간적 변화를 분석한다. 분석방법으로는 Getis와 Ord의  통계를 공간 네트워크로 확장 및 적용한  통계 값을 사용하였다. 이 과정은 서울시 전체 버스 네트워크의 개별 흐름에 대해 각각 적용되기 때문에 대규모 연산을 위해 병렬컴퓨팅 방식을 적용한 슈퍼컴퓨터를 사용하였다. 연구 결과, 첫째, 코로나19 이후 버스 네트워크가 일부 흐름으로 집중된 경향을 보였다. 둘째, 코로나19이 후의 버스 흐름은 주거지, 농업지로의 이동은 증가하고 상업지역, 교통지역으로의 이동은 감소했음을 확인하였다. 셋째, 중심업무 지구 중 여의도 방면의 클러스터, 구로디지털단지역 방면의 클러스터와 달리, 강남일대는 코로나19 전후의 유의미한 변화가 나타나 지 않았다. 이 연구는 국내에서 처음으로 코로나19전후의 버스 네트워크 클러스터를 확인하고 변화 특징을 제시한다는 의미가 있다.

본 연구의 목적은 국내 메타버스 관련 105편의 학술논문들을 대상으로 내용분석과 키워드 네트워크 분석 을 통해 연구동향을 파악하고 미래 교육에 대한 방향성과 시사점을 제안하기 위한 것이다. 연구절차는 􍾧자료 수집􍾨, 􍾧내용분석􍾨, 􍾧키워드 네트워크 분석􍾨 단계로 진행하였다. 분석결과 주요내용은 다음과 같다. 첫째, 학술 지에 게재된 논문들의 분포도는 디자인, 콘텐츠, 미디어 등을 주제로 하는 예술분야에서는 높았으나 교육분 야에서는 낮았다. 둘째, 연구유형은 질적연구, 이론적 연구, 양적연구, 실천적 연구, 혼합연구의 순으로 많게 나타났다. 셋째, 사회과학 분야에서 메타버스와 관련된 연구가 가장 활발하게 이루어지고 있었다. 넷째, 키워 드 네트워크 분석 결과, metaverse, VR, virtual world, COVID-19, AR, immersion, storytelling이 빈도 분석과 연결중심성 분석에서 모두 높은 결과를 나타내어 최근 메타버스가 주요 연구주제임을 확인하였다. 마 지막으로 digital human, ZEPETO, motion capture는 연결중심성에 비해 매개중심성에서 상대적으로 높은 결과를 나타내어 향후 연구 주제에 대한 잠재적 확장 가능성을 확인할 수 있었다. 위의 연구 결과를 통해 향후 교육분야에서 활용할 수 있는 빅데이터의 축적 및 교육 효과성을 입증하기 위한 다양한 실험들이 요구됨을 알 수 있다. 또한, 학습자의 체험적 경험을 통한 몰입감을 효과적으로 전달 할 수 있는 학습 컨텐츠 설계 전략에 대한 연구 확대에 대한 필요성이 도출되었다. 메타버스의 교육적 적용 을 위해 교육학과 다른 학제 간 융합연구는 필수적이며, 다양한 메타버스 플랫폼에 대한 사례연구를 통해 미 래교육을 위한 전략과 방법이 제시되어야 할 것이다. 마지막으로 교육현장에서의 메타버스 기술에 대한 이해 및 활용 격차를 완화하기 위해 교수자들을 대상으로 지속적인 사용자 교육이 요구된다고 제안하였다.

