• 한국해양공학회지
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• 제23권2호
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• pp.47-52
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• 2009

A Lagrangian particle tracking model coupled with the Princeton Ocean Model were used to estimate the average residence time of coastal water in Masan Bay, Korea. Our interest in quantifying the transport time scales in Masan Bay was stimulated by the search for a mechanistic understanding of this spatial variability, which is consistent with the concept of spatially variable transport time scales. Tidal simulation was calibrated through a comparison with the results of semi-diurnal current and water elevation measured at the tidal stations of Masan, Gadeokdo. In the model simulations, particles were released in eight cases, including slack before ebb, peak ebb, slack before flood, and peak flood, during both spring and neap tides. The averaged values obtained from the particle release simulations were used for the average residence times of the coastal water in Masan Bay. The average residence times for the southeastern parts of Somodo and the Samho River, Masan Bay were estimated to be about 20~50days and 70~80days, respectively. The spatial difference for the average residence time was controlled by the tidal currents and distance from the mouth of the bay. Our results might provide useful for understanding the transport and behavior of coastal water in a bay and might be used to estimate the dissimilative capacity for environmental assessment.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2616-2628
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• 2021

The results of effective irradiation swelling in a wide range of burnup levels are numerically obtained for an inert matrix fuel, which are verified with DART model. The fission gas swelling of fuel particles is calculated with a mechanistic model, which depends on the external hydrostatic pressure. Additionally, irradiation and thermal creep effects are included in the inert matrix. The effects of matrix creep strains, external hydrostatic pressure and temperature on the effective irradiation swelling are investigated. The research results indicate that (1) the above effects are coupled with each other; (2) the matrix creep effects at high temperatures should be involved; and (3) ranged from 0 to 300 MPa, a remarkable dependence of external hydrostatic pressure can be found. Furthermore, an explicit multi-variable mathematic model is established for the effective irradiation swelling, as a function of particle volume fraction, temperature, external hydrostatic pressure and fuel particle fission density, which can well reproduce the finite element results. The mathematic model for the current volume fraction of fuel particles can help establish other effective performance models.

• Toxicological Research
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• 제17권
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• pp.293-297
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• 2001

The international pharmaceutical and regulatory communities had been recognizing the limited utility of conventional rodent carcinogenicity study particularly on the second species, mouse, after intense investigation of carcinogenicity data base worldwide, and a new scheme for carcinogenicity testing for pharmaceuticals was proposed at the Expert Working Group on Safety in the International Conference on Harmonization (ICH) in 1996. CB6F 1-Tg rasH2 mouse carrying human prototype c-Ha-ras gene with its own promoter/enhancer is one oj the new carcinogenicity assay model for human cancer risk assessment. Studies have been conducted since 1992 to validate the transgenic (Tg) mice for rapid carcinogenicity test-ing, short term (26 weeks) studies with genotoxic (by Salmonella), non-genotoxic carcinogens, genotoxic non-carcinogens, non-genotoxic non-carcinogens revealed relatively high concordance oj the response of the Tg mouse with classical bioassay across classes of carcinogenic agents. Mechanistic basis for carcinogensis in the model are being elucidated in terms of the role of overexpression and/or point mutation of the transgene. This report review the initial studies of validation of the model and preliminary results of on-going ILSI HESI ACT project will be presented.

• 한국물환경학회지
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• 제33권3호
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• pp.326-333
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• 2017

The lakes and reservoirs have been reported as important sources of carbon emissions to the atmosphere in many countries. Although field experiments and theoretical investigations based on the fundamental gas exchange theory have proposed the quantitative amounts of Net Atmospheric Flux (NAF) in various climate regions, there are still large uncertainties at the global scale estimation. Mechanistic models can be used for understanding and estimating the temporal and spatial variations of the NAFs considering complicated hydrodynamic and biogeochemical processes in a reservoir, but these models require extensive and expensive datasets and model parameters. On the other hand, data driven machine learning (ML) algorithms are likely to be alternative tools to estimate the NAFs in responding to independent environmental variables. The objective of this study was to develop random forest (RF) and multi-layer artificial neural network (ANN) models for the estimation of the daily $CO_2$ NAFs in Daecheong Reservoir located in Geum River of Korea, and compare the models performance against the multiple linear regression (MLR) model that proposed in the previous study (Chung et al., 2016). As a result, the RF and ANN models showed much enhanced performance in the estimation of the high NAF values, while MLR model significantly under estimated them. Across validation with 10-fold random samplings was applied to evaluate the performance of three models, and indicated that the ANN model is best, and followed by RF and MLR models.

• 한국추진공학회지
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• 제22권5호
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• pp.40-48
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• 2018

본 논문은 시뮬레이션 기반으로 메타 모델을 이용하여 LEEFI형 착화장치의 설계 신뢰도를 추정하는 방법과 설계 신뢰도를 추정한 결과를 나타내었다. LEEFI형 착화장치에서 비행편 속도는 화약 기폭에 중대한 영향을 미친다. 복잡한 물리적 현상으로 비행편의 속도가 발생하기 때문에 문헌에 공개된 역학적 모델을 이용하여 비행편 속도를 평가하는 데 많은 연산 시간이 필요하다. 또한 높은 신뢰도를 가지는 착화기는 요구되는 신뢰수준이 증가할수록 신뢰도 평가에 연산 비용이 증가한다. 따라서 설계 신뢰도 추정시 연산 효율성을 증가시키기 위하여 시간에 따른 비행편 속도에 대한 메타모델을 구축하였다. 구축한 메타모델을 이용하여 설계 변수의 다양한 분포 및 시그마 수준에 따른 설계 신뢰도를 추정한 결과를 나타내었다. 그리고 제안하는 추정 방법에 대한 연산 효율성과 정확성을 분석하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.150-150
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• 2023

