• Journal of Korea Port Economic Association
• /
• v.27 no.1
• /
• pp.185-212
• /
• 2011

The purpose of this paper is to show the empirical analysis way for measuring the seaport efficiency by using the previous radial model and the newly modified non-radial models( panel additive model, panel RAM model, and panel SBM model)with Spearman rank order correlation coefficient(SROCC) for 20 Korean ports during 11 years(1997-2007) for 1 inputs(port investment amount) and 4 outputs(Number of Ship Calls, Port Revenue, Customer Satisfaction Score for Port Service and Container Cargo Throughput). The main empirical results of this paper are as follows. First, consistency ratio of SROCC in terms of efficiency scores between radial and panel Additive model was over about 76% and overall consistency ratio was about 71.6%. Second, an efficiency of panel RAM model was higher than that of radial model with similarity. However, panel SBM model shows the very similar efficiency scores with panel radial model. Third, the slack size of radial model is smaller compared to non-radial model. Models' ranking orders in terms of efficiency scores, number of efficient ports are panel RAM model, panel SBM model, and radial model. The order from the minimum efficiency scores was the same order like just before. The policy implication to the Korean seaports and planner is that Korean seaports should introduce the new methods like non-radial models(panel additive model, panel RAM model, and panel SBM model) for measuring the port performance.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.2085-2089
• /
• 2008

수자원 개발이 자연 하천 생태계에 미치는 다양한 악영향들은 하천에 서식하는 동 식물의 생태서식지의 변화를 초래할 수 있다. 하천생태계를 보전하고 복원하기 위해서는 수리학과 생물학이 연계된 생태수리학적 모형을 지속적으로 연구 개발하여 수자원 개발에 따른 장래의 물리적인 거동을 비교적 정확하게 분석 및 예측하는 것이 필요하다. 본 연구의 목적은 21세기 프론티어 연구사업 'RAMS (River Analysis and Modeling System) 적용' 과제 중 개발된 RAM2, RAM4, RAM6 해석 모형으로부터 유속, 수위, 수온, SS, 하상고, 하상재료 데이터를 받아들여 GIS와 연계하는 2차원 하천 생태 모형 (RAM 8)을 개발하는 것이다. 그리고 개발에 따른 모형 검증을 위해서 우리나라 4대 강 중 유일하게 하구둑이 설치되어 있지 않아 자연적인 하천지형과 기수역 생태계가 잘 보존된 한강 하구부를 적용구간으로 선정하여, 서식지 적합성을 판단하고 그에 따른 적정 유량을 산정하여 1차원 서식처 모형인 PHABSIM을 이용한 결과와 비교한다.

• Journal of Korean Society of Water and Wastewater
• /
• v.25 no.5
• /
• pp.667-679
• /
• 2011

In this study, the influences of pollutant from Dae-po Stream and So-gam Stream located at the downstream of Nak-dong River on the water quality at Mul-geum water intake station were analyzed using RAMS model. Field measurements of velocity by ADCP, and water quality distribution of BOD and TP by water sampling were carried out to present the input and verification data for numerical simulations. The comparison between RAM2 and ADCP measurement, which aimed for the analysis of 2-D velocity distribution around Mul-geum water intake station showed that two results matched well along the spanwise direction. The prediction of pollutant concentration by RAM4 agreed fairly well with the measured data except for the points nearby right banks in the vicinity of tributary pollutant source. Flushing effect by the increase of mainstream discharge in Nak-dong River was analyzed to provide the damage mitigation in preparation for the accidental water pollution. With increasing mainstream discharge, high velocity and increased water quantity induced increasing dilution effect, thereby decreasing the inflow pollutant concentration rapidly.

• Korean Journal of Agricultural Science
• /
• v.50 no.4
• /
• pp.675-696
• /
• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.55 no.4
• /
• pp.199-203
• /
• 2006

This paper presents an ASER (Accelerated Soft Error Rate) integral model. The model is based on the facts that the generated EHP/s(electron hole pairs) are diminished after some residual range of the incident alpha particle, where residual range is a function of the incident angle and the capping layer thickness over the semiconductor junction. The ASER is influenced by the flux of the alpha particles, the junction area ratio, the alpha particle incident angle when the critical charge is same as the collected charge, and the sizes of the alpha source and the chip. The model was examined with 8M static RAM samples. The measured ASER data showed good agreement with the calculated values using the model. The ASER decreased exponentially with respect to the operational voltage. As the capping layer thickness increases up to $16{\mu}m$, the ASER increases, and after that thickness, the ASER decreases. The ASER increased as the depth of BNW increased from $0{\mu}m\;to\;4{\mu}m$. and then saturated. The ASER decreased as the node capacitance increased from 2fF to 5fF.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.6B
• /
• pp.515-522
• /
• 2011

