• Tunnel and Underground Space
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• 제26권3호
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• pp.212-219
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• 2016

The sprayable waterproofing membrane is installed between shotcrete to provide crack bridging and hence prevent flow of liquid water as a waterproofing system. Because of its material characteristics, the sprayable membrane can be constructed at more complex structure than sheet membrane. The main component of the sprayable waterproofing membrane is a polymer-based material, therefore, moisture can migrate through sprayable waterproofing membrane materials by capillary and vapor diffusion mechanisms. The moisture transport mechanisms can have an influence on the degree of saturation and may influence the pore pressure and risk of freeze-thaw damage on concrete linings and membrane. In this study, long-term hygrothermal behavior was simulated with considering moisture transport and long-term effects on saturation of tunnel linings. From the simulation, due to water absorption and vapor transport properties of sprayable membrane, change of relative humidity and water content in tunnel lining can be evaluated.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제15권5호
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• pp.2689-2694
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• 2014

This study analyze the impact of omija(maximowiczia chinensis) market by Korea-China FTA and review the change of mid and long term supply-demand from 2014 to 2018. A scenario is also imported to simulate and measure the impacts of the Korea-China FTA. The scenario is that tariff rates for Chinese product(omija) will be zero after 5 years from 2014. A partial equilibrium model of Omija is specified to forecast mid and long term supply-demand and prices. Equations in the model were estimated by using econometric techniques. The results based on scenario are compared with the results by the baseline case(maintenance of current situation). Our study show that when the tariff rates for Chinese product(Omija) will be zero after 5 years from 2014, the cultivated area of Omija is forecasted to decline until 3,370ha in 2018, and the consumption is forecasted to increase up to 12,040.8MT in 2018, and also total revenue of about 9.8 billion korean won will be decreased during 5 years(2014-2018).

• Nuclear Engineering and Technology
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• 제42권6호
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• pp.620-635
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• 2010

In this paper we describe current activities on the project Multi-Scale Modeling and Analysis of convective boiling (MSMA), conducted jointly by the Paul Scherrer Institute (PSI) and the Swiss Nuclear Utilities (Swissnuclear). The long-term aim of the MSMA project is to formulate improved closure laws for Computational Fluid Dynamics (CFD) simulations for prediction of convective boiling and eventually of the Critical Heat Flux (CHF). As boiling is controlled by the competition of numerous phenomena at various length and time scales, a multi-scale approach is employed to tackle the problem at different scales. In the MSMA project, the scales on which we focus range from the CFD scale (macro-scale), bubble size scale (meso-scale), liquid micro-layer and triple interline scale (micro-scale), and molecular scale (nano-scale). The current focus of the project is on micro- and meso-scales modeling. The numerical framework comprises a highly efficient, parallel DNS solver, the PSI-BOIL code. The code has incorporated an Immersed Boundary Method (IBM) to tackle complex geometries. For simulation of meso-scales (bubbles), we use the Constrained Interpolation Profile method: Conservative Semi-Lagrangian $2^{nd}$ order (CIP-CSL2). The phase change is described either by applying conventional jump conditions at the interface, or by using the Phase Field (PF) approach. In this work, we present selected results for flows in complex geometry using the IBM, selected bubbly flow simulations using the CIP-CSL2 method and results for phase change using the PF approach. In the subsequent stage of the project, the importance of effects of nano-scale processes on the global boiling heat transfer will be evaluated. To validate the models, more experimental information will be needed in the future, so it is expected that the MSMA project will become the seed for a long-term, combined theoretical and experimental program.

• Journal of IKEEE
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• 제23권1호
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Korean Journal of Agricultural Science
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• 제48권3호
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• pp.433-446
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• 2021

In this study, the future flood inundation changes under a climate change were simulated in the Tonle Sap basin in Cambodia, one of the countries with high vulnerability to climate change. For the flood inundation simulation using the rainfall-runoff-inundation (RRI) model, globally available geological data (digital elevation model [DEM]; hydrological data and maps based on Shuttle elevation derivatives [HydroSHED]; land cover: Global land cover facility-moderate resolution imaging spectroradiometer [GLCF-MODIS]), rainfall data (Asian precipitation-highly-resolved observational data integration towards evaluation [APHRODITE]), climate change scenario (HadGEM3-RA), and observational water level (Kratie, Koh Khel, Neak Luong st.) were constructed. The future runoff from the Kratie station, the upper boundary condition of the RRI model, was constructed to be predicted using the long short-term memory (LSTM) model. Based on the results predicted by the LSTM model, a total of 4 cases were selected (representative concentration pathway [RCP] 4.5: 2035, 2075; RCP 8.5: 2051, 2072) with the largest annual average runoff by period and scenario. The results of the analysis of the future flood inundation in the Tonle Sap basin were compared with the results of previous studies. Unlike in the past, when the change in the depth of inundation changed to a range of about 1 to 10 meters during the 1997 - 2005 period, it occurred in a range of about 5 to 9 meters during the future period. The results show that in the future RCP 4.5 and 8.5 scenarios, the variability of discharge is reduced compared to the past and that climate change could change the runoff patterns of the Tonle Sap basin.