기작기반의 역학적 모델과 자료기반의 딥러닝 모델은 수질예측에 다양하게 적용되고 있으나, 각각의 모델은 고유한 구조와 가정으로 인해 장·단점을 가지고 있다. 특히, 딥러닝 모델은 우수한 예측 성능에도 불구하고 훈련자료가 부족한 경우 오차와 과적합에 따른 분산(variance) 문제를 야기하며, 기작기반 모델과 달리 물리법칙이 결여된 예측 결과를 생산할 수 있다. 본 연구의 목적은 주요 상수원인 댐 저수지를 대상으로 수심별 수온과 탁도를 예측하기 위해 기작기반과 자료기반 모델의 장점을 융합한 PGDL(Process-Guided Deep Learninig) 모델을 개발하고, 물리적 법칙 만족도와 예측 성능을 평가하는데 있다. PGDL 모델 개발에 사용된 기작기반 및 자료기반 모델은 각각 CE-QUAL-W2와 순환 신경망 딥러닝 모델인 LSTM(Long Short-Term Memory) 모델이다. 각 모델은 2020년 1월부터 12월까지 소양강댐 댐 앞의 K-water 자동측정망 지점에서 실측한 수온과 탁도 자료를 이용하여 각각 보정하고 훈련하였다. 수온 및 탁도 예측을 위한 PGDL 모델의 주요 알고리즘은 LSTM 모델의 목적함수(또는 손실함수)에 실측값과 예측값의 오차항 이외에 역학적 모델의 에너지 및 질량 수지 항을 제약 조건에 추가하여 예측결과가 물리적 보존법칙을 만족하지 않는 경우 penalty를 부가하여 매개변수를 최적화시켰다. 또한, 자료 부족에 따른 LSTM 모델의 예측성능 저하 문제를 극복하기 위해 보정되지 않은 역학적 모델의 모의 결과를 모델의 훈련자료로 사용하는 pre-training 기법을 활용하여 실측자료 비율에 따른 모델의 예측성능을 평가하였다. 연구결과, PGDL 모델은 저수지 수온과 탁도 예측에 있어서 경계조건을 통한 에너지와 질량 변화와 저수지 내 수온 및 탁도 증감에 따른 공간적 에너지와 질량 변화의 일치도에 있어서 LSTM보다 우수하였다. 또한 역학적 모델 결과를 LSTM 모델의 훈련자료의 일부로 사용한 PGDL 모델은 적은 양의 실측자료를 사용하여도 CE-QUAL-W2와 LSTM 보다 우수한 예측 성능을 보였다. 연구결과는 다차원의 역학적 수리수질 모델과 자료기반 딥러닝 모델의 장점을 결합한 새로운 모델링 기술의 적용 가능성을 보여주며, 자료기반 모델의 훈련자료 부족에 따른 예측 성능 저하 문제를 극복하기 위해 역학적 모델이 유용하게 활용될 수 있음을 시사한다.

• Geomechanics and Engineering
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• 제32권6호
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• pp.583-600
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• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.

• Journal of Pharmaceutical Investigation
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• 제37권2호
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• pp.73-78
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• 2007

The aim of this study was to investigate the absorption properties of ketoprofen. The in-situ perfusion model has advantages over in vitro models as it provides intact lymphatic and blood flow circulation. The absorption properties of six different concentrations of ketoprofen have been studied in single pass in-situ rat intestine model. $^{14}C-PEG$ 4000 was used as a permeability marker and the possibility of an energy dependent contribution to ketoprofen absorption was also Investigated using the metabolic inhibitor sodium azide. Three different concentrations of sodium azide were studied to examine its effect on absorption of ketoprofen from the rat intestine. The findings of this study suggest that mono-carboxylic type drugs like ketoprofen cause permeability changes in the intestine. This is shown by the increase in absorption of $^{14}C-PEG$ 4000 as the concentration of ketoprofen is increased. However, the trend for ketoprofen permeability is to decrease over the concentration ranges. It was observed that the Papp values for ketoprofen with sodium azide shows a trend towards reduction in the amount of ketoprofen absorbed from the rat intestine which was significantly different (p<0.05) from that of ketoprofen with sodium azide 3.0mM. This indicates that sodium azide has an affect on the absorption of ketoprofen. The pH of all the perfusion solutions was altered to ${\sim}pH\;6.7$ by the buffering capacity of the small intestine secretions. The results suggest that mechanisms other than passive diffusion may be involved in ketoprofen absorption. This would be consistent with the involvement of active transport or saturatable processes in the absorption of drugs containing monocarboxylic acid group, as has been previously suggested from in vitro data.

• Structural Engineering and Mechanics
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• 제47권4호
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• pp.531-544
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• 2013

Compared with pavement structures of ordinary road sections, pavement structures in the intersection are exposed to more complex traffic characteristics which may exacerbates pavement distresses such as fatigue-cracking, shoving, shear deformation and rutting. Based on a field survey about traffic characteristics in the intersection conducted in Shanghai China, a three dimensional dynamic finite-element model was developed for evaluating the mechanistic responses in the pavement structures under different traffic characteristics, namely uniform speed, acceleration and deceleration. The results from this study indicated that : (1) traffic characteristics have significant effects on the distributions of the maximum principal strain (MPS) and the maximum shear stress (MSS) at the pavement surface; (2) vehicle acceleration or deceleration substantially impact the MPS and MSS at pavement surface and could increase the magnitude of them by 20 percent to 260 percent; (3) in the vertical direction, with the increase of vehicle deceleration rate, the location of the MPS peak value and the MSS peak value changes from the sub-surface layer to the pavement surface.