• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.21-29
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• 2003

It is very important to reduce the computational time in estimating the parameters of vision control algorithm for robot's position control in real time. Unfortunately, the batch estimation commonly used requires too murk computational time because it is iteration method. So, the batch estimation has difficulty for robot's position control in real time. On the other hand, the Extended Kalman Filtering(EKF) has many advantages to calculate the parameters of vision system in that it is a simple and efficient recursive procedures. Thus, this study is to develop the EKF algorithm for the robot's vision control in real time. The vision system model used in this study involves six parameters to account for the inner(orientation, focal length etc) and outer (the relative location between robot and camera) parameters of camera. Then, EKF has been first applied to estimate these parameters, and then with these estimated parameters, also to estimate the robot's joint angles used for robot's operation. finally, the practicality of vision control scheme based on the EKF has been experimentally verified by performing the robot's position control.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.233-243
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• 2010

A method is proposed of estimating Muskingum-Cunge parameters for natural streams using cross-sectional and longitudinal channel geometry and roughness coefficient data. Firstly, for various water-surface levels at a cross section cross-sectional areas and hydraulic radii are calculated. Corresponding discharges are then calculated using Manning's equation. This procedure is repeated for all cross-sections in the reach. Finally, routing parameters are estimated from the calculated cross-sectional area and discharge value pairs by regression analysis. The procedures for estimating Muskingum-Cunge parameters are applied to the South Han River. Flows calculated by Muskingum-Cunge model with estimated parameters showed much better agreement with those by dynamic wave model in peak discharge, time to peak discharge, and normalized RMS errors than those calculated by the HEC-1 Muskingum-Cunge model.

• Journal of Environmental Science International
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• v.24 no.4
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• pp.437-447
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• 2015

Estimation of runoff peak is needed to assess water availability, in order to support the multifaceted water uses and functions, hence to underscore the modalities for efficient water utilization. The magnitude of storm rainfall acts as a primary input for basin level runoff computation. The rainfall-runoff linkage plays a pivotal role in water resource system management and feasibility level planning for resource distribution. Considering this importance, a case study has been carried out in the Hancheon basin of Jeju Island where distinctive hydrological characteristics are investigated for continuous storm rainfall and high permeable geological features. The study aims to estimate unit hydrograph parameters, peak runoff and peak time of storm rainfalls based on Clark unit hydrograph method. For analyzing observed runoff, five storm rainfall events were selected randomly from recent years' rainfall and HEC-hydrologic modeling system (HMS) model was used for rainfall-runoff data processing. The simulation results showed that the peak runoff varies from 164 to 548 m3/sec and peak time (onset) varies from 8 to 27 hours. A comprehensive relationship between Clark unit hydrograph parameters (time of concentration and storage coefficient) has also been derived in this study. The optimized values of the two parameters were verified by the analysis of variance (ANOVA) and runoff comparison performance were analyzed by root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) estimation. After statistical analysis of the Clark parameters significance level was found in 5% and runoff performances were found as 3.97 RMSE and 0.99 NSE, respectively. The calibration and validation results indicated strong coherence of unit hydrograph model responses to the actual situation of historical storm runoff events.

• Geomechanics and Engineering
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• v.10 no.3
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• pp.257-267
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• 2016

Charts are used extensively in slope practical application to meet the need of quick assessment of rock slope design. However, Charts for estimating the shear strength of the rock mass of a slope are considerably limited. In this paper, based on the Hoek-Brown (HB) criterion which is widely used in rock slope engineering, we present charts which can be used to estimate the Mohr-Coulomb (MC) parameters angle of friction ${\phi}$ and cohesion c for given slopes. In order to present the proposed charts, we firstly present the derivation of the theoretical relationships between the MC parameters and ${\sigma}_{ci}/({\gamma}H)$ which is termed the strength ratio (SR). It is found that the values of $c/{\sigma}_{ci}$ and ${\phi}$ of a slope depend only on the magnitude of SR, regardless of the magnitude of the individual parameters ${\sigma}_{ci}$(uniaxial compressive strength), ${\gamma}$(unit weight) and H (slope height). Based on the relationships between the MC parameters and SR, charts are plotted to show the relations between the MC parameters and HB parameters. Using the proposed charts can make a rapid estimation of shear strength of rock masses directly from the HB parameters, slope geometry and rock mass properties for a given slope.

