This study quantitatively evaluates the effects of embankment height and input excitation frequency on crest settlement—a key damage indicator—for railway embankments founded on liquefiable ground. Dynamic numerical analyses were conducted using FLAC2D, based on the cross-section adopted in a previous 1-g shaking table test. The parametric study considered four embankment heights (0, 2, 4, and 6 m) and three input frequencies (0.8, 2.5, and 5.0 Hz). To simulate liquefaction in the foundation soil, the PM4Sand constitutive model was employed within an effective-stress framework. Model validity was first examined by comparing computed time histories of excess pore-water pressure, acceleration, and settlement with experimental results, and by confirming qualitative agreement with observed settlement trends across different embankment heights. The results show that crest settlement does not increase monotonically with embankment height; instead, it reaches a maximum and then decreases beyond a critical range. The largest settlement occurs when the embankment height is approximately 15~25% of the liquefiable layer thickness. This behavior reflects the competition between increased overburden pressure, which enhances liquefaction resistance beneath the embankment, and amplified lateral spreading, which increases permanent deformation. Although excitation frequency influences settlement, its effect is smaller than that of embankment height.

전 세계적인 물 부족 심화로 상수관망의 효율적 운영 및 유지보수(O&M) 중요성이 커지고 있다. 특히 정확한 수압 예측은 잠재적 문제의 사전 감지와 대응에 필수적이다. 이에 본 연구는 전처리된 데이터를 활용하여 현장 적용성이 높은 수압 예측 모델을 개발하는 것을 목표로 하였다. 이를 위해 8개 블록시스템(DMA)의 10분 단위 시계열 데이터와 4종류의 딥러닝 모델(LSTM, GRU, CNN-LSTM, CNN-GRU)을 활용하였으며, optuna를 통해 하이퍼파라미터를 최적화하고 배치 정규화 등을 적용해 학습 안정성을 확보하였다. 평가 결과, CNN-GRU 모델이 가장 우수한 성능을 나타냈다. 해당 모델을 기반으로 입력 조건에 따른 성능을 비교한 결과, 단변수 대비 다변수 입력 조건에서 예측 정확도가 향상됨을 확인하였다. 또한, 10분 선행 시점에서 최고 신뢰도(R2 0.9678, RMSE 0.0375)를 기록했으며, 지속성 모델의 성능이 점진적으로 하락하여 상대적인 저점을 형성하는 7시간 및 17시간 선행 시점에서 CNN-GRU 모델은 지속성 모델 대비 RMSE 기준 각각 48.0% 및 42.1%의 오차 개선을 달성하였다. 결론적으로, 본 연구에서 제안하는 전처리 및 하이퍼파라미터 통합 최적화 프로세스는 DMA별로 상이한 운영 환경에서도 안정적인 예측 성능을 확보할 수 있음을 입증하였다. 이는 현장 엔지니어의 데이터 분석 및 의사결정을 지원함으로써, 상수관망의 안정적인 운영과 유지보수 효율성 향상에 기여할 수 있을 것으로 기대된다.

