• 한국해안해양공학회지
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• 제8권2호
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• pp.185-192
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• 1996

연안에서의 효율적인 확산해석을 위해 연산자 분리기법에 의한 Eulerian-Lagrangian 농도모형과 random walk 방법이 결합된 모형이 수립되었다 수립한 입자추적모형은 특히 고농도 변화 지역에서 오차 없이 만족스럽게 해석할 수 있는 수단으로 판단된다. 모형실험결과 오염원 방류초기의 ▽C$\geq$0.005에서 거의 정확해와 일치하는 결과를 얻었고, ▽C$\leq$0.002에서는 만족스럽지 못한 결과가 유도되어 입자추적모형의 적용한계의 기준으로 제시되었다. 따라서 실제 해역에서의 적용에서 고농도의 오염원이 발생되는 근역에서는 입자추적모형을 적용하고 이후의 전역에 대해서는 농도모형을 이용하여 해석상의 능률 제고와 정도의 향상을 도모할 수 있었다.

• Architectural research
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• 제8권1호
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• pp.37-45
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• 2006

A fungal spore transportation model that accounts for the concentration of airborne indoor spores and the amount of spores deposited on interior surfaces has been developed by extending the current aerosol model. This model is intended to be used for a building with a mechanical ventilation system, and considers HVAC filter efficiency and ventilation rate. The model also includes a surface-cleaning efficiency and frequency that removes a portion of spores deposited on surfaces. The developed model predicts indoor fungal spore concentration and provides an indoor/outdoor ratio that may increase or decrease mold growth risks in real, in-use building cases. To get a more useful outcome from the model simulation, an uncertainty analysis has been conducted in a real building case. By including uncertainties associated with the parameters in the spore transportation model, the simulation results provide probable ranges of indoor concentration and indoor/outdoor ratio. This paper describes the uncertainty quantification of each parameter that is specific to fungal spores, and uncertainty propagation using an appropriate statistical technique. The outcome of the uncertainty analysis showed an agreement with the results from the field measurement with air sampling in a real building.

• Membrane and Water Treatment
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• 제3권2호
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• pp.77-98
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• 2012

A two-dimensional (2D) steady state numerical model of concentration polarisation (CP) phenomena in a membrane channel has been developed using the commercially available computational fluid dynamics (CFD) package CFX (Ansys, Inc., USA). The model incorporates the transmembrane pressure (TMP), axially variable permeate flux, variable diffusivity and viscosity, and osmotic pressure effects. The model has been verified against several benchmark analytical and empirical solutions from the membrane literature. Additionally, the model is able to predict the rejection of an arbitrary solute by the membrane using a pore model, given some basic knowledge of the geometry of the solute molecule or particle, and the membrane pore geometry. This allows for predictive design of membrane systems without experimental determination of the membrane rejection for the specified operating conditions. A demonstration of the model is presented against experimental results for two uncharged test compounds (sucrose and PEG1000) from the literature. The model will be extended to incorporate charge effects, transient simulations, three-dimensional (3D) geometry and turbulent effects in future work.

• Safety and Health at Work
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• 제15권1호
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• pp.33-41
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• 2024

Background: Dust generated during the processing of building materials enterprises can pose a serious health risk. The study aimed to compare and analyze the results of ART and the Monte Carlo model for the dust exposure assessment in building materials enterprises, to derive the application scope of the two models. Methods: First, ART and the Monte Carlo model were used to assess the exposure to dust in each of the 15 building materials enterprises. Then, a comparative analysis of the exposure assessment results was conducted. Finally, the model factors were analyzed using correlation analysis and the scope of application of the models was determined. Results: The results show that ART is mainly influenced by four factors, namely, localized controls, segregation, dispersion, surface contamination, and fugitive emissions, and applies to scenarios where the workplace information of the building materials enterprises is specific and the average dust concentration is greater than or equal to 1.5 mg/m³. The Monte Carlo model is mainly influenced by the dust concentration in the workplace of building materials enterprises and is suitable for scenarios where the dust concentration in the workplace of the building materials enterprises is relatively uniform and the average dust concentration is less than or equal to 6mg/m³. Conclusion: ART is most accurate when workplace information is specific and average dust concentration is > 1.5 mg/m³; whereas, The Monte Carlo model is the best when dust concentration is homogeneous and average dust concentration is < 6 mg/m³.

