• Water for future
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• v.29 no.4
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• pp.209-221
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• 1996

It may be difficult to make exact estimates of peak discharge or runoff depth of a flood and to establish the proper measurement for the flood protection since water stages or discharges have been rarely measured at small river basins in Korea. Three small catchments in the Su-Young river basin in Pusan were selected for the study areas. Various runoff parameters for the study areas were determined, and runoff analyses were performed using three different runoff models available in literatures; the storage function method, the discrete, linear, input-output model, and the linear reservoir model. The hydrographs calculated by three different methods showed good agreement with the observed flood hydrographs, indicating that the models selected are all capable of sucessfully modeling the flood events for small watersheds. The storage function method gave the best results in spite of its weakness that it could not be applicable to small floods, while the linear reservoir model was found to provide relatively good results with less parameters. The capabilities of simulating flood hydrographs were also evaluated based on the effective rainfall from the storage function parameters, the $\Phi$-index method, and the constant percentage method. For the On-Cheon stream watershed, the storage function parameters provided better estimates of effective rainfall for regenerating flood hydrographs than any others considered in the study. The $\Phi$-index method, however, resulted in better estimates of effective rainfall for the other two study areas.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.195-209
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• 2003

Spatial-Stochastic Neural Networks Model(SSNNM) is used to estimate long-term streamflow in the parallel reservoir groups. SSNNM employs two kinds of backpropagation algorithms, based on LMBP and BFGS-QNBP separately. SSNNM has three layers, input, hidden, and output layer, in the structure and network configuration consists of 8-8-2 nodes one by one. Nodes in input layer are composed of streamflow, precipitation, pan evaporation, and temperature with the monthly average values collected from Andong and Imha reservoir. But some temporal differences apparently exist in their time series. For the SSNNM training procedure, the training sets in input layer are generated by the PARMA(1,1) stochastic model and they covers insufficient time series. Generated data series are used to train SSNNM and the model parameters, optimal connection weights and biases, are estimated during training procedure. They are applied to evaluate model validation using observed data sets. In this study, the new approaches give outstanding results by the comparison of statistical analysis and hydrographs in the model validation. SSNNM will help to manage and control water distribution and give basic data to develop long-term coupled operation system in parallel reservoir groups of the Upper Nakdong River.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.123-139
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• 2011

Web spam has a significant influence on the ranking quality of web search results because it promotes unimportant web pages. Therefore, web search engines need to filter web spam. web spam filtering is a concept that identifies spam pages - web pages contributing to web spam. TrustRank, Anti-TrustRank, Spam Mass, and Link Farm Spam are well-known web spam filtering algorithms in the research literature. The output of these algorithms depends upon the input seed. Thus, refinement in the input seed may lead to improvement in the quality of web spam filtering. In this paper, we propose seed refinement techniques for the four well-known spam filtering algorithms. Then, we modify algorithms, which we call modified spam filtering algorithms, by applying these techniques to the original ones. In addition, we propose a strategy to achieve better quality for web spam filtering. In this strategy, we consider the possibility that the modified algorithms may support one another if placed in appropriate succession. In the experiments we show the effect of seed refinement. For this goal, we first show that our modified algorithms outperform the respective original algorithms in terms of the quality of web spam filtering. Then, we show that the best succession significantly outperforms the best known original and the best modified algorithms by up to 1.38 times within typical value ranges of parameters in terms of recall while preserving precision.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.6 s.348
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• pp.9-17
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• 2006

We proposed a new High-speed CMOS Level Up/Down Shifter circuits that can be used with Dynamic Voltage and Frequency Scaling(DVFS) algorithm, for low power system in the SoC(System-on-Chip). This circuit used to interface between the other voltage levels in each CMOS circuit boundary, or between multiple core voltage levels in a system bus. Proposed circuit have advantage that decrease speed attenuation and duty ratio distortion problems for interface. The level up/down shifter of the proposed circuit designed that operated from multi core voltages$(0.6\sim1.6V)$ to used voltage level for each IP at the 500MHz input frequency The proposed circuit supports level up shifting from the input voltage levels, that are standard I/O voltages 1.8V, 2.5V, 3.3V, to multiple core voltage levels in between of $0.6V\sim1.6V$, that are used internally in the system. And level down shifter reverse operated at 1Ghz input frequency for same condition. Simulations results are shown to verify the proposed function by Hspice simulation, with $0.6V\sim1.6V$ CMOS Process, $0.13{\mu}m$ IBM CMOS Process and $0.65{\mu}m$ CMOS model parameters. Moreover, it is researched delay time, power dissipation and duty ration distortion of the output voltage witch is proportional to the operating frequency for the proposed circuit.

