• Journal of Sensor Science and Technology
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• v.14 no.2
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• pp.109-115
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• 2005

Calibration is a prerequisite procedure for employing a camera as a 3D sensor in an automated machines like robots. As accurate sensing is possible only when the vision sensor is calibrated accurately, many different approaches and models have been proposed for increasing calibration accuracy. Particularly an important factor which greatly affects the calibration accuracy is the nonlinearity in the mapping between 3D world and corresponding 2D image. In this paper GMDH algorithm is used to learn the nonlinearity without physical modelling. The technique proposed can be effective in various situations where the levels of noises and characteristics of nonlinear distortion are different. In simulations and an experiment, the proposed technique showed good and reliable results.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.16-17
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• 2019

In order to measure the amount of carbon dioxide emitted from the construction sites, many literature which have been conducted have proposed methodologies for calculating coefficients based on actual data collections for estimating the emission formula. The existing data collected under controlled conditions not on site measurement were too limited to apply in actual sites. The purpose of this study is to conduct analysis based on the data measured in fields and to present predictive models using artificial neural network and nonlinear regression analysis for appropriate predictions and practical applications.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.367-373
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• 1996

H$_{\infty}$ robust control theory is applied to the nuclear steam generator level control. Nuclear steam generator has the properties such as nonlinearity, non-minimum phase, and so, has some difficulties on level control. In a nuclear plant, it is more important to keep the operating variables under certain safety limits against various uncertainties than to meet the optimal performance. The designed H$_{\infty}$ controller shows robust level control against modelling error, disturbance in the nonlinear simulation. As the H$_{\infty}$ controller has both robustness and design transparency, it is adequate to the automation of level control and in licensibility

• Computers and Concrete
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• v.24 no.1
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• pp.7-17
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• 2019

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.

• Journal of the Korean Mathematical Society
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• v.59 no.3
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• pp.519-548
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• 2022

In this paper, a stabilized-penalized collocated finite volume (SPCFV) scheme is developed and studied for the stationary generalized Navier-Stokes equations with mixed Dirichlet-traction boundary conditions modelling an incompressible biological fluid flow. This method is based on the lowest order approximation (piecewise constants) for both velocity and pressure unknowns. The stabilization-penalization is performed by adding discrete pressure terms to the approximate formulation. These simultaneously involve discrete jump pressures through the interior volume-boundaries and discrete pressures of volumes on the domain boundary. Stability, existence and uniqueness of discrete solutions are established. Moreover, a convergence analysis of the nonlinear solver is also provided. Numerical results from model tests are performed to demonstrate the stability, optimal convergence in the usual L² and discrete H¹ norms as well as robustness of the proposed scheme with respect to the choice of the given traction vector.

• Computers and Concrete
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• v.29 no.1
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• pp.59-67
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• 2022

In this paper, a reliability-based approach has been implemented to develop seismic analytical fragility curves of highway bridges. A typical bridge class of the Central and South-eastern United States (CSUS) region was selected. Detailed finite element modelling is presented and Incremental Dynamic Analysis (IDA) is used to capture the behavior of the bridge from linear to nonlinear behavior. Bayesian linear regression method is used to define the demand model. A reliability approach is implemented to generate the analytical fragility curves and the proposed approach is compared with the conventional fragility analysis procedure.

• Earthquakes and Structures
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• v.23 no.5
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• pp.463-472
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• 2022

Reinforced concrete (RC) beam column joints (BCJ) have mostly exhibited poor seismic performance during several past earthquakes, typically due to the poor-quality concrete or lack of reinforcement detailing typical of pre-code design practice. The present study is motivated towards numerical simulation and seismic fragility assessment of one such RC-BCJ. The BCJ is loaded to failure and strengthened using Ultra High Performance-Hybrid Fiber Reinforced Concrete (UHP-HFRC) jacketing. The strengthening is performed for four different BCJ specimens, each representing an intermediate damage state before collapse. viz., slight, moderate, severe, and collapse. From the numerical simulation of all the BCJ specimens, an attempt is made to correlate different modelling and design parameters of the BC joint with respect to the damage states. In addition, seismic fragility analysis of the original as well as the retrofitted damaged BCJ specimens show the relative enhancement achieved in each case.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05c
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• pp.15-20
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• 1996

The nonlinear dynamic analyses were performed by newly developing an appropriate impact modelling for the evaluation of the CANFLEX fuel bundle structural integrity during the refuelling period. The initial load under the refuelling condition is considered as initial velocity at impact incident, and the impact of one bundle contacted another bundle for at short time is studied by performing several dynamic analysis method. The impact analysis shows to predict an appropriate velocity and acceleration profile according to load time history for two bundles impact.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.495-495
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• 2000

The Stewart platform manipulator is a closed-kinematis chain robot manipulator that is capable of providing high st겨ctural rigidity and positional accuracy. However, this is a complex structure, so controllability of the system is not so good. In this paper, it introduces a new robust motion control algorithm using partial state feedback for a class of nonlinear systems in the presence of modelling uncertainties and external disturbances. The major contribution of this work introduces the development and design of robust observer for the slate and the perturbation w.hich is integrated into a variable structure controller(VSC) structure. The combination of controller/observer gives rise to the robust routine called sliding mode control with sliding perturbation observer(SMCSPO). The optimal gains of SMCSPO are easily obtained by genetic algorithm. Simulation and experiment are presented in order to apply to the stewart platform manipulator. There results show highly' accuracy and performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.149-153
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• 2006

Precipitation is the most important component and critical to the study of water and energy cycle. This study investigates the propagation of precipitation retrieval uncertainty in the simulation of hydrologic variables for varying spatial resolution on two different vegetation cover. We explore two remotely sensed rain retrievals (space-borne IR-only and radar rainfall) and three spatial grid resolutions. An offline Community Land Model (CLM) was forced with in situ meteorological data In turn, radar rainfall is replaced by the satellite rain estimates at coarser resolution $(0.25^{\circ},\;0.5^{\circ}\;and\;1^{\circ})$ to determine their probable impact on model predictions. Results show how uncertainty of precipitation measurement affects the spatial variability of model output in various modelling scales. The study provides some intuition on the uncertainty of hydrologic prediction via interaction between the land surface and near atmosphere fluxes in the modelling approach.