최근 기후 변화로 인해 기온 상승과 강수 특성의 변화가 가속화되면서 도로 포장의 장기 성능에 미치는 영향이 점차 중 요해지고 있다. 아스팔트 콘크리트 포장은 온도 및 수분 조건에 민감한 재료 특성을 가지므로 동일한 재료·구조·교통 조건에 서도 기후 조건에 따라 포장 거동과 수명이 크게 달라질 수 있다. 현재 MEPDG(Mechanistic-Empirical Pavement Design Guide) 프로그램은 EICM(Enhanced Integrated Climatic Model) 기후 모델을 통해 온도 및 수분 조건을 반영하여 공용기간 동 안의 포장 성능을 예측하는 성능 기반 설계 방법을 제공하고 있으나, 기후 입력은 과거 관측자료를 기반으로 생성된 정상성 기후 데이터에 의존하고 있다. 그러나 최근 평균기온 상승과 극한 기상 현상의 증가로 장기 기후 조건이 비정상성을 나타내 고 있으며, 이러한 변화는 포장 설계의 신뢰성에 영향을 미칠 가능성이 있다. 본 연구에서는 강릉 지역의 1965–2024년의 장 기 기온 시계열 자료를 대상으로 확장 디키–풀러(Augmented Dickey-Fuller, ADF) 검정과 크비아트코프스키–필립스–슈미트– 신(Kwiatkowski–Phillips–Schmidt–Shin, KPSS) 검정을 수행하여 정상성 여부를 진단하였다. 분석 결과 두 검정 모두 비정상 성이 우세하게 나타나 시계열 내에 장기 추세가 존재함을 확인하였다. 나아가, 미래 기후 변화를 반영하기 위하여 네 가지 기온 시나리오를 설정하였다. 과거 관측자료의 월별 평균을 기반으로 한 기준 시나리오와, 장기 기온 상승 추세를 반영하기 위한 회귀 분석 기반 시나리오를 구성하였다. 또한 IPCC(Intergovernmental Panel on Climate Change)에서 제시한 SSP(Shared Socioeconomic Pathways) 시나리오 중 중간 배출 경로(SSP2-4.5)와 고배출 경로(SSP5-8.5)를 적용하여 미래 기온 조건을 설정 하였다. 강수량, 상대습도, 풍속 및 일조율은 장기 추세가 뚜렷하지 않은 것으로 판단되어 과거 자료의 평균 패턴을 기반으 로 미래 데이터를 생성하였다. 구축된 기후 데이터는 MEPDG 입력 형식으로 변환되어 기후 변화 조건에 따른 포장 성능 평 가에 활용될 수 있으며, 비정상성 기후를 고려한 포장 설계 및 유지관리 전략 수립을 위한 기초 자료를 제공할 수 있을 것 으로 기대된다.

Aiming at the control problem of nonlinear uncertain systems with asymmetric saturated actuators and u nknown external disturbances, a composite control method integrating dynamic surface control (DSC), ad aptive neural network estimation, and a nonlinear saturation compensation mechanism is proposed. In the scenarios of ship course and trajectory tracking, the system faces multiple challenges such as symmetric and asymmetric actuator saturation, as well as unknown external disturbances. Radial basis function (R BF) neural networks are utilized for online approximation of unknown nonlinear functions and external d isturbances. Combined with dynamic surface technology, the problem of "explosion of complexity" in tra ditional backstepping control is eliminated. A nonlinear function with inverse correlation to error gain is designed to dynamically adjust the control gain, balancing the requirements of tracking accuracy and sat uration suppression. Furthermore, a Gaussian error function is introduced to construct a continuously diff erentiable asymmetric saturation model. An auxiliary dynamic system is integrated to compensate for the saturation nonlinear effect, achieving smooth amplitude limitation of rudder angle commands. Comparati ve MATLAB simulation results demonstrate that the course tracking error is reduced by 1°, the fluctuati on amplitude of the rudder angle is decreased by approximately 50%, the number of rudder angle satura tion events is reduced by about 60%, and the error convergence time is shortened by roughly 30%. The proposed composite control method effectively addresses the issues of asymmetric saturation and externa l disturbances, significantly enhancing the accuracy and robustness of the ship course control system.

In this study, the effect of welding heat input on the microstructure and mechanical properties of reduced-activation ferritic/martensitic steel weld metal was investigated to provide a basis for developing welding technology for this steel, which is considered a structural material for fusion reactor blankets. Autogenous bead-on-plate gas tungsten arc welding was performed with heat inputs of 0.57, 1.38, and 2.32 kJ/mm, and the microstructural evolution and mechanical properties of the weld metal were analyzed. The fraction of residual δ-ferrite in the weld metal varied depending on the welding heat input, which acted as a primary factor contributing to the reduction in weld metal strength, although it remained higher than that of the base metal. In addition, the effect of post-weld heat treatment (PWHT) at 730 °C for 1 h was evaluated. Before PWHT, the weld metal exhibited significantly higher hardness compared with the base metal. However, after PWHT, its hardness was substantially reduced, thereby minimizing the differences in hardness of the weld and the base metal.