• Korean Journal of Clinical Pharmacy
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• v.9 no.1
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• pp.49-54
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• 1999

This study was carried out to compare the bioavailability of Hanmi clarithromycin (250 mg/tablet) with that of $Klaricid^{(R)}$ The bioavailability was examined on 20 volunteers who received a single dose (500 mg) of each drug in the fasting state in a randomized balanced 2-way crossover design. After dosing, blood samples were collected for a period of 12 hours. Plasma samples were analyzed for clarithromycin and roxithromycin(internal standard) by HPLC/Coulometric BCD. The pharmaco-kinetic parameters ($AUC_{0-l2hr}$, Cmax, Tmax, $AUC_{inf}$, Ka, Kel, $t_{1/2}$, Vd/F and Cl/F) were calculated from the plasma clarithromycin concentration-time data of each volunteer. The computer program 'WinNonlin' was used for compartmental analysis. One compartment model with first-order input, from order output with lag time, weighting factor $l/y^2$ was chosen as the appropriate pharmacokinetic model. The major pharmacokinetic parameters ($AUC_{0-l2hr},\;AUC_{inf}$, Cmax and Tmax) of Hanmi clarithromycin were $10.7\pm0.5\;{\mu}g{\cdot}hr{\cdot}ml^{-1},\;12.7\pm0.7\;{\mu}g{\cdot}hr{\cdot}ml^{-1},\;1.7\pm0.1\;{\mu}g/ml\;and\;2.0\pm0.2\;hr$, respectively, and those of $Klaricid^{(R)}\;were\;9.8\pm0.5\;{\mu}g{\cdot}hr{\cdot}ml^{-1},\;11.7\pm0.6\;{\mu}g{\cdot}hr{\cdot}ml^{-1},\;1.6\pm0.1\;{\mu}g/ml\;and\;2.1\pm0.1\;hr$, respectively. The differences in mean values of $AUC_{0-l2hr},\;AUC_{inf}$ and Cmax between two products were $9.88\%,\;8.94%\;and\;6.59\%$, respectively. The least significant differences at $\alpha=0.05$ for $AUC_{0-l2hr},\;AUC_{inf}$ and Cmax were $16.08\%,\;17.81\%\;and\;18.94\%$, respectively. Though the plasma clarithromycin concentrations of Hanmi clarithromycin were higher than those of $Klaricid^{(R)}$ at all observed times, the bioavailability of Hanmi clarithromycin appeared to be bioequivalent with that of $Klaricid^{(R)}$. The Ka, Kel, $t_{1/2}$, Vd/F and Cl/F of the Hanmi clarithromycin were $2.69\pm0.53\;hr^{-1},\;0.18\pm0.01 hr^{-1},\;3.9\;hr,\;248.8\pm11.4\;L\;and\;43.7\pm2.6\;L/hr$, respectively, and those of $Klaricid^{(R)} were 2.19\pm0.51\;hr^{-1},\;0.18\pm0.02\;hr^{-1},\;3.7\;hr,\;266.7\pm22.4\;L\;and\;45.3\pm2.8L/hr$, respectively. There were no statistically significant differences between two drugs in all pharmacokinetic parameters.

