• Journal of KSNVE
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• v.4 no.1
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• pp.71-82
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• 1994

We propose a systematic method to select the master states, which are retained in the reduced model after the order reduction process. The proposed method is based on the fact that the range space of right eigenvector matrix is spanned by orthogonal base vectors, and tries to keep the orthogonality of the submatrix of the base vector matrix as much as possible during the reduction process. To quentify the skewness of that submatrix, we define "Absolute Singularity Factor(ASF)" based on its singular values. While the degree of observability is concerned with estimation error of state vector and up to n'th order derivatives, ASF is related only to the minimum state estimation error. We can use ASF to evaluate the estimation performance of specific partial measurements compared with the best case in which all the state variables are identified based on the full measurements. A heuristic procedure to find suboptimal master states with reduced computational burden is also proposed. proposed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.290-293
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• 2008

This paper proposes a criterion to select the modes for modal truncated model of flexible rotor only supported by active magnetic bearings. The proposed approach relies on the concepts of minimum control input and output energy assuming that the system is subjected to transient disturbances. Accurate large order model for the levitated rotor is taken by finite element analysis and transformed to the modal equation. By proposed methodology, which modal states should be retained in the truncated model are investigated over the whole operational speed range by the calculation. Finally, the effectiveness is verified by checking the model error between original model and reduced model.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1447-1453
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• 2008

In this paper, we proposed to improve performance of the design of a cascade controller with the smith-predictor structure. The parameters of controller in the inner loop are determined to minimize the integral of time multiplied by the absolute value of error (ITAE) value of performance Index. The controller of outer loop and parameters of Smith-Predictor can be obtain using reduction model. The model reduction is considered that it is the transient response and the steady-state response through the use of nyquist curve. Simulation examples are given to show the better performance of the proposed method than conventional methods.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.303-313
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• 2018

This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables. The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.33-40
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• 1996

In this paper, we propose the discrete model reduction method of bounded real transfer functions. From the discrete bounded real lemma, we obtain the two riccati equations and define the disrete bounded real balancing using solutions of these two riccati equations. And we get the reduced order discrete model from the GSPA of full order model. Especially, when free parameter of GSPA is .+-.1, we show that the reduced order discrete model retains minimality, stability, and bounded real and BR-balancing properties. And we derive the .inf.-norm error bound between full order model and reduced order model. Finally to illustrate the validity of proposed method, we give an example.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.62.3-62
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• 2002

In this paper, we proposed a control technique that can be applied to various processes. The most of the process can bereduced to second order plus time delay (SOPTD) model. And we proposed improved model reduction algorithm using geneticalgorithm. This method considered four points to reduce the error between original model and reduced model in the Nyquistcurve. And, to compensate time delay, the Smith predictor plus PID controller is adopted. And a new PID tuning algorithm wasproposed, which got from numerical analysis and can be obtained the optimal performance. The PID parameters are obtainedfrom the coefficients and time delay of reduced model. The simulation results show the validity.

• ETRI Journal
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• v.29 no.4
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• pp.527-529
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• 2007

In this paper, a decision-tree-based Markov model for phrase break prediction is proposed. The model takes advantage of the non-homogeneous-features-based classification ability of decision tree and temporal break sequence modeling based on the Markov process. For this experiment, a text corpus tagged with parts-of-speech and three break strength levels is prepared and evaluated. The complex feature set, textual conditions, and prior knowledge are utilized; and chunking rules are applied to the search results. The proposed model shows an error reduction rate of about 11.6% compared to the conventional classification model.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.8
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• pp.584-593
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• 1987

A new combined approximation method using Routh stability array and mean-square error (MSE) method is proposed for deriving reduced-order z-transter functions for discrete time systems. The Routh stability array is used to obtain the reduced-order denominator polynomial, and the numerator polynomial is obtained by minimizing the mean-square error between the unit step responses of the original system and reduced model. The advantages of the new combined approximation method are that the reduced model is always stable provided the original model is stable and the initial and steady-state characteristics of the original model can be preserved in the reduced model.

• Current Optics and Photonics
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• v.2 no.4
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• pp.332-339
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• 2018

A new multi-frequency fringe projection method is proposed to reduce the nonlinear phase error in 3-D shape measurements using an adaptive compensation method. The phase error of the traditional fringe projection technique originates from various sources such as lens distortion, the nonlinear imaging system and a nonsinusoidal fringe pattern that can be very difficult to model. Inherent possibility of phase error appearing hinders one from accurate 3-D reconstruction. In this work, an adaptive compensation algorithm is introduced to reduce adaptively the phase error resulting from the fringe projection profilometry. Three different frequencies are used for generating the gratings of projector and conveyed to the four-step phase-shifting procedure to measure the objects of very discontinuous surfaces. The 3-D shape results show that this proposed technique succeeds in reconstructing the 3-D shape of any type of objects.