• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.2
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• pp.75-86
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• 1996

The formulas for estimating the constants of storage function model including K and TL for runoff analysis and a distributed storage function model are discussed in this study. First, the relations between parameters of the storage function model and the kinematic runoff model are theoretically examined, and then optimum constants of storage function model are obtained by the Standardized Davidson-Fletcher-Powell (SDFP) method. Through this analysis, theoretical formulas were obtained as $K = 0.63 {\alpha} KsB{^0.6}$ and $T_{L}=0.11 {\alpha} KsB{^0.6} r{^0.4} {_e}$, which are difficult to use practically because of the unclarified definition of shape factors. From a practical point of view, empirical formula were derived as $K=15.6{^0.3} {_m}$ and $T_{L}=2.1B{^0.36} {_m} {_e}/r{^0.4} {_e}$ for applied watersheds. The proposed formulas are verified for several recoded floods at a few points of watersheds. It is also found that the distributed storage function. can be applied to flood runoff analysis using the new formulas aboved mentioned.

• Journal of Industrial Technology
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• v.18
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• pp.51-59
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• 1998

To flood forecastion, until now, Storage function method, Streamflow Synthesis and Reservoir Regulation, and HEC-1 model have been analysed generally in various definite simulation. Generally, Streamflow Synthesis and Reservoir Regulation and HEC-1 model are more delicacy and more excellent model than Storage function method in physically. But the resource huge for test of models. On the contrary, Storage function method has not only a few model various and data for decision but also has poor theory background in model excessively simpled water circulation about a basin. In this reason, this study is purpose to develop a statistical flood forecasting model that can forecast with accuracy variety of water height to Nak-Dong river vibration spots in flood with accumulated water resource.

• Transactions of the Society of Information Storage Systems
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• v.10 no.2
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• pp.50-54
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• 2014

Object image using DRPE(Double Random Phase Encryption) in 4f system is encrypted by space-division method using amplitude mask. However, this method has the weakness for the case of having partial data of amplitude mask which can access the original image. To improve the security, we propose the method using the 2-dimension logistic chaos function which shuffles the encrypted data. It is shown in simulation results that the proposed method is highly sensitive to chaos function parameters. To properly decrypt from shuffled encryption data, below 1e-5 % errors of each parameter should be required. Thus compared with conventional method the proposed shows the higher security level.

• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.689-699
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• 2000

This paper presents the integrated storage function model (ISFM) to improve the accuracy of the storage function model (SFM) which is widely employed for flood runoff analysis and its forecasting in Korea. In order to achieve this objective, the optimization method is applied for estimation of parameters of the model which dominate the accuracy of the analysis, which is usually taken by empirical formulae, and they are treated as time dependent variables. The fuzzy control technique is used to detennine the time variant parameters. In addition, the ISFM can be applied to the combined routing of the watershed and the channel with a residual watershed.ershed.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.2
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• pp.149-158
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• 2008

The parameters of each basin, required for the accurate analysis of flood runoff using Storage Function Model, are estimated. Prior to the estimation, sensitivity analysis and extraction of new regional topographic factors for Han River basin are conducted. Based on the result, the outflow constant of basin model is calculated through regression analysis in relation with pre-flood runoff depth. The storage constant of basin model is derived by the optimum storage constant equation, according to the flood event of each basin. The model using the mentioned parameters was compared with K-Water model of Korea Water Resources Corporation and the model of Han River Flood Control Office, and proved to correspond to the observed hydrograph more.

• Journal of Korean Society for Quality Management
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• v.41 no.1
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• pp.135-148
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• 2013

Purpose: This paper presents a method to evaluate the stockpile reliability of propelling charge for performance and storage safety with storage time. Methods: We consider a performance failure level is the amount of muzzle velocity drop which is the maximum allowed standard deviation multiplied by 6. The lifetime for performance is estimated by non-linear regression analysis. The state failure level is assumed that the content of stabilizer is below 0.2%. Because the degradation of stabilizer with storage time has both distribution of state and distribution of lifetime, it must be evaluated by stochastic process method such as gamma process. Results: It is estimated that the lifetime for performance is 59 years. The state distribution at each storage time can be shown from probability density function of degradation. It is estimated that the average lifetime as $B_{50}$ life is 33 years from cumulative failure distribution function curve. Conclusion: The lifetime for storage safety is shorter than for performance and we must consider both the lifetime for storage safety and the lifetime performance because of variation of degradation rate.