• Journal of Internet Computing and Services
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• 제22권3호
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• pp.67-73
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• 2021

In this paper, in order to prevent the fall of the drone, a study was conducted to collect vibration data from the motor connected to the propeller of the drone, and to predict the abnormal vibration of the drone using recurrent neural network (RNN) and long short term memory (LSTM). In order to collect the vibration data of the drone, a vibration sensor is attached to the motor connected to the propeller of the drone to collect vibration data on normal, bar damage, rotor damage, and shaft deflection, and abnormal vibration data are collected through LSTM and RNN. The root mean square error (RMSE) value of the vibration prediction result were compared and analyzed. As a result of the comparative simulation, it was confirmed that both the predicted result through RNN and LSTM predicted the abnormal vibration pattern very accurately. However, the vibration predicted by the LSTM was found to be 15.4% lower on average than the vibration predicted by the RNN.

• Atmosphere
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• 제31권5호
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2023년도 학술발표회
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• pp.206-206
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• 2023

지구온난화로 인한 기후변화에 따라 평균강수량과 증발량이 증가하며 강우지역 집중화와 강우강도가 높아질 가능성이 크다. 우리나라의 경우 협소한 국토면적과 높은 인구밀도로 기후변동의 영향이 크기 때문에 한반도에 적합한 유역규모의 수자원 예측과 대응방안을 마련해야 한다. 이를 위한 수자원 관리를 위해서는 유역에서 강수량, 유출량, 증발량 등의 장기적인 자료가 필요하며 경험식, 물리적 강우-유출 모형 등이 사용되었고, 최근들어 연구의 확장성과 비 선형성 등을 고려하기 위해 딥러닝등 인공지능 기술들이 접목되고 있다. 본 연구에서는 ASOS(동해, 태백)와 AWS(삼척, 신기, 도계) 5곳의 관측소에서 2011년~2020년까지의 일 단위 기상관측자료를 수집하고 WAMIS에서 같은 기간의 오십천 하구 일 유출량 자료를 수집 후 5개 관측소를 기준으로Thiessen 면적비를 적용해 기상자료를 구축했으며 Angstrom & Hargreaves 공식으로 잠재증발산량 산정해 3개의 모델에 각각 기상자료(일 강수량, 최고기온, 최대 순간 풍속, 최저기온, 평균풍속, 평균기온), 일 강수량과 잠재증발산량, 일 강수량 - 잠재증발산량을 학습 후 관측 유출량과 비교결과 기상자료(일 강수량, 최고기온, 최대 순간 풍속, 최저기온, 평균풍속, 평균기온)로 학습한 모델성능이 가장 높아 최적 모델로 선정했으며 일, 월, 연 관측유출량 시계열과 비교했다. 또한 같은 학습자료를 사용해 다층 퍼셉트론(Multi Layer Perceptron, MLP) 앙상블 모델을 구축하여 수자원 분야에서의 인공지능 활용성을 평가했다.

• Journal of The Korean Society of Agricultural Engineers
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• 제65권2호
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• pp.35-45
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• 2023

The DOR (Drainage outlet raising) in the paddy field has been suggested as one of the most important best management practices for the TMDL (Total maximum daily load) management in the technical guidelines by the NIER (National institute of environmental research). However, this method is underestimated and is not well adopted by local governments for the TMDL. The purpose of this study is to evaluate the unit load reduction equation according to the application of DOR in order to expand this equation. The original equation in the guideline was derived using the HSPF (Hydrological Simulation Program-Fortran) model for 1 year in Changnyeong. We analyzed the reduction effect of the original equation application by collecting additional long-term monitoring data from the Buan, Icheon, Iksan, and Jeonju. When comparing the reduction loads between the original equation and monitoring results, the evaluation results of the original equation were 11% of the monitoring analysis results, which was underestimated. This means that the original equation needs to be improved. For assessing the equation, the HSPF Paddy-RCH model was established according to the NI ER guideline and evaluated for applicability. The performance results of the model showed a reasonable range by the statistical criteria. Modified equations 1 and 2 were proposed based on the monitoring and modeling results. Modified equation 1 was the method of modifying the original equation's main factors, and modified equation 2 was the method of applying the non-point pollution reduction efficiency according to the rainfall class using the long-term modeling results. At the level of 58.6~64.6% of monitoring data, the difference between them could be further reduced compared to the original equation. The suggested approach will be more reasonable and practicable for decision-makers and will contribute to the TMDL management plans.

• Current Photovoltaic Research
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• 제10권1호
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• pp.16-22
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• 2022

The long-term stability of PSCs against visual and UV light, moisture, electrical bias and high temperature is an important issue for commercialization. In particular, since the operation temperature of solar cell can rise above 85℃, a study on thermal stability is required. In this study, the cause of thermal-induced degradation of PSCs was investigated using the SCAPS-1D simulation tool. First, PSCs of TiO₂/CH₃NH₃PbI₃/Spiro-OMeTAD/Au structure were exposed to a constant temperature of 85℃ to observe changes in conversion efficiency and quantum efficiency. Because the EQE reduction above 500 nm was remarkable, we simulated PSCs performance as a function of lifetime, doping density of perovskite and spiro-OMeTAD. Consequently, the main cause of thermal-induced degradation is considered to be the change in the perovskite doping concentration and lifetime due to ion migration of perovskite.