• Journal of the Korean Society for Precision Engineering
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• v.20 no.6
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• pp.167-177
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• 2003

In this study, a mathematical nonlinear dynamic model is introduced to predict the damping force of automotive shock absorber. And 11 design parameters were proposed for the sensitivity analysis of damping force. Design parameters consist of 5 piston valve design parameters, 5 body valve design parameters and 1 initial pressure of reservoir chamber air. All of these design parameters are main design parameters of shock absorber in the procedure of shock absorber design. The simulation results of this paper offer qualitative information of damping force variation according to variation of design parameters. Therefore, simulation results of this paper can be usefully use in the design procedure of shock absorber

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Journal of Korea Water Resources Association
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• v.30 no.6
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• pp.679-690
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• 1997

The risk assessment model for hydrlolgic safety analysis of dam and levee in developed by using Monte-Carlo and AFOSM (Advanced First-Order Second-Moment) method. The fault tree analysis and four phases approach are presented for the safety eveluation of risk of dam and levee. The risk model consists of rainfall-runoff analysis, reservoir routing and channel routing considering the variations in the model parameter. For the rainfall-runoff analysis, KRRL method is adopted with 200-year precipitation and PMP (Probable Maximum Precipitation). Reservoir routing is performed by fourth order Runge-Kutta method and channel routing by standard step method. The suggested model will contribute to safety evaluation of dam and levee and their rehabilitation decision problem.

• Journal of the Korean GEO-environmental Society
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• v.4 no.3
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• pp.69-77
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• 2003

This study was carried out to evaluate the WASP5 model parameters and to analyze the sensitivity of parameters in Daechung Reservoir. The values predicted by the model and tendency were very similar to the observed data at Daejeon intake, so it is possible to predict water quality of the Daejeon intake region in the future. Results from the sensitivity analysis showed that Chlorophyll-a was sensitive to variations in saturated growth rate of phytoplankton, endogenous respiration rate of phytoplankton, extinction coefficient and temperature. T-N was sensitive to mineralization rate of dissolved organic nitrogen and temperature. T-P was affected by T-P load, temperature, extinction coefficient, mineralization rate of dissolved organic phosphorus and saturated growth rate of phytoplankton. BOD was influenced by deoxygenation rate and temperature, and DO was influenced by temperature. Adequate input data was applied and assessed through the model sensitivity analysis. So it is possible to distinguish the input data which need careful attention when it has application to model.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.67-75
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• 2004

In this study, real-time forecasting model(Neural Dam Inflow Forecasting Model; NDIFM) based on neural network to predict the dam inflow which is occurred by flood runoff is developed and applied to check its availability for the operation of multi-purpose reservoir Developed model Is applied to predict the flood Inflow on dam Nam-Gang in Nak-dong river basin where the rate of flood control dependent on reservoir operation is high. The input data for this model are average rainfall data composed of mean areal rainfall of upstream basin from dam location, observed inflow data, and predicted inflow data. As a result of the simulation for flood inflow forecasting, it is found that NDIFM-I is the best predictive model for real-time operation. In addition, the results of forecasting used on NDIFM-II and NDIFM-III are not bad and these models showed wide range of applicability for real-time forecasting. Consequently, if the quality of observed hydrological data is improved, it is expected that the neural network model which is black-box model can be utilized for real-time flood forecasting rather than conceptual models of which physical parameter is complex.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.533-541
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• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Journal of the Korean Geotechnical Society
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• v.26 no.4
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• pp.47-58
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• 2010

Seepage rate through the core zone of rockfill dam, estimated from graphical technique and the equation by Sakamoto (1998), is different from the real condition because of neglecting unsaturated flow. With existing method to estimate total seepage rate, it is difficult to understand the tendency of total seepage rate changes by reservoir water level change. Steady state seepage rate and the factors affecting the time needed to attain to changes of reservoir water level and saturated hydraulic conductivity and unsaturated hydraulic properties of core material are analysed thorough the 2-D steady and unsteady state seepage analyses of Soyanggang dam. Numerical results revealed that the seepage rate can be expressed by the linear equation form and the value of unsaturated soil parameter n is the most important factor affecting the seepage rate and the time needed to attain steady state. The estimation method presented in this study can be used by the designer and the personnel of dam safety for convenient estimation of seepage rate and quantitative analysis of measured seepage rate without 2-D and 3-D numerical analyses.

• Korean Journal of Ecology and Environment
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• v.39 no.1 s.115
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• pp.41-51
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• 2006

The influence of land use and pollutant source on water quality was investigated using 3-yrs monitoring data major influent stream in Hwaong reservoir watershed. The seven water quality station (N1, N2, J1, J3, J4, E2, E3) were used analysis of land use and pollutant effect, and six water quality station (N3, N4, J2, J5, E1, E4) were used analysis of waster quality status. Water quality parameter were positively correlated with residential and forest, negatively with paddy and upland especially during dry period. During wet period, correlation between land use and water quality was less apparent. Population and livestock density was correlated well to water quality parameter than just number of population and livestock. The watersheds studied are mainly non-urban and their land uses are similar to typical watershed of other estuarine reservoirs, therefore, the correlation developed in this study might be helpful to manage other estuarine reservoir watersheds.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1334-1343
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• 2008

Growing needs for efficient management of water resources urge the joint operation of dams and integrated management of whole basin. As one of the tools for supporting above tasks, this study aims to constitute a hydrologic model that can simulate the streamflow discharges at some control points located both upper and down stream of dams. One of the currently available models is being studied to be applied with a least effort in order to support the ongoing project of KWATER (Korea Water Resources Corporation), "Establishment of integrated operation scheme for the dams in Han River Basin". On this study, following works have been carried out : division of Han River Basin into 24 sub-basins, use of rainfall data of 151 stations to make spatial distribution of rainfall, selection of control points such as Soyanggang Dam, Chungju Dam, Chungju Release Control Dam, Heongseong Dam, Hwachun Dam, Chuncheon Dam, Uiam Dam, Cheongpyung Dam and Paldang Dam, selection of SSARR (Streamflow Synthesis and Reservoir Regulation) model as a hydrologic model, preparation of input data of SSARR model, sensitivity analysis of parameter using hydrologic data of 2002. The sensitivity analysis showed that soil moisture index versus runoff percent (SMI-ROP), baseflow infiltration index versus baseflow percent (BII-BFP) and surface-subsurface separation (S-SS) parameters are higher sensitive parameters to the simulation result.