인터넷은 전통적인 미디어를 대체하고 주요 뉴스 미디어 플랫폼 중 하나가 되었습니다. 인터넷 소스의 뉴스는 접근성이 좋고 편리하기 때문에 기존 뉴스 소스에 비해 빠르고 간단하게 이동할 수 있습니다. 그러나 가짜 뉴 스가 대량으로 발생하고 정치적, 상업적 이유로 온라인 커뮤니티에 퍼지면서 확인되지 않은 소식통으로부터 입수한 모든 언론 보도가 진짜인 것은 아니다. 가짜 뉴스는 이론적으로나 의도적으로 독자들을 속이거나 잘못 알릴 수 있다. 왜냐하면 사람들은 오프라인 커뮤니티에 영향을 미칠 수 있는 어떤 정보에도 쉽게 얽히게 되기 때문이다. 일부 수동 웹사이트는 정보가 사실인지 확인하도록 설계되어 있지만 온라인, 특히 웹에서 빠르게 확 산되는 정보의 양은 확장되지 않습니다. 이 문제를 해결하기 위해 자동 팩트체크 어플리케이션은 확장성과 자 동화의 요건에 대응하도록 설계되었습니다. 그러나 현재 애플리케이션 방법에는 기계 학습 분류 모델 성능을 개선하기 위해 가짜 뉴스 특징을 식별하는 포괄적인 다차원 데이터 세트가 없다. 이 문제를 해결하기 위해 본 연구논문에서는 사용자가 기사의 제목을 입력하면 데이터를 분류하는 Formb 챗봇을 제안했다. 이 연구 작업 에서 데이터 집합의 분류는 반복 신경망(RNN)과 장기 단기 기억(LSTM) 모델을 사용하여 수행되었다. 가짜 및 실제 뉴스 데이터 세트는 사전 처리되어 모델을 교육하는 데 사용됩니다. 저장된 모델은 지정된 입력 텍스트 의 신뢰성을 확인하기 위해 불일치 서버에 배포됩니다. Disconsid API는 python 파일을 chatbot으로 실행할 수 있는 액세스를 제공합니다. 분석 측면에서, 제안된 모델은 96.77%의 정확도로 CNN와 같은 기존 뉴럴 네트 워크 모델을 능가한다.

The global economy has been hit hard by the COVID-19 pandemic, and countries around the world urgently need to restore economic growth. As countries in Eurasia become more and more closely connected, it is inevitable for China's Belt and Road Initiative and South Korea's new Northern policy to connect. In this context, what matters is the connection of the railway network. The purpose of this study is to analyze the impact of the integration of the Korean Terminal Railway (TKR) into the Eurasian railway network by connecting the Korean Terminal Railway (TKR) with China's China-Europe freight trains. The results show that there is an interactive positive correlation between railway freight volume and GDP, and South Korea is expected to become the logistics center of East Asia and improve its status and influence in the world center. Within the Korean peninsula, the railway connection can draw North Korea into a larger multilateral agreement system, and South Korea can use external forces within the system to push North Korea to seek more economic cooperation and actively promote the reunification process. The economic development of Northeast Asia also plays a positive role in the economic recovery of the three provinces in northeast China.

정보통신기술의 급격한 발전에 따라 우리는 디지털 기술사용역량, 정보관리능력, 커뮤니케이션, 컴퓨터 및 매체의 사용능력을 함양해야 하는 디지털 환경에 처했다. 본 논문에서는 현재 중국의 디지털 리터러시에 대한 사회적 인식을 토대로 미래의 트렌트를 찾고자 관련 빅데이터를 수집하여 분석하였다. 이에 따라 실증분석을 위해 Textom을 이용, 2015년~2020년까지의 중국 인터넷 채널에서 디지털 리터러시를 주 제어로 자료를 수집하였으며, 텍스트 마이닝을(빈도분석, TF-IDF분석, 연결중심성분 석) 진행하고, 네트워크 분석(CONCOR)을 수행하였다. 분석결과는 첫째, 중국의 디 지털 리터러시가 정부의 선도를 바탕으로 안정적으로 추진되고 있음을 알 수 있었 다. 둘째, 국가교육 관점에서 디지털 리터러시의 접근역량, 비판적 이해 역량, 사회적 참여 역량 등에 관한 교육과정을 구축할 필요가 있음을 확인하였다.

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.

