This study performed the seismic response analysis of an LNG storage tank supported by a disconnected piled raft foundation (DPRF) with a load transfer platform (LTP). For this purpose, a precise analytical model with simultaneous consideration of Fluid-Structure Interaction (FSI) and Soil-Structure Interaction (SSI) was used. The effect of the LTP characteristics (thickness, stiffness) of the DPRF system on the seismic response of the superstructure (inner and outer tanks) and piles was analyzed. The analytical results were compared with the response of the piled raft foundation (PRF) system. The following conclusions can be drawn from the numerical results: (1) The DPRF system has a smaller bending moment and axial force at the head of the pile than the PRF system, even if the thickness and stiffness of the LTP change; (2) The DPRF system has a slight stiffness of the LTP and the superstructure member force can increase with increasing thickness. This is because as the stiffness of the LTP decreases and the thickness increases, the natural frequency of the LTP becomes closer to the natural frequency of the superstructure, which may affect the response of the superstructure. Therefore, when applying the DPRF system, it is recommended that the sensitivity analysis of the seismic response to the thickness and stiffness of the LTP must be performed.

The decommissioning of domestic Nuclear Power Plants (NPPs) in Korea is expected to begin with the Kori-1, which was permanently shutdown in 2017. In addition, Wolsong-1 has been also permanently shutdown, and another type will be the decommissioning project following Kori-1. KHNP is promoting operation and decommissioning projects as the owner of NPPs, and the Central Research Institute (CRI) has been developing a Final Decommissioning Plan (FDP) for the decommissioning license document. The FDP consists of 11 major chapters in the order of overview of the project, characteristic evaluation, safety assessment, radiation protection, decontamination & dismantlement activities, waste management, etc. The contents described in each chapter are individual chapters, but there are also parts that consider the connection with other chapters. The CRI, which develops the FDP for the first decommissioning project in Korea, has spent a lot of time and effort considering this and has been proceeding through trial and error until the present stage. Therefore, this study aims to explain the current status of FDP, a license document for domestic decommissioning projects, and the link between major input data in major chapters. It can be said that System, Structure, and Components (SSCs) subject to dismantling are considered as the scope of FDP. Chapters that perform estimations on these dismantling targets may include safety assessments, exposure dose assessments for workers and residents, and waste inventory assessments. Therefore, an important part of performing the estimation works is to consider the entire scope of decommissioning activities, and as a way, it can start from data based on the inventory data. After generating the inventory data, the waste treatment classification for the inventory is designated by reflecting the results of the characterization. In addition, for cost estimation, the cost of decommissioning project is predicted by inputting some data (i.e., UCF) such as work process, number of workers, and time required for each item with data reflected in quantity and characterization. After that, based on these inventory, characterization, and UCF data, accident scenarios and industrial safety evaluation are performed for the safety assessment. The worker exposure dose is estimated by considering the dose rate of the workspace with these data. In the case of the amount of waste, the final amount of waste is estimated by considering the factors of reduction and decontamination. In summary, the main estimation contents of FDP are evaluated by adding elements required for the purpose of each chapter from data combined with inventory, characterization, and UCF, so the contents of these chapters are based on the logic of considering the entire scope of decommissioning in common.

The deep geological repository for high-level radioactive waste requires careful consideration due to its exceptionally long-term implications, making long-term impact assessments essential. However, evaluating the long-term effects of deep geological repositories using performance assessment models is accompanied by various sources of uncertainty, including uncertainties about the future, model uncertainties, and uncertainties associated with input data. These multifaceted uncertainties arise from factors such as a lack of current knowledge, contributing to a complex web of unpredictability. Managing, mitigating, and ultimately eliminating these uncertainties is crucial for ensuring the performance and safety of deep geological repositories. Currently, the Korea Radioactive Waste Agency (KORAD) is developing a complex behavior model that incorporates Thermal-Hydraulic-Mechanical-Chemical (THMC) phenomena within the disposal system using PFLOTRAN. To address model uncertainties and furthermore input data uncertainties for this intricate model, an automated sensitivity analysis system has been developed. This automated system operates without human intervention, facilitating tasks such as automatic parameter adjustments and the quantification of uncertainties. Furthermore, this system aids in identifying key factors characterized by substantial uncertainties. Through this system, it is possible to examine concentration distributions in each components of the deep disposal facility in response to changes in input data and to identify factors with significant uncertainties. The sensitivity results and key uncertainty factors obtained through this system are intended to be used for optimizing uncertainties in future research and development.

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier’s abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

This study aimed to investigate the effect of visual input enhancement (VIE) on the comprehension of reading texts and the learning of two grammatical forms: English relative clauses and articles. Individual learners’ working memory (WM) capacity was also tested to explore its impact on the effectiveness of VIE. A total of 48 Korean college learners of English were assigned into three groups: (a) relative group receiving VIE on relative clauses (b) article group receiving VIE on articles, and (c) a control group receiving no VIE. Results showed that VIE did not have any negative effect on the learners’ reading comprehension. Rather, it had positive effects on the learning of the two grammatical forms. According to the findings, VIE on relative clauses enhanced the learners’ receptive knowledge of the grammatical form, whereas VIE on articles enhanced the learners’ productive knowledge of the form. There was a potential link between the effectiveness of VIE and the learners’ working memory processing ability. Pedagogical implications are also discussed based on these findings.

