• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.2
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• pp.10-16
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• 2012

Currently, the majority of systems which are non-linear are in need of the correct system equations for controlling and monitoring. Therefore, the correct estimation of parameters is crucial. Generally, parameters are changed due to system deterioration or sudden environmental alterations. Given the limitations of system monitoring unstable controls can arise. In the following paper, the parameter estimation method is proposed using software filters to combat these system instabilities. For dynamic instances, a powerful particle filter is used to control the nonlinear and noisy environments in which they take place. Using a setup simulation comprised of a slider and pendulum, the state variable of noise is obtained. After collecting the data, the proposed algorithm is used to estimate both the state variable and its parameters. Finally, these results are checked with correct parameter estimations to evaluate and verify the algorithms performance.

• Korean Management Science Review
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• v.29 no.3
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 1998.11a
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• pp.76-80
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• 1998

In order to identify the cause of ground water drawdown of a piezometer installed around the LPG storage cavern, parameter estimations were conducted by inverse and forward numerical models. An inverse model, SK-EST developed by SK Engineering & Construction Co., Ltd.(SKEC,1997) was performed to estimate the change of the hydraulic conductivity. It was verified by the commercial forward model, AQUA3D (VATNASKIL,1995). The simulation results showed that the hydraulic diffusivity of the rock mass between the piezometer and the cavern had been increased and the change rate of the hydraulic head had been abruptly increased in response to the change of the operation pressure. Finally the statistical analysis for observed data showed the increase of the change rate of the hydraulic head and thus proved the applicability of SK-EST.

• Aerospace Engineering and Technology
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• v.3 no.1
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• pp.251-256
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• 2004

A stable reference voltage generator is necessary to the infrared image signal readout circuit(ROIC) to improve noise characteristics in comparison with signals originated from infrared devices, that is, to gain good images. In this study, bandgap reference circuit operating at cryogenic temperature of 77K for Infrared image ROIC(readout integrated circuit) was propose. Most of bandgap reference circuits which are presented so far operate at room temperature, and they are not suitable for infrared image ROIC operating at liquid nitrogen temperature, 77K. To design bandgap reference circuit operating at cryogenic temperature, the parameter characteristics of used devices as temperature change are seen, and then bandgap reference circuit is proposed with considering such characteristics. It demonstrates practical use possibility through taking measurements and estimations.

• Smart Structures and Systems
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• v.20 no.5
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• pp.525-537
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• 2017

This paper explores different vibration control strategies for the cancellation of human-induced vibration on a structure with time-varying modal parameters. The main motivation of this study is a lively urban stress-ribbon footbridge (Pedro $G\acute{o}mez$ Bosque, Valladolid, Spain) that, after a whole-year monitoring, several natural frequencies within the band of interest (normal paring frequency range) have been tracked. The most perceptible vibration mode of the structure at approximately 1.8 Hz changes up to 20%. In order to find a solution for this real case, this paper takes the annual modal parameter estimates (approx. 14000 estimations) of this mode and designs three control strategies: a) a tuned mass damper (TMD) tuned to the most-repeated modal properties of the aforementioned mode, b) two semi-active TMD strategies, one with an on-off control law for the TMD damping, and other with frequency and damping tuned by updating the damper force. All strategies have been carefully compared considering two structure models: a) only the aforementioned mode and b) all the other tracked modes. The results have been compared considering human-induced vibrations and have helped the authors on making a decision of the most advisable strategy to be practically implemented.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.287-292
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• 2005

This paper presents a rational polynomial approximation method to estimate modal parameters of wind excited structures using incomplete noisy measurements of structural responses and partial measurements of wind velocities only. A stochastic model of the excitation wind force acting on the structure is estimated from partial measurements of wind velocities. Then the transfer functions of the structure are approximated as rational polynomial functions. From the poles and zeros of the estimated rational polynomial functions, the modal parameters, such as natural frequencies, damping ratios, and mode shapes are extracted. Since the frequency characteristics of wind forces acting on structures can be assumed as a smooth Gaussian process especially around the natural frequencies of the structures according to the central limit theorem (Brillinger, 1969; Yaglom, 1987), the estimated modal parameters are robust and reliable with respect to the assumed stochastic input models. To verify the proposed method, the modal parameters of a TV transmission tower excited by gust wind are estimated. Comparison study with the results of other researchers shows the efficacy of the suggested method.

• Structural Engineering and Mechanics
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• v.34 no.1
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• pp.15-38
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• 2010

In order to predict the shear strengths of reinforced concrete beams, many deterministic models have been developed based on rules of mechanics and on experimental test results. While the constant and variable angle truss models are known to provide reliable bases and to give reasonable predictions for the shear strengths of members with shear reinforcement, in the case of members without shear reinforcement, even advanced models with complicated procedures may show lack of accuracy or lead to fairly different predictions from other similar models. For this reason, many research efforts have been made for more accurate predictions, which resulted in important recent publications. This paper develops probabilistic shear strength models for reinforced concrete beams without shear reinforcement based on deterministic shear strength models, understanding of shear transfer mechanisms and influential parameters, and experimental test results reported in the literature. Using a Bayesian parameter estimation method, the biases of base deterministic models are identified as algebraic functions of input parameters and the errors of the developed models remaining after the bias-correction are quantified in a stochastic manner. The proposed probabilistic models predict the shear strengths with improved accuracy and help incorporate the model uncertainties into vulnerability estimations and risk-quantified designs.

• Computational Structural Engineering
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• v.3 no.4
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• pp.143-148
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• 1990

The modal parameter estimations of linear multi-degree-of-freedom structural dynamic systems are carried out in time domain. For this purpose, the equation of motion is transformed into the auto regressive and moving average model with auxiliary stochastic input(ARMAX) model. The parameters of the ARMAX model are estimated by using the sequential prediction error method. Then the modal parameters of the system are obtained thereafter. Experimental results are given for a 3-story budding model subject to ground exitations.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.65-74
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• 1993

Suppose that we have two populations(or systems), say ${\Pi}_{1}\;and\;{\Pi}_{2}$, to be tested. A random sample of size n from each population is taken and the test for each system will be terminated when the first r failures among n random samples are observed. This kind of test is caned the type-II censored (or item-censored) testing without replacement. Under this scheme we consider the problem of estimating the unknown parameters of interests and the reliability for a given time t for each population.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.121-127
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• 2010

We consider the robust trajectory tracking control problem for a skid steering mobile robots. A dynamic model is derived accounting for the effects of wheel skidding. The control design utilizes the dynamic feedback linearization techniques, so as to obtain a predictable behavior for the instantaneous center of rotation thus preventing excessive skidding. The additive controller using the sliding mode type is then robustified against the unmodelled dynamics and parameter uncertainty. Simulation results show the good performances under excessively uncorrected estimations of the longitudinal forces and the lateral resistive forces caused by the skidding of the wheels in tracking trajectories.