범밀도이론함수(Density Functional Theory, DFT) 기반의 제일원리전산모사는 기저상태의 DFT 에너지를 구하는데 많은 시간소요 및 전산자원을 소모하였다. 이러한 막대한 전산자원의 소모는 DFT 계산에서 고려할 원자수를 수 백개 이 하로 제한되게 되었으며, 이를 해결하기 위해서는 전자구조 계산이 아닌 원자의 환경 내에 원자간 상호작용을 정의 (Force Field, 힘장)하고 이를 통해 주어진 조성 혹은 구조에 따른 에너지를 빠르게 예측 할 수 있어야 한다. 본 논문에서 는 Behler-Parrinello가 제시한 인공신경망 모델을 활용해 인공지능 다원계 힘장을 개발하고 코발트-구리 산화물의 조성에 따른 에너지를 예측하고 안정한 구조를 탐색하는 연구를 수행하였다. 인공신경망 기술로 부터 구리-코발트 산화물에 대 해 15.7 meV/atom의 에너지 오차와 단위거리당 힘 103.6 meV/Å의 정확도를 가지는 인공신경망 포텐셜을 개발하였다. 이 방법으로 빠르고 정확하게 CuCoO 표면구조의 산소 결함률에 따른 생성에너지를 계산할 수 있었고, 에너지 컨벡스 홀을 도시 조성에 따른 안정한 구조를 예측하였다.

This study attempted to reinterpret the meaning of the Roh MooHyun Jeju-Declaration in accordance with the age of artificial intelligence based on the liberal theory. In the era of artificial intelligence, a new aspect of the U.S.-China hegemonic competition was considered. The Roh MooHyun Declaration, which declared Jeju Island a peaceful island, was reinterpreted from three points of view of liberal theory. This study presented the necessity and orientation of the World Peace Island Network International Organization.

선박과 교각이 충돌하면 생명과 안전에 큰 위협이 될 수 있다. 따라서 선박-교각 충돌력 영향 인자를 식별하고 다양한 충돌 조 건에서의 충돌력에 대한 연구의 필요성이 있다. 본 논문에서는 선박-교각 충돌의 유한요소 모델을 설정하고, 수치 시뮬레이션을 통해 선 적상태, 운항속도, 충돌 각도의 세 가지 입력조건을 조합하여 50가지 케이스에서의 선박-교각 최대 충돌력을 계산하였다. 계산된 유한요 소해석 결과를 사용하여 신경망 추정 모델을 학습하고 최대 충돌력을 추정함으로써 빠른 시간에 최대 충돌력을 추정하는 프로세스를 제 안하였다. 신경망 예측 모델은 가장 기초적인 역전파 신경망과 시간정보를 고려할 수 있는 순환신경망인 Elman 신경망 2가지 모델을 사 용하였다. 10가지 케이스의 테스트 데이터로 시험한 결과 Elman 신경망을 사용했을 경우에 평균상대오차가 4.566%로 역전파 신경망보다 나은 최대 충돌력 추정이 가능함을 확인하였고 8가지 케이스에서 5%이하의 상대오차를 보여 주었다. 본 신경망을 이용한 최대 충돌력 추 정법은 유한요소해석을 수행하지 않아도 되므로 계산 시간이 짧아 선박 항해 중 충돌을 회피할 수 없는 경우 피해를 최소화하는 의사결 정의 기초 방법으로 사용할 수 있다.

선박으로부터 발생하는 배출가스에 대한 규제가 강화되고 이를 해결하기위한 대안으로 전기추진시스템의적용이 대형상선에서 부터 중ㆍ소형선박에 이르기까지 그 사용이 증가되고있다. 전기추진시스템의 효율 향상을 위한 방법으로 발전원의 개선, 배터리ㆍ연료전 지ㆍ태양광 등의 친환경 발전원의 시스템 연계 및 정류기, 전력변환장치, 추진전동기의 개발과 제어방식의 연구를 들 수 있다. 그 중 정류 방식에 있어 상천이변압기과 다이오드를 이용하는 방식이 널리 사용되었으나, 직류배전을 이용한 친환경발전원의 계통 연계, 가변속 발 전원의 사용, 중ㆍ소형 전기추진시스템의 적용을 통해 전력용 반도체 소자를 이용한 AFE정류기에 대한 수요가 증대되고 있다. 이러한 AFE 정류기를 제어하는 방식에 있어 기존의 비례적분제어기가 아닌 신경회로망을 이용한 방식을 본 연구에서는 제안하였다. 기존의 제 어기 데이터를 활용하여 Matlab/Simulink를 통해 학습한 신경회로망제어기를 설계하고 PSIM을 통해 설계된 정류시스템에 신경회로망 제어 기를 적용하여 부하변동에 따른 직류출력단의 파형과 역률 개선의 유효성을 확인하였다. 이는 공간이 협소한 중소형 친환경 선박의 정류 시스템으로써 적용이 가능하다.