Seismic design and risk assessment require input ground motions that accurately reflect both the seismic intensity associated with the target hazard level and the regional seismic characteristics of Korea. In this study, a scenario earthquake was defined through seismic hazard deaggregation. Due to the lack of recorded ground motions in Korea for this particular scenario, a finite fault was modeled. Seed ground motions related to the scenario earthquake were generated using the empirical Green’s function method, based on the 912 Gyeongju earthquake. During the spectral matching process, the convergence of the spectrum used for ground motion selection and the target Uniform Hazard Spectrum (UHS) was analyzed. This analysis led to the proposal of specific spectral conditions for selecting ground motions. The final set of input ground motions was then applied in time-history analyses of a nuclear power plant containment structure to assess its seismic response characteristics. The analysis results demonstrate that the proposed ground motion generation procedure applies to the development of ground motions in regions with moderate seismicity.

본 연구의 목적은 제2언어로서의 중국어 교육에서 읽기 및 스토리텔링을 통한 교 수법(TPRS)의 적용 사례를 분석하여 국내 중국어 교육에서의 효과와 적용 가능성을 검토하는 것이다. 이에 본고에서는 언어습득 이론에서 크라센의 입력 가설을 바탕으 로 TPRS의 개념과 절차를 이론적으로 정리하고, 중국에서 발표된 다양한 TPRS 교육 사례를 분석했다. 사례 분석 결과, TPRS 방식은 의미 중심 입력을 통해 학습자 의 참여를 효과적으로 유도하고 듣기, 말하기, 읽기, 문법 및 어휘 능력을 향상시키 는 것으로 나타났다. 이 연구는 스토리 기반 수업이 언어와 문화를 통합하고 학습자 의 공감을 촉진하며 자발적인 말하기를 장려하는 데 효과적이라는 것을 시사한다. 그러나 스토리 구성, 수업 설계, 교사 역량 부담, 전용 교재 부족 등의 과제가 존재 한다. 국내 중국어 교육에서 TPRS의 적용 가능성을 높이기 위해서는 스토리 뱅크 구축, 교사 연수 프로그램 개발, 기존 스토리를 수업에 활용할 수 있도록 재구성하는 방안이 필요하다고 제안한다.

VR 콘텐츠에서 사용자의 몰입을 유도하기 위해서 사용하는 방법들 중 하나로는 컨트롤러에서 벗어난 입력 방식이 있다. 많은 콘텐츠에서 컨트롤러를 대체하기 위한 수단으로 핸드 트래킹 기술을 이용하여 손동작을 사용자 인터페이스로 사용하고 있다. 그러나 컨트롤러나 키보드와 같은 기계와 달리 사람의 손모양은 같은 모양이어도 사람마다 편안하게 느끼는 손 모양의 각 도가 다르기 때문에 개인차가 발생할 수 있다. 이는 핸드 트래킹의 인식률 저하를 야기하여 사 용자가 손모양을 이용한 사용자 인터페이스에 익숙해지는 것을 방해하는 요소가 될 수 있다. 이에 본 연구에서는 손가락 각도와 같은 손모양에 대한 사용자의 개인차가 VR 컨텐츠의 사용 자 경험에 미치는 영향에 관해 연구했고, 이를 설명하였다.

In this study, contribution evaluation method applying Independent Component Analysis (ICA) was proposed. The necessity of applying ICA to the contribution evaluation was investigated through numerical simulation. The simulation modeled a scenario where the vibration/noise sources were physically overlapped in a small space, and their frequency characteristics were similar. For comparison between the conventional contribution evaluation method and the proposed method, the contribution evaluation was performed using the ordinary and partial contribution evaluation methods. Through this analysis, it was confirmed that the proposed method can identify contributions by restoring the signal when the frequency characteristics of the vibration/noise sources were similar, and their positions overlapped. These results confirm that the contribution evaluation method based on independent component analysis is effective in appropriately analyzing vibration/noise sources when their frequency characteristics are similar, and their positions overlap.

