• Journal of the Korean Graphic Arts Communication Society
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• v.25 no.1
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• pp.65-80
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• 2007

The input and output device requires precise color representation and CMS (Color Management System) because of the increasing number of ways to apply the digital image into electronic publishing. However, there are slight differences in the device dependent color signal among the input and output devices. Also, because of the non-linear conversion of the input signal value to the output signal value, there are color differences between the original copy and the output copy. It seems necessary for device-dependent color information values to change into device-independent color information values. When creating an original copy through electronic publishing, there should be color management with the input and output devices. From the devices' three phases of calibration, characterization and color conversion, the device-dependent color should undergo a color transformation into a device-independent color. 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 undergoing a color transformation in the input and output devices, the best results were created when the original target underwent a color transformation by the scanner and digital camera input device by the linear multiple regression, and the LCD output device underwent a color transformation by the GOG model.

• 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 Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.59-67
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• 2013

This paper presents a neural net type process model for enhancing learning compensation function in hot strip finishing rolling mill. Adequate input and output variables of process model are chosen, the proposed model was designed as single layer neural net. Equivalent carbon content, strip thickness and rolling speed are suggested as input variables, and looper's manipulation variable is proposed as output variable. According to simulation result using process data to show the validity of the proposed process model, neural net type process model's outputs give almost similar data to process output under same input conditions.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.289-294
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• 1988

This paper describes tbe design and implementation of the adaptive controller to maintain the glood weld quality in gas metal arc welding process. The weld torch travel speed and the surface temperature are taken, respectively, as an input and an output of the welding control system. Because of the very complex phenomena of the process, the input-output dynamic model was experimentally identified by AIC (Akiake Information Criterion). Based on the model structure, the explicit model reference adaptive controller is simulated in order to regulate the output tempernture to the desired level.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.58-66
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• 1996

An adaptive input-output linearization technique of an interior permanent magnet synchronous motor with a specified output dynamic performance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive technique where the stator resistance and the magnitude of flux linkage can be estimated with the current dynamic model and state observer. Using these estimated parameters, the linearizing control inputs are calculated. With these control inputs, the input-output linearization is performed and the load torque is estimated. The adaptation laws are derived by the Popov's hyperstability theory and the positivity concept. The robustness and the output dynamic performance of the proposed control scheme are verified through the computer simulations.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.16 no.3
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• pp.50-57
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• 2008

This paper analyse the economic inducement effects of aviation industry using Input-Output Model. For measuring economic inducement effects of aviation industry on korean economy, this paper divides air transport industry as two - manufacturing industry and service industry. we also use Input-Output Table of year 1990 through 2003 from Bank of Korea. Empirical results tells that aviation manufacturing industry have high product inducement effects to national economy although its low value-added coefficient such as 0.486 for aviation manufacturing industry and 0.447 for aviation service industry. public R&D doesn't have much effect to each of aviation industries.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.851-856
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• 2011

In this paper, the fuzzy output-feedback controller is addressed for a discrete-time nonlinear interconnected systems with common input. The nonlinear interconnected system is represented by a T-S (Takagi-Sugeno) fuzzy model. Based on T-S fuzzy interconnected system, the fuzzy output-feedback controller is designed with common input. The stability condition of the closed-loop system is represented to the LMI (Linear Matrix Inequality) form. PEMFC model is given to show the verification of the controller discussed throughout the paper.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.394-405
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• 2012

Purpose: In this study, we suggest the systematic and comprehensive model to develop PI(Performance Indicators) of the organization or the process. Methods: The model is developed theoretically by using SIPOC(Supplier, Input, Process, Output, Customer) approach which is a tool to analyze the process and is compared with existing models to develop PI or KPI(key performance indicators); financial indicators, BSC, IPOO(input, process, output, outcome), traditional QCD (quality, cost, delivery), and IOS(input, output system). Results: The model provides more systematic method to develop PI and more comprehensive set of PI pools for all kinds of hierarchical levels of process than any other models to develop PI or KPI. Conclusion: This model will provide useful tools for the managers and the organizations who wish to develop PI.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.