• Geomechanics and Engineering
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• v.16 no.5
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• pp.471-482
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• 2018

A flat-jointed bonded-particle model (BPM) has been proved to be an effective tool for simulating mechanical behaviours of intact rocks. However, the tedious and time-consuming calibration procedure imposes restrictions on its widespread application. In this study, a systematic approach is proposed for simplifying the calibration procedure. The initial relationships between the microscopic, constitutive parameters and macro-mechanical rock properties are firstly determined through dimensionless analysis. Then, sensitivity analyses and regression analyses are conducted to quantify the relationships, using results from numerical simulations. Finally, four examples are used to demonstrate the effectiveness and robustness of the proposed systematic approach for the calibration procedure of BPMs.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.657-664
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• 1999

This paper introduced the basic theory of LRCS(Linear Reservoir and Channel System) rainfall runoff model proposed by Korean researchers(Lee and Lee, 1995), and discussed the change of model output according to objective functions in sensitivity analysis and calibration process of model. It proposed "hat" matrix and affluence measures for affluence analysis of parameters in calibration, and investigated relationship between change of model output according to error propagation in parameter estimation, and sensitivity of model output according to variance of model output and change of parameters. Accuracy of parameter estimates was known by analysis of sensitivity coefficient, diagonal element $h_i$ and $D_i$._i$.

• Geomechanics and Engineering
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• v.22 no.1
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• pp.97-108
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• 2020

The time-consuming and less objectivity are the main problems of conventional micromechanical parameters calibration method of Particle Flow Code simulations. Thus this study aims to address these two limitation of the conventional "trial-and-error" method. A new calibration method for the linear parallel bond model (CM-LPBM) is proposed. First, numerical simulations are conducted based on the results of the uniaxial compression tests on limestone. The macroscopic response of the numerical model agrees well with the results of the uniaxial compression tests. To reduce the number of the independent micromechanical parameters, numerical simulations are then carried out. Based on the results of the orthogonal experiments and the multi-factor variance analysis, main micromechanical parameters affecting the macro parameters of rocks are proposed. The macro-micro parameter functions are ultimately established using multiple linear regression, and the iteration correction formulas of the micromechanical parameters are obtained. To further verify the validity of the proposed method, a case study is carried out. The error between the macro mechanical response and the numerical results is less than 5%. Hence the calibration method, i.e., the CM-LPBM, is reliable for obtaining the micromechanical parameters quickly and accurately, providing reference for the calibration of micromechanical parameters.

• Journal of Software Assessment and Valuation
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• v.16 no.1
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• pp.45-53
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• 2020

In this paper, we describe a new method for acquiring calibration data using a user interaction process, which occurs continuously during web browsing in gaze estimation, and for performing calibration naturally while estimating the user's gaze. The proposed non-intrusive calibration is a tuning process over the pre-trained gaze estimation model to adapt to a new user using the obtained data. To achieve this, a generalized CNN model for estimating gaze is trained, then the non-intrusive calibration is employed to adapt quickly to new users through online learning. In experiments, the gaze estimation model is calibrated with a combination of various user interactions to compare the performance, and improved accuracy is achieved compared to existing methods.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.250-255
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• 2003

The position of a 3-dimensional(3D) point can be measured by using calibrated stereo camera. To obtain more accurate measurement ,more accurate camera calibration is required. There are many existing methods to calibrate camera. The simple linear methods are usually not accurate due to nonlinear lens distortion. The nonlinear methods are accurate more than linear method, but it increase computational cost and good initial guess is needed. The multi step methods need to know some camera parameters of used camera. Recent years, these explicit model based camera calibration work with the development of more precise camera models involving correction of lens distortion. But these explicit model based camera calibration have disadvantages. So implicit camera calibration methods have been derived. One of the popular implicit camera calibration method is to use neural network. In this paper, we propose implicit stereo camera calibration method for 3D reconstruction using support vector machine. SVM can learn the relationship between 3D coordinate and image coordinate, and it shows the robust property with the presence of noise and lens distortion, results of simulation are shown in section 4.

• Proceedings of the KSRS Conference
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• v.1
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• pp.402-405
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• 2006

Radiometric calibration of optical image data is necessary to convert raw digital number (DN) value of each pixel into a physically meaningful measurement (radiance). To extract rather quantitative information regarding biophysical characteristics of the earth surface materials, radiometric calibration is often essential procedure. A sensor detects the radiation of sunlight interacted atmospheric constituents. Therefore, the amount of the energy reaching at the sensor is quite different from the initial amount reflected from the surface. To achieve the target reflectance after atmospheric correct, an initial step is to convert DN value to at-sensor radiance. A linear model, the simplest radiometric model, is applied to averaged spectral radiance for this conversion. This study purposes to analyze the sensitivity of several factors affecting on radiance for carrying out absolute radiometric calibration of panchromatic images from KOMPSAT2 launched at July, 2006. MODTRAN is used to calculate radiance at sensor and reflectance of target is measured by a portable spectro-radiometer at the same time the satellite is passing the target for the radiometric calibration. As using different contents of materials composing of atmosphere, the differences of radiance are investigated. Because the spectral sensitivity of panchromatic images of KOMPSAT2 ranges from 500 to 900 nm, the materials causing scattering in visible range are mainly considered to analyze the sensitivity. According to the verified sensitivity, direct measurement can be recommenced for absolute radiometric calibration.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1082-1095
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• 2019

Camera calibration is an important part of machine vision and close-range photogrammetry. Since current calibration methods fail to obtain ideal internal and external camera parameters with limited computing resources on mobile terminals efficiently, this paper proposes an improved fast camera calibration method for mobile terminals. Based on traditional camera calibration method, the new method introduces two-order radial distortion and tangential distortion models to establish the camera model with nonlinear distortion items. Meanwhile, the nonlinear least square L-M algorithm is used to optimize parameters iteration, the new method can quickly obtain high-precise internal and external camera parameters. The experimental results show that the new method improves the efficiency and precision of camera calibration. Terminals simulation experiment on PC indicates that the time consuming of parameter iteration reduced from 0.220 seconds to 0.063 seconds (0.234 seconds on mobile terminals) and the average reprojection error reduced from 0.25 pixel to 0.15 pixel. Therefore, the new method is an ideal mobile terminals camera calibration method which can expand the application range of 3D reconstruction and close-range photogrammetry technology on mobile terminals.

• Journal of Korea Water Resources Association
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• v.37 no.2
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• pp.133-144
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• 2004

A Tank Model composed of 4 tanks with soil moisture structure was applied to Daecheong Dam and Soyanggang Dam watersheds. Calibration and verification were repeated 332 and 472 times for each watershed using SCE-UA global optimization method for different calibration periods and objective functions. Four different methods of evapotranspiration calculation were used and evaluated. They are pan evaporation, 1963 Penman, FAO-24 Penman-Monteith, and FAO-56 Penman-Monteith methods. Tank model with soil moisture structure showed better results than the standard tank model for daily rainfall-runoff simulation. Two types of objective function for model calibration were found. Proper calibration period are 3 years, in which dry year and flood year are included. If a calibrationperiod has an inadequate runoff rate, the period should be more than 8 years. The four methods of eyapotranspiraton computation showed similar results, but 1963 Penman method was slightly inferior to the other methods.