• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.103-108
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• 2016

In communication systems the solution of the problem of maximizing the mutual information between the input and output of a channel composed of several subchannels under total power constraint has a waterfilling structure. OFDM and MIMO can be decomposed into parallel subchannels with CSI. Waterfilling solves the problem of optimal power allocation to these subchannels to achieve the rate approaching the channel capacity under total power constraint. In waterfilling, more power is alloted to good channels(high SNR) and less or no power to bad channels to increase the rate of good channels, resulting in channel capacity. Waterfilling finds the exact water level satisfying the power constraint employing an iterative algorithm to estimate and update the water level. In this process computation of partial sums of inverse of square of subchannel gain is repeatedly required. In this paper we reduced the computation time of waterfilling algorithm by replacing the partial sum computation with reference to an array which contains the precomputed partial sums in initialization phase.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.246-249
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• 2007

In numerical analysis for phase change material, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be used without modelling. In this study applicability of neural networks in modelling superheated vapor region of water was examined by comparing with the quadratic spline. neural network consists of an input layer with 2 nodes, two hidden layers and an output layer with 3 nodes. Quadratic spline interpoation method was also applied for comparison. Neural network model revealed smaller percentage error to quadratic spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the superheated range of the steam table.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.2
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• pp.171-182
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• 2015

The economic effects of sewage and wastewater treatment service (SWTS) sector on other sectors have been rarely investigated in the literature. This paper attempts to apply an inter-industry analysis to looking into the economic effects of the SWTS sector. To this end, the most recently published 2012 input-output table is used here. In particular, the SWTS sector is specified as exogeneous to identify the economic effects of the SWTS sector on other sectors. Production-inducing effect, value-added creation effect, and employment-inducing effect are quantified based on demand-driven model. Supply shortage effect and price pervasive effect are also analyzed employing supply-driven model and Leontief price model, respectively. The results show that production-inducing effect and value-added creation effect of a unit of investment or production in SWTS sector are estimated to be 1.7076 and 0.7392, respectively. The employment-inducing effect of one billion of investment or production in the SWTS sector is computed to be 11.0498 persons. The shortage effect of the SWTS sector amounts to 0.8417 won. The overall price effect of the 10% increase in the price of SWTS sector is calculated to be 0.0115%. This quantitative information can be utilized in predicting the economic effects of the SWTS sector-related activities or policy-making.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.45-57
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• 1998

The purpose of this paper is to develop a neural network model in order to forecast flood inflow into the reservoir that has the nature of uncertainty and nonlinearity. The model has the features of multi-layered structure and parallel multi-connections. To develop the model. backpropagation learning algorithm was used with the Momentum and Levenberg-Marquardt techniques. The former technique uses gradient descent method and the later uses gradient descent and Gauss-Newton method respectively to solve the problems of local minima and for the speed of convergency. Used data for learning are continuous fixed real values of input as well as output to emulate the real physical aspects. after learning process. a reservoir inflows forecasting model at flood period was constructed. The data for learning were used to calibrate the developed model and the results were very satisfactory. applicability of the model to the Chungju Mlultipurpose Reservoir proved the availability of the developed model.

• Journal of Environmental Health Sciences
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• v.37 no.3
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• pp.218-225
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• 2011

Objectives: In this paper, a dielectric barrier discharge (DBD) plasma reactor was investigated for degrading the dye Rhodamine B (RhB) in aqueous solutions. Methods: The DBD plasma reactor system in this study consisted of a plasma component [titanium discharge (inner), ground (outer) electrode and quartz dielectric tube], power source, and gas supply. The effects of various parameters such as first voltage (input power), gas flow rate, second voltage (output power), conductivity and pH were investigated. Results: Experimental results showed that a 99% aqueous solution of 20 mg/l Rhodamine B is decolorized following an eleven minute plasma treatment. When comparing the performance of electrolysis and plasma treatment, the RhB degradation of the plasma process was higher that of the electrolysis. The optimum first voltage and air flow rate were 160 V (voltage of trans is 15 kV) and 3 l/min, respectively. With increased second voltage (4 kV to 15 kV), RhB degradation was increased. The higher the pH and the lower conductivity, the more Rhodamine B degradation was observed. Conclusions: OH radical generation of dielectric plasma process was identified by degradation of N, N-dimethyl-4-nitrosoaniline (RNO, indicator of OH radical generation). It was observed that the effect of UV light, which was generated as streamer discharge, on Rhodamine B degradation was not high. Rhodamine B removal was influenced by real second voltage regardless of initial first and second voltage. The effects of pH and conductivity were not high on the Rhodamine B degradation.

