• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.13-20
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• 2013

The purpose of this study was to evaluate the applicability of the HSPEXP expert system for the calibration of the Hydrological Simulation Program - Fortran (HSPF) for the study watershed. HSPEXP offers advice to the modeler, suggesting parameter changes that might result in better representation of a river basin and provides explanations supporting the recommended parameter changes. The study watershed, Sancheong, is located within the Nakdong River Basin and having the size of $1,072.4km^2$. Input data for the HSPF model were obtained from the landuse map, digital elevation map, meteorological data and others. Water flow data from 2006 to 2008 were used for calibration and from 2009 to 2010 were for validation. Using the HSPEXP expert system, hydrological parameters were adjusted based on total volume, then low flows, storm flows, and finally seasonal flows. For the calibration and validation period, all the HSPEXP model performance criteria were satisfied.

• Journal of Wetlands Research
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• v.11 no.2
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• pp.107-121
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• 2009

Many rainfall-runoff models have been used for the flood forecasting. However, the determination of rainfall-runoff model parameters is very difficult. In this study, we investigated the efficiency of flood forecasting models by studying the optimization techniques for parameter calibration of SFM, Tank, and SSARR models. We analyzed the correlations between parameters in optimization techniques, then classified the parameters into parameter groups. For this we applied the sequential calibration method through the sensitivity analysis. As the results of the analysis, the parameter groups clibration method showed better result for peak flow and clibtation time.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.435-441
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• 2006

For the calibration of rainfall-runoff model, automatic calibration methods are used instead of manual calibration to obtain the reliable modeling results. When mathematical programming techniques such as linear programming and nonlinear programming are applied, there is a possibility to arrive at the local optimum. To solve this problem, genetic algorithm is introduced in this study. It is very simple and easy to understand but also applicable to any complicated mathematical problem, and it can find out the global optimum solution effectively. The objective of this study is to develope a parameter optimization program that integrate a genetic algorithm and a rainfall-runoff model. The program can calibrate the various parameters related to the runoff process automatically. As a rainfall-runoff model, SWMM is applied. The automatic calibration program developed in this study is applied to the Jangcheon watershed flowing into the Youngrang Lake that is in the eutrophic state. Runoff surveys were carried out for two storm events on the Jangcheon watershed. The peak flow and runoff volume estimated by the calibrated model with the survey data shows good agreement with the observed values.

• Journal of Korean Society on Water Environment
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• v.40 no.4
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• pp.179-194
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• 2024

Biophysicochemical processes in water environments and treatment systems have been great concerns of engineers and scientists for controlling the fate and transport of contaminants. These processes are practically formulated as mathematical models written in coupled differential equations. However, because these process-based mathematical models consist of a large number of model parameters, they are complicated in analytical or numerical computation. Users need to perform substantial trials and errors to achieve the best-fit simulation to measurements, relying on arbitrary selection of fitting parameters. Therefore, this study adopted a Bayesian calibration method to estimate best-fit model parameters in a systematic way and evaluated the applicability of the calibration method to biophysicochemical processes of water environments and treatment systems. The Bayesian calibration method was applied to the microbial growth-decay kinetics and flocculation kinetics, of which experimental data were obtained with batch kinetic experiments. The Bayesian calibration method was proven to be a reasonable, effective way for best-fit parameter estimation, demonstrating not only high-quality fitness, but also sensitivity of each parameter and correlation between different parameters. This state-of-the-art method will eventually help scientists and engineers to use complex process-based mathematical models consisting of various biophysicochemical processes.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.11a
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• pp.357-358
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• 2006

Accurate calibration matching for maladjusted stereo cameras with calibrated pixel distance parameter is presented. The camera calibration is a necessary procedure for stereo vision-based depth computation. Intra and extra parameters should be obtain to determine the relation between image and world coordination through experiment. One difficulty is in camera alignment for parallel installation: placing two CCD arrays in a plane. No effective methods for such alignment have been presented before. Some amount of depth error caused from such non-parallel installation of cameras is inevitable. If the pixel distance parameter which is one of Intra parameter is calibrated with known points, such error can be compensated in some amount and showed the variable experiments for accurate effects.