We have observed a phenomenon where the internal X capacitors of the input EMI filter experienced damage during operation. To solve the problem, we have analyzed the malfunction by identifying the characteristics and operating principles of EMI filter. Based on this analysis, we have derived improvement strategies and validated them through experiments. This paper help some people prevent the similar problem when developing the similar equipment and solve the similar problem of the similar equipment.

This study attempts to analyze the economic impact of the service robot industry using Input-Output analysis, which is conducted based on Demand-driven model, the Leontief price model, the Backward and Forward Linkage Effects, and the Exogenous Methods. In a Demand-driven model analysis, we can conclude that the service robot industry contains characteristics of both the manufacturing industry and the service industry, which causes a positive impact on the overall industry by compensating for the weaknesses of the two industries. The Leontief price analysis indicates when wages in the service robot industry increase, prices related to robot manufacturing also increase. Also, when profits in the service robot industry increase, prices related to service provision increase, too. The Backward and Forward Linkage Effects analysis shows that the service robot industry is highly sensitive to the current economic condition and has a great influence on the service industry. The service robot industry can highlight the aspect of service characteristics when the manufacturing industry is in recession and vice versa. In addition, the service robot industry can be regarded as a value-adding and domestic economy promoting industry which utilizes knowledge of information and communication technologies. It is important to foster the service robot industry in South Korea, which is in economic recession to provide an opportunity to stimulate the growth of both service and robot industries.

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

Pyroprocessing is a promising technology for managing spent nuclear fuel. The nuclear material accounting of feed material is a challenging issue in safeguarding pyroprocessing facilities. The input material in pyroprocessing is in a solid-state, unlike the solution state in an input accountability tank used in conventional wet-type reprocessing. To reduce the uncertainty of the input material accounting, a double-stage homogenization process is proposed in considering the process throughput, remote controllability, and remote maintenance of an engineering-scale pyroprocessing facility. This study tests two types of mixing equipment in the proposed double-stage homogenization process using surrogate materials. The expected heterogeneity and accounting uncertainty of Pu are calculated based on the surrogate test results. The heterogeneity of Pu was 0.584% obtained from Pressurized Water Reactor (PWR) spent fuel of 59 WGd/tU when the relative standard deviation of the mass ratio, tested from the surrogate powder, is 1%. The uncertainty of the Pu accounting can be lower than 1% when the uncertainty of the spent fuel mass charged into the first mixers is 2%, and the uncertainty of the first sampling mass is 5%.

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

The purpose of this study is to analyze the structure, status and economic ripple effects of the fisheries processing industry in Korea using interindustry analysis. Five input-output tables published over the past twenty years have been reclassified with a focus on the fisheries processing sector. Through these multi-period tables, we analyzed changes in the inducing effects in production, value added and employment as well as the backward-forward linkage effects. As a result of the analysis, it was found that the industrial scale of the fisheries processing industry is very small compared to other food manufacturing industries. The backward linkage effect of the fisheries processing industry was greater than that of other industries, but the forward linkage effect was rather low. This means that the fisheries processing industry can be greatly affected by industrial depression of the downstream industries such as fishery and aquaculture. Production and employment-inducing effects of the fisheries processing industry have shown a decreasing trend in recent years. This reflects the reality that intermediate inputs are gradually being replaced by imports from domestic production due to the expansion of market opening and the depletion of fishery resource. In the future, it is necessary to prepare a strategy to increase the value-added productivity of the fisheries processing sector and foster it as an export industry.

선박의 주묘 위험성을 평가할 수 있는 프로그램이 개발되어 있지만 선박의 제원에 해당되는 다양한 입력요소들을 직접 찾아서 입력해야 하므로 VTS 관제사가 정박지에 정박 중인 선박들로부터 이러한 입력요소들을 모두 확인하여 프로그램을 활용하는 것은 현실적으로 어려운 상황이다. 이에 본 연구에서는 VTS 관제사 입장에서 선박으로부터 쉽게 획득할 수 있는 총톤수(GT)를 독립변수로 설정하고 프로그램 입력요소들을 종속변수로 하여 선형 및 비선형 회귀분석을 실시하였다. 다항식 모델(선형)과 멱급수 모델(비선형)의 적합도를 비교한 결과, 컨테이너선과 벌크선의 경우에는 모든 입력요소에서 멱수급 모델이 적합한 것으로 평가되었다. 하지만 탱커선의 경우에는 수선간장, 선폭, 흘수는 멱수급 모델이 적합하고, 정면풍압면적, 앵커의 무게, 의장수, 묘쇄공으로부터 선저까지의 높이는 다항식 모델이 더 적합한 것으로 평가되었다. 또한 탱커선의 정면풍압면적 요소를 제외한 다른 나머지 종속변수들은 모두 결정계수가 0.7 이상으로 높은 적합도를 보였다. 따라서 주묘 위험성 평가 프로그램의 입력요소 중 외력 요소, 해저 저질, 수심 및 앵커 체인의 신출량을 제외한 나머지 입력요소들은 선박의 총톤수만 입력하면 회귀분석 모델식에 의해 자동으로 입력됨으로써 주묘 위험성 평가가 가능할 것으로 판단된다.