• Composite Materials and Engineering
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• v.3 no.3
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• pp.201-220
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• 2021

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

• Korean Journal of Soil Science and Fertilizer
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• v.35 no.3
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• pp.162-168
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• 2002

The silica uptake of rice plant(Oryza sativa L.) and a potential capacity of paddy soil as a source of silica supply for rice plant was studied under field experimental condition. Amount of Si from irrigation water, rainfall, which was a source of silica supply, and percolating rate of water through paddy soil profile as a source of silica loss from paddy soil, was investigated. Rice plants were grown in three different conditions, which included Control as non-fertilized, RDA's recommandation(NPK, Urea-Fused phophate-Potassium chloride=$110-45-57kg\;ha^{-1}$) and RDA's recommandation with supplement of silicate fertilizer(NPK+Si, Urea-Fused phosphate-Potassium chloride+Si=$110\;N-45\;P_2O_5-57\;K_2O+267.2\;Si\;kg\;ha^{-1}$). An amount of silica supply from rainfall was $0.5kg\;ha^{-1}$ and average amount of silica supplied from irrigation water $42.5kg\;ha^{-1}$, ranging from $28.1kg\;ha^{-1}$ to $58.8kg\;ha^{-1}$. Silica amount percolated through the soil profile have uniform trend comparatively showing $62.9kg\;ha^{-1}$ in Control, $64.8kg\;ha^{-1}$ in NPK treatment and $62.9kg\;ha^{-1}$ in NPK+Si treatment. Silica uptaked by Rice plant was $335.6kg\;ha^{-1}$ in Control, $406.6kg\;ha^{-1}$ in NPK+Si treatment and $471.1kg\;ha^{-1}$ in NPK+Si treatment. The difference between an amount of Si input(from rainfall, irrigation water and silicate fertilizer) and an amount of Si output(percolated Si in soil, uptaked Si by rice plant) was $357.4kg\;ha^{-1}$ in control, $412.1kg\;ha^{-1}$ in NPK treatment and $238.2kg\;ha^{-1}$ in NPK+Si treatment. Results of our study imply that paddy soil is a potential pool as a source of Si supply during growing periods of rice plant.

• Korean Journal of Ecology and Environment
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• v.37 no.4 s.109
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• pp.431-435
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• 2004

A nitrate-contaminated groundwater was hydrogeochemically investigated to estimate the factors controlling groundwater quality in an alluvial fan area. Even though monthly groundwater levels increased with monthly rainfalls, the monthly $NO_3^--N$ concentrations in groundwater showed a small variation, mostly exceeding a maximum contaminant level of 10 mg $L^{-1}$ in environmental quality standards for groundwater during 2003. The 2003 annual groundwater recharge was 1,730 mm =20,056 mm-18,326 mm. Where 20,056 mm and 18,326 mm are annual sum of daily increase and decrease in ground water level. However, the annual sum of increase in ground water level (20,056 mm) was approximately 10 times higher than annual rainfall. Moreover, the annual sum of daily ground water level decrease (-18,326mm) showed that a large amount of groundwater was discharged with $NO_3^-$-contamination. Hydrogeochemically, a large amount of groundwater input and output through the alluvial fan area were observed after rainfall with a considerably high concentration of $NO_3^-$. Consequently, this alluvial fan area including forest area reflects on the evidence under the condition of 'nitrogen excess' or 'nitrogen saturation'. In addition, such a large amount of groundwater outflow can cause environmental damage in surface water, associated with $NO_3^-$- contamination. This study also expects that this hydrogeochemical data will be useful for water management.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.2
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• pp.18-35
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• 1993

The water level control system of the steam generator in a pressurized water reactor and its control problems are analysed. In this work the stable control strategy during the low power operation and transient states is studied. To solve the problem, a fuzzy logic control method is applied as a basic algorithm of the controller. The control algorithm is based on the operator's knowledges and the experiences of manual operation for water level control at the compact nuclear simulator set up in Korea Atomic Energy Research Institute. From a viewpoint of the system realization, the control variables and rules are established considering simpler tuning and the input-output relation. The control strategy includes the dynamic tuning method and employs a substitutional information using the bypass valve opening instead of incorrectly measured signal at the low flow rate as the fuzzy variable of the flow rate during the pressure control mode of the steam generator. It also involves the switching algorithm between the control valves to suppress the perturbation of water level. The simulation results show that both of the fine control action at the small level error and the quick response at the large level error can be obtained and that the performance of the controller is improved.

• Journal of the Korean Geotechnical Society
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• v.30 no.5
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• pp.47-54
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• 2014

Although shear wave velocity ($V_s$) is an important design factor in seismic design, the measurement is not usually made in typical field investigation due to time and economic limitations. In the present study, an investigation was made to predict sand $V_s$ based on the standard penetration test (SPT) results by using artificial neural network (ANN) model. A total of 650 dataset composed of SPT-N value ($N_{60}$), water content, fine content, specific gravity for input data and $V_s$ for output data was used to build and train the ANN model. The sensitivity analysis was then performed for the trained ANN to examine the effect of the input variables on the $V_s$. Also, the ANN model was compared with seven existing empirical models on the performance. The sensitivity analysis results revealed that the effect of the SPT-N value on $V_s$ is significantly greater compared to other input variables. Also, when compared with the empirical models using Nash-Sutcliffe Model Efficiency Coefficient (NSE) and Root Mean Square Error (RMSE), the ANN model was found to exhibit the highest prediction capability.