This study aimed to analyze causality of climatic factors that affecting the yield of whole crop barley (WCB) by constructing a network within the natural ecosystem via the structural equation model. The WCB dataset (n=316) consisted of data on the forage information and climatic information. The forage information was collected from numerous experimental reports from New Cultivars of Winter Crops (1993-2012) and included details of fresh and dry matter yield, and the year and location of cultivation. The climatic information included details of the daily mean temperature, precipitation, and sunshine duration from the weather information system of the Korea Meteorological Administration. The variables were growing days, accumulated temperature, precipitation, and sunshine duration in the season for the period of seeding to harvesting. The data was collected over 3 consecutive seasons—autumn, winter, and the following spring. We created a causality network depicting the effect of climatic factors on production by structural equation modeling. The results highlight: (i) the differences in the longitudinal effects between autumn and next spring, (ii) the factors that directly affect WCB production, and (iii) the indirect effects by certain factors, via two or more paths. For instance, the indirect effect of precipitation on WCB production in the following spring season via its effect on temperature was remarkable. Based on absolute values, the importance of WCB production in decreasing order was: the following spring temperature (0.45), autumn temperature (0.35), wintering (-0.16), and following spring precipitation (0.04). Therefore, we conclude that other climatic factors indirectly affect production through the final pathway, temperature and growing days in the next spring, in the climate-production network for WCB including temperature, growing days, precipitation and sunshine duration.

Ms. Hee Choon Oh was a Jeju Haenyeo(woman diver) and one of the few surviving victims of the Jeju 4.3 (1948). As a Haenyeo: Jeju society was very poor at the time during the Japanese colonial period and the division of the two Korea, especially after the Korean War, so she had to work regardless of gender. It was hard to go into the sea, but it was the only thing to support my family. In retrospect, Going out to work in the sea was like a life-threatening adventure every day. While there were many emergency moments, She becomes one with the sea and forgot all the pain. After having spent a lot of time together with the sea, I had to stop working as a Haenyeo because it was hard to take care of nine children after having heart surgery. I was able to receive Honorary Haenyeo through continuous exchanges and dedication to the society. As a victim of the Jeju 4.3: I had no choice but to explain it why I had to do work as a Haenyeo and to understand my life. I am a woman who chose the job of Haenyeo, but I have lived through the pain of the dark modern and contemporary history of Korea. The unfair one-year prison life that I wanted to hide even from my children hurt me all my life. Over the past 70 years, sharing pain with the sea, neighbors, and family, serving the region, and hopefully waiting for a better society and justice to come. and finally It was not until 2019, 70 years after 1948, that I was officially acquitted by a Korean court. I regained my honor as a Haenyeo and was able to get rid of my deep sadness.

The water supply facilities of Korea have achieved a rapid growth, along with the other social infrastructures consisting a city, due to the phenomenon of urbanization according to economic development. Meanwhile, the level of water supply service demanded by consumer is also steadily getting higher in keeping with economic growth. However, as an adverse effect of rapid growth, the quantity of aged water supply pipes are increasing rapidly, Bursts caused by pipe aging brought about an enormous economic loss of about 6,161 billion won as of 2019. These problems are not only worsening water supply management, also increasing the regional gap in water supply services. The purpose of this study is to classify hazard evaluation indicators and to rank the water distribution network hazard by cluster using the TOPSIS method. In conclusion, in this study, the entropy-based multi-criteria decision-making methods was applied to rank the hazard management of the water distribution network, and the hazard management ranking for each cluster according to the water supply conditions of the county-level municipalities was determined according to the evaluation indicators of water outage, water leakage, and pipe aging. As such, the hazard ranking method proposed in this study can consider various factors that can impede the tap water supply service in the water distribution network from a macroscopic point of view, and it can be reflected in evaluating the degree of hazard management of the water distribution network from a preventive point of view. Also, it can be utilized in the implementation of the maintenance plan and water distribution network management project considering the equity of water supply service and the stability of service supply.