기존의 확률론적 안전성 평가의 신뢰도 제고를 위하여 잘 알려진 입력 파라미터의 일반적인 분포에 새롭게 측정된 신뢰도 있는 데이터를 결합하여 사후분포를 구할 수 있는 베이지안 업데이팅 방법론을 제안하였다. 마코프체인 몬테 칼로 샘플링 기법의 알고리듬을 통한 GoldSim 모듈도 개발하였다. 복수의 입력 파라미터의 사전분포에 대해 연속적으로 사후분포를 구 해낼 수 있는 베이지안 업데이팅이 가능하도록 개발된 이 모듈을 GoldSim 템플릿 형태의 기존의 GSTSPA 프로그램으로 이행하여 보다 신뢰도 있는 확률론적 방사성폐기물 처분 시스템 안전성 평가가 가능하도록 하였다. 이는 기존에 존재하는 사 전분포의 일반적인 형태는 취하되 새롭게 얻어지는 실제 측정치나 전문가들의 의견을 기존의 분포에 적용하여 보다 더 높은 믿음을 갖는 입력 파라미터의 사후분포를 얻을 수 있게 한다. 균열암반 내 핵종 이동에 관련된 몇 개의 입력 파라미터의 사전분포의 세차례의 연속적 업데이팅을 통해 프로그램의 유용성도 예시하였다. 이 연구를 통하여 처분시스템과 같이 장기적 평가가 필요하고 넓은 모델링 지역을 가지며 측정된 입력자료가 부족한 경우 보다 더 믿음직한 방법으로 안전성 평가를 수행할 수 있는 것을 보였다.

In this analysis, the analytical model was verified through the normal mode analysis of the piston for the 2.9 liter IDI (indirect injection) engine. Heat transfer analysis was carried out by selecting two cases of applied temperature using the validated model. The first case was a condition of 350℃ on the piston upper surface and 100℃ on the piston body and inner wall. In the second case, the conditions were set to give a temperature of 400℃ on the upper surface of the piston and 100℃ on the piston body and the inner wall. In addition, the temperature distribution due to heat transfer was obtained for the pistons with boundary conditions of two cases, and then the thermal stress distribution due to thermal expansion was obtained using the input. Using this analysis result, the thermal stress caused by thermal expansion due to the thermal conduction of the piston is examined and used as the basic data for design.

In an effort to find a way to optimize the learning condition, this study examined the effects of two types of input frequency distribution, skewed-first distribution (SFD) versus balanced distribution (BD), and two types of focus-on-form techniques, input processing (IP) versus input enhancement (IE), on the learning of English participles by Korean middle school students. A total of 91 students participated in this study, divided into five subgroups: SFD + IP, SFD + IE, BD + IP, BD + IE, and Control. The learning outcomes and the generalizability of the learned knowledge were measured through scaled judgment tasks and picture description tasks, immediately after treatment as well as one week later. The results showed that the input frequency distribution factor did not have statistically significant effects on learning and generalizability, while the degrees of explicitness of focus-on-form techniques had statistically significant effects on both. The interaction effects of the two main factors were not statistically significant. More detailed findings are presented with some pedagogical implications for Korean EFL classrooms.

Building Information Modeling (BIM) tools have not only increased the use of technology in the design process, but also increased the need for more information standard systems. The object classification system consists of 327 types of construction results obtained from 25 kinds of facilities, 174 types of parts, and 207 types of construction parts. In the previous study, the property classification system was developed into 4 major classifications, 13 middle classifications, 58 small classifications (category), and 333 attribution information of roads and rivers. It is extremely difficult to input the property information according to such extensive object classification. In addition, the development of external applications such as Revit plug-ins has created a need to automate specific and repetitive tasks. Therefore, following the BIM property classification system, an attribute input program was implemented for the system to enhance the productivity and convenience of the BIM users.

The study investigated how task types such as input or output could affect Korean EFL learners’ vocabulary learning at both short- and long-term periods after treatment. Forty two college students in Korea were randomly assigned to one of the four tasks. Based on Involvement Load Hypothesis, each task induced the same or different involvement loads: Read without glossary (Input), Gap with glossary (Input & Output), Gap without glossary (Input & Output), and Sentence and write (Output). Receptive and productive vocabulary knowledge was measured right after the treatment and a month later. The output-oriented tasks were found to be more effective than the input-oriented tasks regardless of type of vocabulary knowledge. The current study concluded that modalities such as input or output other than task-induced involvement load can contribute to Korean EFL learners’ vocabulary learning. The pedagogical discussion will be made at the conclusion.