• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.401-406
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• 2004

In this paper, a new time-domain adaptive predistortion scheme is proposed to compensate for the nonlinearity of high power amplifiers (HPA) in OFDM systems. A complex Wiener-Hammerstein model (WHM) is adopted to describe the input-output relationship of unknown HPA with linear dynamics, and a power series model with memory (PSMWM) is used to approximate the HPA expressed by WHM. By using the PSMWM, the compensation input to HPA is calculated in a real-time manner so that the linearization from the predistorter input to the HPA output can be attained even if the nonlinear input-output relation of HPA is uncertain and changeable. In numerical example, the effectiveness of the proposed method is confirmed and compared with the identification method based on PSMWM.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.6
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• pp.1819-1830
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• 1991

The problems associated with noise/energy source identification using multiple input/single output model in linear systems are investigated. Partial coherence function is formulated for the model introducing a virtual force and extraneous noises into the conventional two input/single output system. The analytical results show that the partial coherence function in two input/single output linear system is the function of noise/signal ratios when multiple inputs are mutually coherent and extraneous noises exist. Parametric studies for ordinary and partial coherence functions are carried out to demonstrate the effects of noise/signal ratios for these functions.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.3
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• pp.354-362
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• 2000

This paper identifies a discrete-time linear combustion model of Refuse Incineration Plant(RIP) which characterizes steam generation quantity, where the RIP is considered as a MIMO system with thirteen-inputs and one-output. The structure of RIP model is described as an ARX model which are analytically obtained from the combustion dynamics. Furthermore, using the Instrumental Variable(IV) identification algorithm, model structure and unknown parameters are identified from experimental input-output data sets, In result, it is shown that the identified ARX model well approximates the input-output combustion characteristics given by experimental data sets.

• Journal of Power System Engineering
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• v.3 no.2
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• pp.34-41
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• 1999

This paper identifies a linear combustion model of Refuse Incineration Plant(RIP) which characterizes its combustion dynamics, where the proposed model has thirteen-inputs and one-output. The structure of the RIP model is given as an ARX model which obtained from the theoretical analysis. And then, some unknown model parameters are decided from experimental input-output data sets, using system identification algorithm based on Instrumental Variables(IV) method. In result, it is shown that the proposed model well approximates the input-output combustion characteristics riven by experimental data sets.

• Journal of the Korean Graphic Arts Communication Society
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• v.25 no.1
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• pp.11-26
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• 2007

In this paper, an experiment was done where the input device used the linear multiple regression and the sRGB color space to perform a color transformation. The output device used the GOG, GOGO and sRGB for the color transformation. After the input device underwent a color transformation, a $3\;{\times}\;20\;size$ matrix was used in a linear multiple regression and the scanner's color representation of scanner was better than a digital still camera's color representation. When using the sRGB color space, the original copy and the output copy had a color difference of 11. Therefore it was more efficient to use the linear multiple regression method than using the sRGB color space. After the input device underwent a color transformation, the additivity of the LCD monitor's R, G and B signal value improved and therefore the error in the linear formula transformation decreased. From this change, the LCD monitor with the GOG model applied to the color transformation became better than LCD monitors with other models applied to the color transformation. Also, the color difference varied more than 11 from the original target in CRT and LCD monitors when a sRGB color transformation was done in restricted conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.895-905
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• 1999

This paper identifies a linear time-invariant mathematical model of each stand of a five-stand tandem cold mill to design a robust $H_\infty$ thickness controller by applying input and output data sets to N4SID (Numerical algorithms for Subspace State Space System Identification) method. The input-output data sets describe interstand interference in the process of tandem cold rolling and are obtained from a nonlinear simulator of the tandem cold mill. In result, it is shown that the identified model well approximates the nonlinear model than a Taylor linearized model. Furthermore, uncertainties including roll eccentricity and incoming strip variation are quantitatively analyzed from the plot of maximum singular values.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1201-1204
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• 2004

This paper suggests a DCS (Distributed Control System) model for steam temperature system of the thermal power plant. The model calculated within sectional range is linear. In order to calculate mathematical models, the system is partitioned into two or three sectors according to its thermal conditions, that is, saturated water/steam and superheating state. It is divided into three sections; water supply, steam generation and steam heating loop. The steam heating loop is called 'superheater' or steam temperature system. Water spray supply is the control input. A first order linear model is extracted. For linear approach, sectional linearization is achieved. Modeling methodology is a decomposition-synthetic technique. Superheater is composed of several tube-blocks. For this block, linear input-output model is to be calculated. Each tiny model has its transfer function. By expanding these block models to total system, synthetic DCS linear models are derived. Control instrument include/exclude models are also considered. The resultant models include thermal combustion conditions, and applicable to practical plant engineering field.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.183-193
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• 2007

This paper shows that a response function for the center of fuzzy output nay not be the same as that for the spread in a fuzzy linear regression model and then suggests a separate fuzzy regression model makes a distinction between response functions of the center and the spread of fuzzy output. Also we use a least squares method to estimate the separate fuzzy regression model and compare an accuracy of proposed model with another fuzzy regression model developed by Diamond (1988) and Kao and Chyu (2003).

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.239-246
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• 2003

Statisticians, or data miners, are often requested to assess the importances of input variables in the given supervised learning model. For the purpose, one may rely on separate ad hoc measures depending on modeling types, such as linear regressions, the neural networks or trees. Consequently, the conceptual consistency in input variable importance measures is lacking, so that the measures cannot be directly used in comparing different types of models, which is often done in data mining processes, In this short communication, we propose a unified approach to the importance measurement of input variables. Our method uses sensitivity analysis which begins by perturbing the values of input variables and monitors the output change. Research scope is limited to the models for continuous output, although it is not difficult to extend the method to supervised learning models for categorical outcomes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.185-188
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• 1997

In this paper, we execute identification, linearization, and control of a nonlinear system using recurrent neural networks. In general nonlinear control system become complex because of nonlinearity and uncertainty. And though we compose nonlinear control system based on the model, it is difficult to get good control ability. So we identify the nonlinear control system using the recurrent neural networks and execute feedback linearization of identified model, In this process we choose the optional linear system, and the system which will have to be feedback linearized if trained to follow the linearity between input and output of the system we choose. We the feedback linearized system by applying standard linear control strategy and simulation. And we evaluate the effectiveness by comparing the result which is linearized theoretically.