• Journal of Information Technology Services
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• v.8 no.4
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• pp.129-140
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• 2009

The accurate estimation of software reliability is important to a successful development in software engineering. Until recent days, the models using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software reliability using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying immune algorithm, changing the number of generations, memory cells, and allele. The proposed IA-SVR model outperforms some recent results reported in the literature.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.445-450
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• 2004

Tunnel fire accident is one of the critical railway accidents, together with collision and derailment. For the safe operation many tunnel design guidelines are proposed but many Korean railway tunnels do not satisfy these guidelines. For the safety improvement, current safety level is estimated in this study. But so many uncertainties in major input parameters make the safety estimation difficult. In this study, probabilistic techniques are applied for the consideration of uncertainties in major input parameters. As results of this study, probabilistic safety estimation code is developed.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.785-787
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• 1999

In this paper, recursive least square algorithm is presented to estimate the parameters of step motor under low-speed operation. Parameter estimation is important for compensating the input current by calculating the ratio of the motor torque constant and detent torque constant that causes torque-ripple in low-speed applications. On-line parameter estimation process is a preliminary procedure to apply step motor to adaptive control. Computer simulation shows that the estimated parameters converge in finite time.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.76.5-76
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• 2001

This paper considers the parameter estimation for the state-space model based time-delay systems in the case that the Lyapunov stability of the system is guaranteed. In order to estimate the parameters, two estimation methods can be proposed which are known as the parallel model and the series parallel model. It is shown that the parameters can be estimated using each method, and also certied that the results are correct by simulations.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.149-152
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• 1998

From the viewpoint of model-based current control, it is indispensable to use the accurate system parameters for the high control performance. This paper adopts the Least-Squares algorithm as a parameter estimation scheme because it has the fast convergence rate and the low sensitivity to noises. In case of the intelligent current controller with delay compensator, the simulation results show that the adopted estimation scheme can be successfully applied to PWM converters and also show the improved control performance in the estimated parameters.

• Journal of the Korea Management Engineers Society
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• v.23 no.4
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• pp.159-164
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• 2018

Human-machine serial systems must be normal in both systems. Though the failure of machine is irreducible by itself, the human errors are of recurring type. When the human performance is described quantitatively, non-homogeneous Poisson Process model of human errors can be developed. And the model parameters can be estimated by maximum likelihood estimation and numerical analysis method. System reliability is obtained by multiplying machine reliability by human reliability.

• Smart Structures and Systems
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• v.22 no.3
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• pp.315-326
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• 2018

This paper presents a novel time-domain method for the identification of plastic rotations and stiffness parameters of single-bay frames with nonlinear plastic hinges. Each plastic hinge is modelled as a pseudo-semi-rigid connection with nonlinear hysteretic moment-curvature characteristics at an element end. Through the comparison of the identified end rotations of members that are connected together, the plastic rotation that furnishes information of the locations and plasticity degrees of plastic hinges can be identified. The force consideration of the frame members may be used to relate the stiffness parameters to the elastic rotations and the excitation. The damped-least-squares method and damped-and-weighted-least-squares method are adopted to estimate the stiffness parameters of frames. A noise-removal strategy employing a de-noising technique based on wavelet packets with a smoothing process is used to filter out the noise for the parameter estimation. The numerical examples show that the proposed method can identify the plastic rotations and the stiffness parameters using measurements with reasonable level of noise. The unknown excitation can also be estimated with acceptable accuracy. The advantages and disadvantages of both parameter estimation methods are discussed.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1998.04a
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• pp.23-30
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• 1998

An estimation of seismic risk parameters by seismic zones of the Korea Peninsula in order to calculate the seismic hazard values using these was erformed. Seven seismic source zones were selected in consideration of seismicity and geology of Korean Peninsula. The seismicity parameters that should be estimated are maximum intensity, activity rate and b value in the Gutenberg - Richter relation. For computation of these parameters, least square method or maximum likelihood method is applied to the earthquake data in two ways; the one for the data without maximum intensity and the other with maximum intensity. Earthquake data since Choseon Dynasty is regarded as complete and estimation of parameters was made for these data using above two ways. And recently, a new method is published that estimate the seismicity parameters using mixed data containing large historical events and recent complete observations. Therefore, this method is applied to the whole earthquake data of the Korean Peninsula. It turns out that the b value computed considering maximum intensity is slightly lower than that computed considering without maximum intensity, and it becomes still lower when the incomplete data prior to Choseon Dynasty is used. In the case of the activity rates, the values obtained without maximum intensity and that with maximum intensity are similar, though they are lower when the incomplete data is used. The values of maximum intensities are usually lower when considering incomplete data. In the seismic source zone including the Yangsan Fault zone, however, the values are higher when considering the incomplete data.

• Water for future
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• v.29 no.1
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• pp.151-161
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• 1996

The proposed flood forecasting system combines a flood routing model with a parameter estimation model. In the parameter estimation model system states and parameters are treated with the extended state-space formulation. The extended Kalman filter is adopted to estimate the states and parameters. A sensitivity analysis is used to investigate the relative significance of the parameters. Insensitive parameters are treated as constants and parameters that are mutually correlated are combined in a simplified form. The developed estimation methodology is applied todam sites of the multi-purpose reservoirs in Korea. The forecasted hydrographs from the extended Kalman filter satisfactorily coincide with the observed. From the time sequence plots of estimated parameters, it is found that the storage coefficient is almost constant, but exponent varies appreciably in time.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.