• Water for future
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• v.26 no.2
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• pp.99-105
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• 1993

A practical flood forecasting model(FFM) is suggested. The output of the model is the results which the initial condition of meteorological parameters and soil moisture are projected on the future. The physically based station model for rainfall forecasting(RF) and the storage function model for runoff prediction(RP) are adopted respectively. Input variables for FFM are air temperature, pressure, and dew-point temperature at the ground level and the flow at the rising limb(FRL). The constant parameters for FFM are average of optimum values which the past storm events have. Also loss rate of rainfall can predicted by FRL.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.255-262
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• 2000

Shaking table tests were peformed to evaluate the subgrade reaction of ground according to the build-up of pore water pressure. Model pile was installed in the sand ground. The acceleration of the model ground, the pore water pressure build-up and displacement of pile were recorded by measuring devices. Subgrade reaction approach based on Winker soil model was applied to obtain the modulus of the horizontal subgrade reaction. The results of analysis show that the reduction factor of the subgrade reaction due to pore pressure increase is about 1 and the horizontal subgrade reaction of liquefied ground is not influenced by the stiffness of pile, a ground acceleration and the intial ground density.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.5
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• pp.43-49
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• 2005

In this study. it was suggested that the elastic model to analyze the behavior of pore water pressure in saturated sand specimen on the condition of non-drainage. The model based on the experiments which were performed for the relationships between the pore water pressure and the grain size of specimen, and effective stress, respectively. The suggested model embodied the pore water and soil grain as separate elastic springs of different stiffness. The springs were joined parallel and the axial strains were restricted to the same deformation. The suggested model was well consistent with the experiments.

• Journal of Biosystems Engineering
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• v.18 no.3
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• pp.220-229
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• 1993

A dimensional analysis was carried out to investigate if model agricultural radial tire can predict the tractive performance of prototype tires. Experimental data was analyzed to prove the results of dimensional analysis. The results was summerized as follows ; 1. When the model and prototype tires are tested under the same soil conditions, inflation pressure, slip and dynamic load, traction coefficient ratio between two tires depend on the geometry of two tires. 2. According to the regression analysis of the experimental data, traction equation parameters of the prototype tires can be predicted from the that of model tire 3. Predicted traction coefficient of prototype tire, calculated from the traction equation paramters, showed good correlation with that of experimental results. Thus it was possible to predict net and gross traction of prototype tire from the model traction equation parameters.

• Journal of Industrial Technology
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• v.12
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• pp.15-23
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• 1992

This research is to investigate the effect of base roughness of footing on characteristics of load-settlement curve. Parametric experiments of small scaled model test were performed with changing the properties of base roughness of model footing; Gluing the vinyl, aluminum, sand paper, sand beneath the model footing surface. The width of model footing and relative density of soil foundation were also changed to investigate their effects on settlement characteristics of footing. The ultimate bearing capacity as well as the initial slope of load-settlement curve obtained from test results were compared with those from limit equilibrium methods proposed by Terzaghi, Hansen and Meyerhof. From test results, it was confirmed that the base roughness affected the failure mechanisms of showing different shapes of slip lines formed beneath the footing.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.350-353
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• 2011

전 세계적으로 기후변화로 인한 자연재해가 빈번하게 발생함에 따라 수자원 분야에서 또한 환경의 변화에 대한 정확한 예측이 더욱 요구되고 있다. 국내에서도 이를 위하여 다양한 방법을 통하여 연구가 이루어지고 있으나 본 연구에서 사용된 Common Land Model (CLM)은 국내에서의 실질적인 적용이 아직 부족하다. 이 모형은 Soil-Vegetation-Atmosphere Transfer 모형 중 대표적 모델로 Land Surface Model (LSM), Biosphere-Atmosphere Transfer Scheme (BATS), Chinese Academy of Sciences Institute of Atmospheric Physics LSM의 세 모형이 결합되어 발전하였다. CLM의 강제입력자료로는 위성, 지면모형 등을 기반으로 만들어진 자료를 제공하는 Korea Land Data Assimilation Systme (KLDAS; 한반도지표자료동화체계)의 격자화 된 자료를 사용하여 모형에 강제시켰다. KLDAS는 기존의 Land Data Assimilation System (LDAS)에서 발전한 형태로 동아시아 지역을 대상으로 자료를 제공하고 있으며, 본 연구에서는 이 자료를 사용하여 국내 전반에 걸쳐 격자에 대한 수문 기상학적 인자를 산출하였다.

• Journal of Korea Water Resources Association
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• v.33 no.S1
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• pp.20-27
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• 2000

The grid-based KIneMatic wave STOrm Runoff Model(Kim, 1998; Kim, et al., 1998) which predicts temporal variation and spatial distribution of saturated overland flow, subsurface flow and stream flow was evaluated at two watersheds. this model adopts the single overland flowpath algorithm and simulates surface and/or subsurface water depth at each cell by using water balance of hydrologic components. the model programmed by C-language uses ASCII-formatted map data supported by the irregular gridded map of the GRASS (Geographic Resources Analysis Support System) GIS and generates the spatial distribution maps of discharge, flow depth and soil moisture of the watershed.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.67-72
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• 2000

A dynamic draft model is necessary to analyze mechanics of tillage and to design optimal tillage tools. In order to deal with draft dynamics, a neural network paradigm was applied to develop dynamic draft models. For the development of the models, three kinds of tillage tools were used to measure drafts in the soil bin and a time lagged recurrent neural network was developed. The neural network had a structure to predict dynamic draft, having a function of one-step-ahead prediction. A procedure for network prediction model identification was established. The results show promising modeling of the dynamic drafts with developed neural network.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.2
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• pp.133-144
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• 1996

To get accurate results, the realistic stress-strain relationships of soils are dependent on a number of factors such as soil types, density, stress levels and stress path. Such attempts are continuously being made by the developement of analytical models for soils incorporating all such factors. Isotropic compression-expansion test and a series of drained conventional triaxial tests with several stress path for Baekma river sand were performed to investigate parameters characteristics of Lade's single work hardening model dependant on the stress path. Using the computer program based on the regression analysis, the values of parameters for the model were determined. In conclusion, the parameters of Lade's model are little influenced by the stress paths. Though yield criterion parameters ( h, ${\alpha}$a) are much influenced by stress level and stress path, the parameters don't have influence on stress-strain behavior.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.122-127
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• 1998

This paper presents the neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and developed the classification software based on the neuro-fuzzl model. Also, a comparison of the neuro-fuzzy and maximum-likelihood classifiers is presented in this paper. The Airborne Multispectral Scanner(AMS) imagery of Tae-Duk Science Complex Town were used for this comparison. The neuro-fuzzy classifier was more considerably accurate in the mixed composition area like "bare soil" , "dried grass" and "coniferous tree", however, the "cement road" and "asphalt road" classified more correctly with the maximum-likelihood classifier than the neuro-fuzzy classifier. Thus, the neuro-fuzzy model can be used to classify the mixed composition area like the natural environment of korea peninsula. From this research we conclude that the neuro-fuzzy classifier was superior in suppression of mixed pixel classification errors, and more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover signatures.