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조상호,최형권,유정열,Cho, Sang-Ho,Choi, Hyoung-Gwon,Yoo, Jung-Yul 대한기계학회 2003 大韓機械學會論文集B Vol.27 No.9
Flow characteristics of the fluid laden with many particles in the two-dimensional channel are investigated using the Navier-Stokes equations coupled with the equation of motion of particles by direct numerical simulation. A four-step fractional step method with Crank-Nicolson scheme and ALE technique is used for P2P1 mixed finite element method. The motion and distribution of particles in the fluid is virtually described as a result of direct numerical simulation and the increase of viscosity is compared with theoretical equations. The effect of channel height on the relative viscosity and the tubular pinch effect are discussed.
전기방사를 이용한 Polycaprolactone 나노섬유 부직포의 제조 및 세로 점착 ·증식 거동
조상호,오세행,이근형,김학용,이진호 한국생체재료학회 2003 생체재료학회지 Vol.7 No.4
Electrospinning is a fabrication process that can produce nonwoven, highly porous nanometer-scale fiber-based matrices using an electrostatically driven jet of polymer solution or melt. The method represents an attractive approach for polymeric biomaterial processing which provides the morphology similar to extracellular matrix (ECM) that may retain mechanical strength, flexibility, and high porosity. In this study, we fabricated polycaprolactone (PCL) nonwoven nanofibrous sheets and coated them with cell-adhesive protein, collagen type IV. The PCL nanofibrous sheets were characterized via morphology observation by a scanning electron microscope (SEM) and the measurement of mechanical properties by an universal testing machine (UTM). The cell adhesion and growth behaviors of the PCL nanofibrous sheets with/without collagen coating as well as control nonporous PCL sheets were compared by in vitro cell culture using fibroblast as a model cell. It was observed that cell adhesion and growth of the PCL nanofibrous sheet were improved by collagen coating, and were better than those of the control nonporous sheet due to the high level of surface area for cells to attach.
단계적 회귀분석과 인공신경망 모형을 이용한 광양항 석탄·철광석 물동량 예측력 비교 분석
조상호,남형식,류기진,류동근 한국항해항만학회 2020 한국항해항만학회지 Vol.44 No.3
It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months January 2009-January 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis. 항만의 주요 정책 및 향후 운영계획 수립 시 정확한 물동량 예측에 관한 연구는 매우 중요하며 이러한 중요성으로 인해 관련 연구가 활발히 수행되고 있다. 본 논문에서는 국내 최대 석탄 및 철광석 처리 항만인 광양항을 대상으로 단계적 회귀분석과 인공신경망모형을 활용하여 모형간 예측력을 비교하였다. 2009년 1월부터 2019년 1월까지 총 121개월의 월별자료를 활용하였으며 석탄 및 철광석 물동량에 영향을 주는 요인을 선정하여 공급관련요인과 시장·경제관련요인으로 분류하였다. 단계적 회귀분석 결과, 광양항 석탄 물동량 예측모형의 경우, 입항선박 톤수, 석탄가격 및 대미환율이 최종변수로 선정되었고 철광석 물동량 예측모형의 경우, 입항선박 톤수, 철광석가격이 최종변수로 선정되었다. 인공신경망모형의 경우, 모델 성능에 영향을 미치는 다양한 Hyper-parameters를 조정하며 최적 모델을 선정하는 시행착오법을 사용하였다. 분석결과 인공신경망모형이 단계적 회귀분석에 비해 우수한 예측성능을 나타내었으며 예측 모형별 예측값과 실측값을 그래프 상 비교 시에도 인공신경망모형이 단계적 회귀분석에 비해 고·저점을 유사하게 나타냈다.
조상호,김홍범,성명모 한국공업화학회 2019 Journal of Industrial and Engineering Chemistry Vol.77 No.-
Nickel sulfide (NiSx) was grown by atomic layer infiltration using bis(dimethylamino-2-methyl-2-butoxo)nickel(II) [Ni(dmamb)2] and hydrogen sulfide (H2S) as a metal precursor and a sulfur source. Thesteady-state growth rate of thefilm was 3.7 Å/cycles at 160–190 C which was much faster compared tothose by conventional atomic layer deposition method (<0.7 Å/cycles). This nickel sulfide thinfilms werecharacterized by taking X-ray photoelectron spectroscopy, scanning electron microscopy, X-raydiffraction, and hall measurements. The depositedfilms on Si wafer was single-phase polycrystallinewith multiple domains. The NiSx film grown onfluorine-doped tin oxide (FTO)-coated glass was appliedto a counter electrode in dye-sensitized solar cells, which performed a high catalytic activity for thereduction of I3to I and the comparable cell efficiency of 7.12% with cells using conventional Pt-coatedFTO counter electrode.