• Smart Structures and Systems
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• v.30 no.5
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• pp.447-462
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• 2022

The viscosity of a shear-thickening fluid damper (STFD) can increase dramatically when the STFD undergoes high-rate of excitation. Therefore, accurate numerical modelling of the STFD has been considered difficult due to this distinct feature. This study aims to develop a numerical model to accurately simulate the response of the STFD. First, a STFD is designed, fabricated, and installed in the laboratory. Then, performance tests are conducted in which sine waves with nine frequencies at three amplitude levels are adopted as the displacement excitations to the STFD. A novel numerical model which contains two parameter sets of the discrete Bouc-Wen model as well as two parameters for transiting the two parameter sets. Therefore, a total number of eighteen parameters need to be identified in the damper model. The symbiotic organisms search is applied to optimize the parameters. Numerical simulation results demonstrate that the proposed STFD model with transit parameter sets outperforms the conventional discrete Bouc-Wen model. The proposed STFD model can be applied to analyses of structures in which STFDs are installed in the future.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.67-79
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• 2011

The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.

• Structural Engineering and Mechanics
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• v.18 no.1
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• pp.91-112
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• 2004

Concrete is a composite material and at meso-level, may be assumed to be composed of three phases: aggregate, mortar-matrix and aggregate-matrix interface. It is postulated herein that although non-linear material parameters are generally used to model this composite structure by finite element method, linear elastic fracture mechanics principles can be used for modelling at the meso level, if the properties of all three phases are known. For this reason, a novel meso-mechanical approach for concrete fracture which uses the composite material model with distributed-phase for elastic properties of phases and considers the size effect according to linear elastic fracture mechanics for strength properties of phases is presented in this paper. Consequently, the developed model needs two parameters such as compressive strength and maximum grain size of concrete. The model is applied to three most popular fracture mechanics approaches for concrete namely the two-parameter model, the effective crack model and the size effect model. It is concluded that the developed model well agrees with considered approaches.

• Journal of Korea Water Resources Association
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• v.46 no.5
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• pp.519-530
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• 2013

To provide a reliable tool for runoff simulations of ungauged watersheds upstream of reservoirs, a daily runoff simulation model, Tank model, is restructured, the parameter regionalization of the model is conducted, and the model's applicability is evaluated. Taking into account the characteristics of runoffs from the watersheds, a three-tank model is employed. The percolation process of the model's third tank is eliminated, considering the water budgets of the watersheds, and its evapotranspiration component is improved, reflecting the conditions of meteorological observation in South Korea. The sensitivity analysis of the model shows that the model's behaviors, varying with a sensitive parameter, ${\alpha}$, are reasonable. The regional parameter estimation equations are determined, using the characteristics and land uses of the watersheds as variables. The model is applied for the runoff simulations of three watersheds and the water stage simulation of one reservoir, and the simulation results are then compared with the observed values, which prove to be in close agreement with the observations. In addition, the results from simulating inflows of twenty-four reservoirs using the model show that the averages of evapotranspiration rate and runoff rate are 42.8% and 56.6%, respectively, which are resonable. Consequently, it is concluded that the model is practically applicable to simulating runoffs from watersheds upstream of reservoirs, and simulated inflow data are useful for watershed management and reservoir planning, design, and operation.

• Carbon letters
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• v.22
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• pp.14-24
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• 2017

Carboxylated multi-wall carbon nanotubes (MWCNTs-COOH) have been used as efficient adsorbents for the removal of picric acid from aqueous solutions under stirring and ultrasound conditions. Batch experiments were conducted to study the influence of the different parameters such as pH, amount of adsorbents, contact time and concentration of picric acid on the adsorption process. The kinetic data were fitted with pseudo-first order, pseudo-second-order, Elovich and intra-particle diffusion models. The kinetic studies were well described by the pseudo-second-order kinetic model for both methods. In addition, the adsorption isotherms of picric acid from aqueous solutions on the MWCNTs were investigated using six two-parameter models (Langmuir, Freundlich, Tempkin, Halsey, Harkins-Jura, Fowler-Guggenheim), four three-parameter models (Redlich-Peterson, Khan, Radke-Prausnitz, and Toth), two four-parameter equations (Fritz-Schlunder and Baudu) and one five-parameter equation (Fritz-Schlunder). Three error analysis methods, correlation coefficient, chi-square test and average relative errors, were applied to determine the best fit isotherm. The error analysis showed that the models with more than two parameters better described the picric acid sorption data compared to the two-parameter models. In particular, the Baudu equation provided the best model for the picric acid sorption data for both methods.

• Clean Technology
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• v.7 no.2
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• pp.119-126
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• 2001

The prediction of ozone formation was studied using the neural network and the stochastic method. Parameter estimation method and artificial neural network(ANN) method were employed in the stochastic scheme. In the parameter estimation method, extended least squares(ELS) method and recursive maximum likelihood(RML) were used to achieve the real time parameter estimation. Autoregressive moving average model with external input(ARMAX) was used as the ozone formation model for the parameter estimation method. ANN with 3 layers was also tested to predict the ozone formation. To demonstrate the performance of the ozone formation prediction schemes used in this work, the prediction results of ozone formation were compared with the real data. From the comparison it was found that the prediction schemes based on the parameter estimation method and ANN method show an acceptable accuracy with limited prediction horizon.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.2
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• pp.69-75
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• 2002

AVR parameter tuning for voltage control of power system generators has generally been performed with the analytic methods and the simulation methods, which mostly depend on off-line linear mathematical models of excitation control system. However, due to the nonlinear nature of excitation control system, excitation control system performance of the tuned Parameters using the above conventional tuning methods may not be appropriate for some operating conditions. This paper presents an AVR parameter tuning method using actual on-line data of the excitation control system with the parameter optimization technique. As this method utilizes on-line operating data of the target excitation control system not the mathematical model of the system, it can overcome the limitation of model uncertainty Problems in conventional method, and it can tune the AVR parameter set which gives desired performance at the operating conditions. For the verification of proposed tuning method, two case studies with scaled excitation systems and the real-time power system simulator are presented.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.566-569
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• 2009

In this paper, we propose a less conservative a linear matrix inequality (LMI) condition for the constrained robust model predictive control of systems with input constraints and polytopic uncertainty. Systems with input constraints are represented as perturbed systems with sector bounded conditions. For the infinite horizon control, closed-loop stability conditions are obtained by using a parameter dependent Lyapunov function. The effectiveness of the proposed method is shown by an example.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.397-405
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• 1998

We develop parametric bootstrap model selection criteria in an example to fit a random sample to either a general normal distribution or a normal distribution with prespecified mean. We apply the bootstrap methods in two ways; one considers the direct substitution of estimated parameter for the unknown parameter, and the other focuses on the bias correction. These bootstrap model selection criteria are compared with AIC. We illustrate that all the selection rules reduce to the one sample t-test, where the cutoff points converge to some certain points as the sample size increases.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.921-929
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• 2014

A bivariate exponential distribution with a location parameter is proposed as a model for a two-component shared load system with a guarantee time. Some statistical properties of the proposed model are investigated. The maximum likelihood estimators and uniformly minimum variance unbiased estimators of the parameters, mean time to failure, and the reliability function of system are obtained with unknown guarantee time. Simulation studies are given to illustrate the results.