• Corrosion Science and Technology
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• v.19 no.4
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• pp.174-188
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• 2020

Atmospheric corrosion is a natural surface degradation process of metal due to changes in environmental parameters in the surrounding atmosphere. It is very sensitive to environmental parameters such as temperature, relative humidity, sulphur dioxide, and chloride, making it a major global economic challenge. Existing forecasting empirical corrosion models including the ISO standard are based on statistical analysis of experimental studies without considering the behavior of atmospheric parameters. The present study proposes a reliable global empirical model for estimating short and long-term atmospheric corrosion rates based on environmental parameters and corrosion mechanisms obtained from a parametric study. Repercussion of atmospheric corrosion rate due to individual and combined influences of environmental parameters specifies their importance in the estimation. New global empirical coefficients obtained for environmental parameters are statistically established (R² =0.998) with 95% confidence limit. They are validated using experimental datasets of existing studies observed at 88 different continental locations. The current proposed model can predict atmospheric corrosion by means of corrosion formation mechanisms influenced by combined effects of environmental parameters, further abating applicability limitations of location and time.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1041-1052
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• 2013

In this study, we estimate parameters of a distributed hydrologic model, GRM (grid based rainfall-runoff model), using a model-independent parameter estimation tool, PEST. We implement auto calibration of model parameters such as initial soil moisture, multipliers of overland roughness and soil hydraulic conductivity in the Geumho River Catchment and the Gamcheon Catchment using radar rainfall estimates and ground-observed rainfall represented by Thiessen interpolation. Automatic calibration is performed by GRM-MP (multiple projects), a modified version of GRM without GUI (graphic user interface) implementation, and "Parallel PEST" to improve estimation efficiency. Although ground rainfall shows similar or higher cumulative amount compared to radar rainfall in the areal average, high spatial variation is found only in radar rainfall. In terms of accuracy of hydrologic simulations, radar rainfall is equivalent or superior to ground rainfall. In the case of radar rainfall, the estimated multiplier of soil hydraulic conductivity is lower than 1, which may be affected by high rainfall intensity of radar rainfall. Other parameters such as initial soil moisture and the multiplier of overland roughness do not show consistent trends in the calibration results. Overall, calibrated parameters show different patterns in radar and ground rainfall, which should be carefully considered in the rainfall-runoff modelling applications using radar rainfall.

• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.68-76
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• 2014

The accurate estimation of construction cost is important to a successful development in construction projects. In previous studies, the construction cost are estimated by statistical methods. Among the statistical methods, support vector regression (SVR) has attracted a lot of attentions because of the generalization ability in the field of cost estimation. However, despite the simplicity of the parameter to be adjusted, it is not easy to find optimal parameters. Therefore, to build an effective SVR model, SVR's parameters must be set properly without additional data handling loads. So this study proposes a novel approach, known as genetic algorithm (GA), which searches SVR's optimal parameters, then adopt the parameters to the SVR model for estimating cost in the early stage of apartment housing projects. The aim of this study is to propose a GA-SVR model and examine the feasibility in cost estimation by comparing with multiple regression analysis (MRA). The experimental results demonstrate the estimating performance based on the percentage of estimations within 25% and find it can effectively do the accurate estimation without through the trial and error process.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.2
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• pp.122-133
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• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.391-398
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• 2023

In this paper, a penalized maximum likelihood estimation (PMLE) method that applies a penalty to increase the accuracy of a basis-screening-based Kriging model (BSKM) is introduced. The maximum order and set of basis functions used in the BSKM are determined according to their importance. In this regard, the cross-validation error (CVE) for the basis functions is employed as an indicator of importance. When constructing the Kriging model (KM), the maximum order of basis functions is determined, the importance of each basis function is evaluated according to the corresponding maximum order, and finally the optimal set of basis functions is determined. This optimal set is created by adding basis functions one by one in order of importance until the CVE of the KM is minimized. In this process, the KM must be generated repeatedly. Simultaneously, hyper-parameters representing correlations between datasets must be calculated through the maximum likelihood evaluation method. Given that the optimal set of basis functions depends on such hyper-parameters, it has a significant impact on the accuracy of the KM. The PMLE method is applied to accurately calculate hyper-parameters. It was confirmed that the accuracy of a BSKM can be improved by applying it to Branin-Hoo problem.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.629-632
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• 1997

This paper presents the design method of controller which is combined Genetic Algorithms with the Generalized minimum variance self tuning controller. It is shown that the controllers adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a polynomial parameters. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.