• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.104-115
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• 2013

For natural human-robot interaction, we need to know location and shape of facial feature in real environment. In order to track facial feature robustly, we can use the method combining particle filter and active appearance model. However, processing speed of this method is too slow. In this paper, we propose two ideas to improve efficiency of this method. The first idea is changing the number of particles situationally. And the second idea is switching the prediction model situationally. Experimental results is presented to show that the proposed method is about three times faster than the method combining particle filter and active appearance model, whereas the performance of the proposed method is maintained.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.1
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• pp.110-122
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• 1996

The objective of this study is to understand the dynamic characterictics of OHV type valve trains and to design and optimal cam profile which will improve engine performance. A numerical model for valve train dynamics is presented, which aims at both accuracy and computational efficiency. The lumped mass model and distributed parameter model were used to describe the valve train dynamics. Nonlinear characterictics in the valve spring behavior were included in the model. Comprehensive experiments were carried out concerning the valve train dynamics, and the model was tuned based on the test results. The dynamic model was used in designing an optimal cam profile. Because the objective function has many local minima, a conventional local optimizer cannot be used to find an optimal solution. A modified adaptive random search method is successfully employed to solve the problem. Cam lobe area could be increased up to 7.3% without any penalties in kinematic and dynamic behaviors of the valve train.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.650-655
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• 2016

In this paper, a novel RLS-based DOB (Recursive Least Squares Disturbance Observer) scheme is proposed to improve the performance of DOB for nominal model identification. A nominal model can be generally assumed to be a second order system in the form of a proper transfer function of an ARMA (Autoregressive Moving Average) model. The RLS algorithm for the model identification is proposed in association with DOB. Experimental studies of the balancing control of a one-wheel robot are conducted to demonstrate the feasibility of the proposed method. The performances between the conventional DOB scheme and the proposed scheme are compared.

• Nuclear Engineering and Technology
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• v.34 no.6
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• pp.533-544
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• 2002

The neutron induced nuclear data for Mo-95, Tc-99, Ru-101 and Rh-103 was calculated and evaluated in the fast energy region. The energy dependent optical model potential parameters were extracted based on the recent experimental data and applied up to 20 MeV. The s-wave strength function was calculated from the parameters. Spherical optical model, statistical model in equilibrium energy, multistep direct and multistep compound model in pre-equilibrium energy and direct capture model were used in the calculation. The theoretically calculated cross sections were compared with the experimental data and the evaluated files The model- calculated total and capture cross sections were in good agreement with the reference experimental data. The direct capture contribution improved the capture cross sections in pre- equilibrium region. The evaluated cross section results were compiled to ENDF-6 format and will improve the ENDF/B-Vl.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.552-557
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• 1997

Based on the bubble coalescence adjacent to the heated wall as a flow structure for CHF condition, Chang and Lee developed a mechanistic critical heat flux (CHF) model for subcooled flow boiling. In this paper, improvements of Chang-Lee model are implemented with more solid theoretical bases for subcooled and low-quality flow boiling in tubes. Nedderman-Shearer's equations for the skin friction factor and universal velocity profile models are employed. Slip effect of movable bubbly layer is implemented to improve the predictability of low mass flow. Also, mechanistic subcooled flow boiling model is used to predict the flow quality and void fraction. The performance of the present model is verified using the KAIST CHF database of water in uniformly heated tubes. It is found that the present model can give a satisfactory agreement with experimental data within less than 9% RMS error.

• International Journal of Fluid Machinery and Systems
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• v.5 no.4
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• pp.143-151
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• 2012

Predicting the future condition of machine and assessing the remaining useful life are the center of prognostics. This paper contributes a new prognostic method based on grey model and survival probability. The first step of the method is building a normal condition model then determining the error indicator. In the second step, the survival probability value is obtained based on the error indicator. Finally, grey model coupled with one-step-ahead forecasting technique are employed in the last step. This work has developed a modified grey model in order to improve the accuracy of prediction. For evaluating the proposed method, real trending data of low methane compressor acquired from condition monitoring routine were employed.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.245-254
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• 2016

Many surveys provide categorical data and there may be one or more missing categories. We describe a nonignorable nonresponse model for the analysis of two-way contingency tables from small areas. There are both item and unit nonresponse. One approach to analyze these data is to construct several tables corresponding to missing categories. We describe a hierarchical Bayesian model to analyze two-way categorical data from different areas. This allows a "borrowing of strength" of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the small areas. Also we use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data on thirteen states to obtain the finite population proportions.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.1224-1231
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• 2009

1D Analysis have been using Design of SCP in order to improve the soft ground. But 2D Analysis is researching and developing to get more accurate results. Using 2D Analysis, suitable Numerical Analysis Model should be selected and be tested in many situations. In this study, Laboratory Model Tests are analyzed by Numerical Analysis Method. After selecting Numerical Analysis Model, it is being tested many conditions.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.1
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• pp.58-64
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• 2004

In order to determine the level of safety on highway driving, the relationship between the psychophysiological signal and driving condition was investigated. In particular, a Demand-Effort model was conceptualized and used in this study to diagnose the suitability of driving by reading the patterns of psychophysiological signals. To run the model, threshold values were determined to categorize the high, moderate and low level of effort. To examine the sensitivity of the model, a cross-validation process was performed by collecting additional data. Further investigation need to be conducted to improve the sensitivity of the model for practical application.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.119-121
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• 2003

In small signal stability analysis of power systems, eigenvalue analysis is the most useful method and the detailed modeling of generator gives an important effect to the eigenvalues. Generator full model is used for precise dynamic analysis of generators and controllers while two-axis model is used for multimachine systems because of the reduced order of the state matrix. Also, the eigenvalue sensitivity coefficients are used for optimization of controller parameters to improve system stability. This paper compare the first order eigenvalue sensitivity coefficients of controllers in case of generator full model with those of two-axis model. As a result of an example the estimated eigenvalues using sensitivity coefficients in case of generator full model is very close to those of state matrix within 1% error ratios.