• Journal of Environmental Science International
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• v.2 no.3
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• pp.193-199
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• 1993

This study has two objectives. One is developing the runoff model for Hoe-Dong Reservoir basin located at the upstream of Su-Young River in Pusan. To develop the runoff model, basic hydrological parameters - curve number to find effective rainfall, and storage coefficient, etc. - should be estimated. In this study, the effective rainfall was calculated by the SCS method, and the storage coefficient used in the Clark watershed routing was cited from the report of P.E.B. The other is the derivation of transfer function for Hoe-Dong Reservoir basin. The linear, discrete, input-output model which contained six parameters was selected, and the parameters were estimated by the least square method and the correlation function method, respectively. Throughout this study, rainfall and flood discharge data were based on the field observation in 1981.8.22 - 8.23 (typhoon Gladys). It was observed that the Clark watershed routing regenerated the flood hydrograph of typhoon Gladys very well, and this fact showed that the estimated hydrological parameters were relatively correct. Also, the calculated hydrograph by the linear, discrete, input-output model showed good agreement with the regenerated hydrograph at Hoe-Dong Dam site, so this model can be applicable to other small urban areas. Key Words : runoff, effective rainfall, SCS method, clark watershed iou상ng, hydrological parameters, parameter estimation, least square method, correlation function method, input-output model, typhoon gladys.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.5
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• pp.619-624
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• 2005

This work is for examining a simplified equation based on the rational formula, which can easily decide storm-water detention volume in small urban catchments. The storm-water detention volume is determined by the inflow hydrograph flowing to detention basin and the outflow hydrograph discharged from the detention basin. The ratio of average outflow over the period of rainfall duration against allowable discharge was 0.5 in former simplified equation. But this research has found that the average outflow ratio depends on the storage methodology. In the case of the on-line storage method, the average outflow ratio is a function of the time of concentration of the catchments and rainfall duration, which ranged from 0.5~1.0. In the case of the off-line storage method, the average ratio is a function of peak discharge and allowable discharge except above time of concentration and rainfall duration, where its function value ranged from 1.0~2.0. When applying this equation to small catchment in Mokpo city, South Korea, we could easily calculate the relation curve between the storm-water detention volume and allowable discharge.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.56-64
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• 1998

Multi-layered perceptrons that are a nonlinear neural network model, have been widely used for various applications mainly thanks to good function approximation capability for nonlinear fuctions. However, for digital hardware implementation of the multi-layere perceptrons, the quantization scheme using "look-up tables (LUTs)" is commonly employed to handle nonlinear signmoid activation functions in the neworks, and thus requires large amount of storage to prevent unacceptable quantization errors. This paper is concerned with a new effective methodology for digital hardware implementation of multi-layered perceptrons, and proposes a "piecewise affine approximation" method in which input domain is divided into (small number of) sub-intervals and nonlinear sigmoid function is linearly approximated within each sub-interval. Using the proposed method, we develop an expression and an error backpropagation type learning algorithm for a multi-layered perceptron, and compare the performance with the quantization method through Monte Carlo simulations on XOR problems. Simulation results show that, in terms of learning convergece, the proposed method with a small number of sub-intervals significantly outperforms the quantization method with a very large storage requirement. We expect from these results that the proposed method can be utilized in digital system implementation to significantly reduce the storage requirement, quantization error, and learning time of the quantization method.quantization method.

• Bulletin of the Korean Mathematical Society
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• v.53 no.2
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• pp.589-600
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• 2016

In this paper, we propose a new incremental gradient method for solving a regularized minimization problem whose objective is the sum of m smooth functions and a (possibly nonsmooth) convex function. This method uses an adaptive stepsize. Recently proposed incremental gradient methods for a regularized minimization problem need O(mn) storage, where n is the number of variables. This is the drawback of them. But, the proposed new incremental gradient method requires only O(n) storage.