The hydrodynamic models are widely used in the research for analysis of flow characteristics and design of hydraulic structure and river channel. These models need to be calibrated with observed data. But, there are few field data of two-dimensional flow velocity in flood because the direct measurement of the flood flow velocity are very dangerous. For this reason the results of two-dimensional numerical models are usually calibrated and verified with only a few observed data. Moreover, the verification of numerical models for the design flood is usually carried out using the result of one-dimensional model, HEC-RAS. In this study, using the flow velocity profile extracted from the aerial photos of a flood of the Tone River in Japan, two-dimensional numerical models, RAM2 in RAMS, RMA2 in SMS, and one-dimensional numerical model, HEC-RAS which are most widely used in research and design work are verified and the validity for verification of two-dimensional models with HEC-RAS is reviewed. The results showed that the water surface elevation of HEC-RAS, RAM2 and RMA2 models have similar results with observed data. But, the velocity results of RAM2 and RMA2 models in the floodplain have some difference with the velocity from aerial photo analysis. And the velocity result of HEC-RAS has big difference with the sectional averaged value of velocity from aerial photo analysis.

• Journal of Korea Technology Innovation Society
• /
• v.19 no.4
• /
• pp.711-735
• /
• 2016

Drug Development is very important for promoting public health and pharmaceutical industry. There has been many studies on the efficiency of drug development, but there are few studies on the drug development R&D performed by government. Since CCR model assumes unidirectional influence of input and output, it is not appropriate to analyze the efficiency of R&D due to the time-lag and spill-over effect. Also, BBC model which assumes variable returns to scale has difficulty in deriving priorities between decision making units. Recently, Range Adjusted Measure (RAM) model has been suggested in R&D efficiency analysis. RAM model measures the efficincy by eliminating inefficiencies under variable returns to scale assumption, and its strong monotonicity enables to provide clear priorities between decision making units. In this study, we analyzed the efficiency of national R&D programs for drug development using the two-step approach, including RAM model and Tobit regression analysis, and discussed major policy implications.

• Geomechanics and Engineering
• /
• v.34 no.5
• /
• pp.547-559
• /
• 2023

We evaluated the applicability of machine learning techniques and the Kuz-Ram model for predicting the mean fragmentation size in open-pit mines. The characteristics of the in-situ rock considered here were uniaxial compressive strength, tensile strength, rock factor, and mean in-situ block size. Seventy field datasets that included these characteristics were collected to predict the mean fragmentation size. Deep neural network, support vector machine, and extreme gradient boosting (XGBoost) models were trained using the data. The performance was evaluated using the root mean squared error (RMSE) and the coefficient of determination (r²). The XGBoost model had the smallest RMSE and the highest r² value compared with the other models. Additionally, when analyzing the error rate between the measured and predicted values, XGBoost had the lowest error rate. When the Kuz-Ram model was applied, low accuracy was observed owing to the differences in the characteristics of data used for model development. Consequently, the proposed XGBoost model predicted the mean fragmentation size more accurately than other models. If its performance is improved by securing sufficient data in the future, it will be useful for improving the blasting efficiency at the target site.

• Korean Chemical Engineering Research
• /
• v.58 no.3
• /
• pp.498-498
• /
• 2020

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.38 no.2
• /
• pp.108-119
• /
• 2015

This study intends to propose a non-oriented DEA based game cross-efficiency approach for supplier selection. With a discussion on the choice of DEA models and approaches that are most appropriate for supplier selection, we propose a game cross efficiency model based upon the non-oriented variable returns-to-scale RAM DEA by adapting the existing game cross efficiency model based upon the oriented constant returns-to-scale CCR DEA. We develop the RAM game cross efficiency model and a convergent iterative solution procedure to find the best game cross efficiency scores that constitute a Nash equilibrium. We illustrate the proposed model with two data sets of supplier selection, and demonstrate that significantly different results are obtained when compared with the existing approaches.