• Horticultural Science & Technology
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• v.34 no.6
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• pp.860-870
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• 2016

Continuous monitoring of environmental parameters provides farmers with useful information, which can improve the quality and productivity of crops grown in greenhouses. The objective of this study was to develop a greenhouse environment measurement system using a low-cost microcontroller with open-source software. Greenhouse environment parameters measured were air temperature, relative humidity, and carbon dioxide ($CO_2$) concentration. The ranges of the temperature, relative humidity, and $CO_2$ concentration were -40 to $120^{\circ}C$, 0 to 100%, and 0 to 10,000 ppm, respectively. A $128{\times}64$ graphic LCD display was used for real-time monitoring of the greenhouse environments. An Arduino Uno R3 consisted of a USB interface for communicating with a computer, 6 analog inputs, and 14 digital input/output pins. A temperature/relative humidity sensor was connected to digital pins 2 and 3. A $CO_2$ sensor was connected to digital pins 12 and 13. The LCD was connected to digital pin 1 (TX). The sketches were programmed with the Arduino Software (IDE). A measurement system including the Arduino board, sensors, and accessories was developed (totaling $244). Data for the environmental parameters in a venlo-type greenhouse were obtained using this system without any problems. We expect that the low-cost microcontroller using open-source software can be used for monitoring the environments of plastic greenhouses in Korea.

• Advances in concrete construction
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• v.13 no.2
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• pp.123-132
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• 2022

This paper aims to adapt Multilinear regression (MLR) to predict the strength and toughness of SIFCON containing various pozzolanic materials. Slurry Infiltrated Fibrous Concrete (SIFCON) is one of the most common terms used in concrete manufacturing, known for its benefits such as high ductility, toughness and high ultimate strength. Assessment of compressive strength (CS.), flexural strength (F.S.), splitting tensile strength (STS), dynamic elasticity modulus (DME) and impact energy (I.E.) using the experimental approach is too costly. It is time-consuming, and a slight error can lead to a repeat of the test and, to solve this, alternative methods are used to predict the strength and toughness properties of SIFCON. In the present study, the experimentally investigated SIFCON data about various mix proportions are used to predict the strength and toughness properties using regression analysis-multilinear regression (MLR) models. The input parameters used in regression models are cement, fibre, fly ash, Metakaolin, fine aggregate, blast furnace slag, bottom ash, water-cement ratio, and the strength and toughness properties of SIFCON at 28 days is the output parameter. The models are developed and validated using data obtained from the experimental investigation. The investigations were done on 36 SIFCON mixes, and specimens were cast and tested after 28 days of curing. The MLR model yields correlation between predicted and actual values of the compressive strength (C.S.), flexural strength, splitting tensile strength, dynamic modulus of elasticity and impact energy. R-squared values for the relationship between observed and predicted compressive strength are 0.9548, flexural strength 0.9058, split tensile strength 0.9047, dynamic modulus of elasticity 0.8611 for impact energy 0.8366. This examination shows that the MLR model can predict the strength and toughness properties of SIFCON.

• Korea Journal of Hospital Management
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• v.28 no.2
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• pp.44-65
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• 2023

Purposes: As super-aging population and low fertility rates are threatening the sustainability of the National Health Insurance funds, enhancing the efficiency of hospital management is paramount. In the past, studies analyzing the efficiencies of hospitals primarily made inter-hospital comparisons, but it is important to assess hospitals' internal efficiency and develop improvement measures in order to attain practical improvements in hospital efficiencies. The purpose of this study is to analyze the efficiencies of specialists by medical specialty in a hospital in order to provide foundational data for efficient hospital management. Methodology/Approach: We used the activity-based costing (ABC) data and hospital statistical data from one tertiary hospital in Seoul to analyze the efficiency of specialists by medical specialty. Efficiency was analyzed and compared among specialists using the data envelopment analysis developed by Charnes, Cooper, and Rhodes (DEA-CCR) model and the slacks-based measure (SBM) models. The input variables were labor cost, material cost, and operational expenses, and the output variables were the number of outpatients, number of inpatients, outpatient revenue, and inpatient revenue. Findings: First, there was a marked deviation in efficiency across specialists. Second, there was a marked deviation in efficiency across medical specialties. Third, there was little difference in efficiency according to the specialist's sex, age, and job position. Fourth, the SBM model produced more conservative results and better explained efficiency parameters than the CCR model. Practical Implications: The efficiency of a specialist was more influenced by their medical specialty than their personal characteristics, namely sex, age, and job position. Therefore, Further research is needed to analyze the efficiencies of each subspecialty and identify factors that contribute to the variations in efficiencies across medical specialties, such as clinical practices and fee structures.

• Computers and Concrete
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• v.19 no.3
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.