• Near Infrared Analysis
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• v.1 no.1
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• pp.1-7
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• 2000

A convenient algorithm for optimizing wavelength selection in multiple linear regression (MLR) has been developed. MOP (MLP Optimization Program) has been developed to test all possible MLR calibration models in a given spectral range and finally find an optimal MLR model with external validation capability. MOP generates all calibration models from all possible combinations of wavelength, and simultaneously calculates SEC (Standard Error of Calibration) and SEV (Standard Error of Validation) by predicting samples in a validation data set. Finally, with determined SEC and SEV, it calculates another parameter called SAD (Sum of SEC, SEV, and Absolute Difference between SEC and SEV: sum(SEC+SEV+Abs(SEC-SEV)). SAD is an useful parameter to find an optimal calibration model without over-fitting by simultaneously evaluating SEC, SEV, and difference of error between calibration and validation. The calibration model corresponding to the smallest SAD value is chosen as an optimum because the errors in both calibration and validation are minimal as well as similar in scale. To evaluate the capability of MOP, the determination of benzene content in unleaded gasoline has been examined. MOP successfully found the optimal calibration model and showed the better calibration and independent prediction performance compared to conventional MLR calibration.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.64-71
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• 1997

Objective of calibration is to find out the accurate kinematic relationships between robot joint angles and the position of the end-effector by estimating accurate model parameters defining the kinematic function. Estimating the model parameters requires measurement of the end-effector position at a number of different robot configurations. This paper studies the implication of measurement configurations in robot calibration. For selecting appropriate measurement configurations in robot calibration, an index is defined to measure the observability of the model parameters with respect to a set of robot configurations. It is found that, as the observability index of the selected measurement configurations increase the attribution of the position errors to the parameter errors becomes dominant while the effects of the measurement and unmodeled errors are less significant; consequently better estimation of parameter errors is expected. To demonstrate the implication of the observability measure in robot calibration, computer simulations are performed and their results are discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.224-229
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• 2007

This paper presents a camera calibration method using several images for three dimensional measurement applications such as stereo systems, mobile robots, and visual inspection systems in factories. Conventional calibration methods that use single image suffer from errors related to reference point extraction in image, lens distortion, and numerical analysis of nonlinear optimization. The camera parameter values obtained from images of same camera is not same even though we use same calibration method. The camera parameters that are obtained from several images of different view for a calibration target is usaully not same with large error values and we can not assume a special probabilistic distribution when we estimate the parameter values. In this paper, the median value of camera parameters from several images is used to improve estimation of the camera values in an iterative step with nonlinear optimization. The proposed method is proved by experiments using real images.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.1
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• pp.76-84
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• 2012

This paper presents the measurement system of arrow's point of impact using laser scan camera and describes the image calibration method. The calibration process of distorted image is primarily divided into explicit and implicit method. Explicit method focuses on direct optical property using physical camera and its parameter adjustment functionality, while implicit method relies on a calibration plate which assumed relations between image pixels and target positions. To find the relations of image and target position in implicit method, we proposed the performance criteria based polynomial theorem model that overcome some limitations of conventional image calibration model such as over-fitting problem. The proposed method can be verified with 2D position of arrow that were taken by SICK Ranger-D50 laser scan camera.

• Journal of Korea Water Resources Association
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• v.33 no.S1
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• pp.3-10
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• 2000

A newly developed heuristic algorithm, Harmony Search, is applied to the parameter calibration problem of the nonlinear Muskingum model. The Harmony Search could, mimicking the improvisation of music player, find better parameter values for in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in the aspect of SSQ(the sum of the square of the deviations between the observed and routed outflows) as well as in the aspects of SAD(the sum of the absolute value of the deviations), DPO(deviations of peak of routed and actual flows) and DPOT(deviatios of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of asuming the initial values of desing parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.