• KSBB Journal
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• 제6권2호
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• pp.175-180
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• 1991

The quantitative effects of molecular weight and concentrations of two phase-forming polymers-polyethylene glycol and crude dextran on the two phase extractive ethanol fermentation were investigated using a Box-Wilson central composite protocol. The regression model obtained was used in order to determine optimum compositions of aqueous two phase system. In the aqueous two phase extractive ethanol fermentation of Kluyueromyces fragilis CBS 1555 with Jerusalem artichoke juice, it was found from the regression model that the variables influenlcing on ethanol fermentation were PEG concentration, time, Dx concentration, and PEG molecular weight strongly in order. The interaction of PEG concentration and PEG molecular weight was also found, and the effect of PEG concentration decreased with increase in molecular weight of PEG. The ethanol concentration incresed with increase in molecular weight of PEG, and with decrease in concentration of PEG. In conolusion, maximum concentration of ethanol produced was obtained at the following compositions; PEG MW 20000, Dx concentration ranged from 4% to 5%, and PEG concentration ranged from 3% to 7%.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.510-512
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• 2022

미세먼지 예측 정확도 향상을 위해 다양한 연구가 진행되고 있다. 그러나 미세먼지 농도가 가지는 다양한 특성에 따라 예측 모델의 학습이 잘 이루이지지 않는 문제가 있다. 본 논문에서는 시계열의 특성과 불규칙적인 특성을 가지는 미세먼지 농도의 학습 및 예측을 위해 LSTM과 EEMD 기반의 미세먼지 농도 예측 모델의 성능을 비교하고자 한다. 두 모델을 통해 시계열 특성 파악 방법과 독립적인 개별 특성 파악 방법의 성능 차이를 확인한 결과, 개별 특성 파악에 강점을 가지는 EEMD 예측 모델이 LSTM 기반의 예측 모델보다 좋은 성능을 보이는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Computers and Concrete
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• 제8권2호
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• pp.125-142
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• 2011

In the present paper, a numerical simulation method based on mesoscopic composite structure of concrete, the truss network model, is developed to evaluate the diffusivity of concrete in order to account for the microstructure of concrete, the binding effect of chloride ions and the chloride concentration dependence. In the model, concrete is described as a three-phase composite, consisting of mortar, coarse aggregates and the interfacial transition zones (ITZs) between them. The advantage of the current model is that it can easily represent the movement of mass (e.g. water or chloride ions) through ITZs or the potential cracks within concrete. An analytical method to estimate the chloride diffusivity of mortar and ITZ, which are both treated as homogenious materials in the model, is introduced in terms of water-to-cement ratio (w/c) and sand volume fraction. Using the newly developed approaches, the effect of cracking of concrete on chloride diffusion is reflected by means of the similar process as that in the test. The results of calculation give close match with experimental observations. Furthermore, with consideration of the binding capacity of chloride ions to cement paste and the concentration dependence for diffusivity, the one-dimensional nonlinear diffusion equation is established, as well as its finite difference form in terms of the truss network model. A series of numerical analysises performed on the model find that the chloride diffusion is substantially influenced by the binding capacity and concentration dependence, which is same as that revealed in some experimental investigations. This indicates the necessity to take into account the binding capacity and chloride concentration dependence in the durability analysis and service life prediction of concrete structures.

• 한국물환경학회지
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• 제31권3호
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• pp.272-285
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• 2015

Accurate assessment of chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing is challenging due to the optical complexity of case 2 waters. and the inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of Chl-a concentrations. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. So we have applied and validated a recently developed Red-NIR three-band model and an IOPs Inversion Model for estimating Chl-a concentration and deriving inland water IOPs in Lake Uiam. Three band and IOPs based Chl-a estimation model accuracy was assessed with samples collected in different seasons. The results indicate that this models can be used to accurately retrieve Chl-a concentration and absorption coefficients. For all datasets the determination coefficients of the 3-band models versus Chl-a concentration ranged 0.65 and 0.88 and IOPs based model versus Chl-a concentration varied from 0.73 to 0.83 respectively. and Comparison between 3-band and IOPs based models showed significant performance with decrease of root mean square error from 18% to 33.6%. The results of this study provides the potential of effective methods for remote monitoring and water quality management in turbid inland water bodies using hyper-spectral remote sensing.

• Bulletin of the Korean Chemical Society
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• 제32권11호
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• pp.3967-3972
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• 2011

A whole body physiologically based pharmacokinetic (PBPK) model was applied to investigate absorption, distribution, and physiologic variations on pharmacokinetics of imatinib in human body. Previously published pharmacokinetic data of the drug after intravenous (i.v.) infusion and oral administration were simulated by the PBPK model. Oral dose absorption kinetics were analyzed by adopting a compartmental absorption and transit model in gut section. Tissue/plasma partition coefficients of drug after i.v. infusion were also used for oral administration. Sensitivity analysis of the PBPK model was carried out by taking parameters that were commonly subject to variation in human. Drug concentration in adipose tissue was found to be higher than those in other tissues, suggesting that adipose tissue plays a role as a storage tissue for the drug. Variations of metabolism in liver, body weight, and blood/plasma partition coefficient were found to be important factors affecting the plasma concentration profile of drug in human body.