콩은 높은 단백질 함량과 다양한 기능적 특성으로 인해 식품 및 사료 산업에 필수적인 작물이다. 그러나 농촌 인구의 고령화와 저렴한 수입 콩으로 인해 국내 콩 생산량은 꾸준히 감소하고 있다. 이러한 문제를 해결하기 위해서는 농업 기계 기술의 발전이 필수적이며, 특히 콩수확기의 선별 메커니즘을 개선하는 것이 중요하다. 따라서 본 연구는 CFD-DEM 결합 시뮬레이션을 사용하여 콩수확기의 선별장치 내의 유동 역학과 입자 움직임을 분석하여 선별 효율성을 향상시키는 것을 목표로 했다. 경남농업기술원에서 재배한 진풍 콩(Glycine max (L.) Merrill) 품종을 실험에 사용하였다. 선별장치는 콩, 콩대, 줄기를 분리하여 콩알만 수집하도록 설계되었다. 실험 중에는 콩 줄기를 균일하게 투입하여 분리율과 수집률을 측정하였다. 또한 유동 분석을 위해 표준 k-ε 난류 모델을 사용하였으며, CFD-DEM 결합 방법을 사용하여 선별 장치 내의 내부 유동과 입자 움직임을 시뮬레이션하였다. 추가로 CFD 분석 결과를 DEM 시뮬레이션에 활용하여 Ganser 항력 모델을 적용하여 콩과 콩대의 분리 특성을 분석하였다. 마지막으로 CFD-DEM 결합 시뮬레이션을 통해 콩수확기의 선별 장치 성능을 평가하고 최적의 팬 회전속도를 결정하였다. 실험에서 팬 회전속도는 각각 900 rpm, 1,000 rpm, 1,100 rpm, 1,200 rpm으로 설정하였다. 실제 선별장치에서 측정한 풍구 회전 시의 공기 유속과 시뮬레이션 에서 팬 회전으로 발생한 공기 유속 간의 RMSE 값은 0.64 m/s에서 1.12 m/s로 나타났다. 풍구 회전수에 대한 콩의 수집률을 시뮬레이션 결과 풍구 회전수가 증가할수록 수집률이 감소했으며, 900 rpm일 때 최대 94.08%의 수집률을 보였다. 콩대와 콩줄기 분리율의 경우 900 rpm과 1,000 rpm에서 55%~60%로 낮은 효율을 보였다. 1,100 rpm에서 86.38%, 1,200 rpm일 때 86.14%의 분리율이 측정되었다. 콩 수집률과 콩대 분리율 모두에서 최적의 성능을 발휘하려면, 풍구 회전수는 1,100 rpm이 적절한 것으로 보인다.

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.

지진취약도를 산정하기 위해서는 목표 부지의 특성을 제대로 표현할 수 있는 입력 지진파의 산정이 중요하다. 본 논문에서는 국내 외 강진 및 중‧약진 지역에서의 입력 지진파에 대한 단자유도 모델의 지진취약도를 분석하였다. 분석을 위한 첫 번째 단계로, 국외 강 진 기록 중 근/원거리에서 측정한 2개의 입력 지진파 세트와 국내 중·약진 지역 특성에 적합한 입력 지진파 2개의 세트, 총 4개의 입력 지진파 세트를 선정하였다. 대상 구조물로는 3가지 고유주기에 대한 비선형 단자유도 모델을 적용하였고, 취약도 분석을 위해 증분동 적해석을 이용하였다. 또한, 4가지 손상 상태를 정의하고, 손상 상태 각각에 대해 4가지 입력 지진파 세트의 고유주기별 지진취약도 결과를 제시하였다.

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.

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.