• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.110-110
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• 2001

By the state transformation including independent outputs functions, a nonlinear process model can be decomposed into two subsystems; the one(design model) is described in output variables as new states and used for control system synthesis and the other(disturbance model) is described in the original unavailable states and its couplings with the design model are treated as uncertain time-varying parameters in the design model. Its existence with respect to the design model is ignored. So, the design model is and uncertain time-variant system. Control synthesis based on a reduced design model is a combined form of a time-variant input-output linearization with parameter estimation. The parameter estimation is also based on the design model and it gives the parameter estimates such that the estimated outputs follow the actual outputs in a specified way. The disturbances form disturbance model and as well all the other uncertainties affecting the outputs will be reflected into the estimated parameters used in the linearizing control law.

• Journal of the Korean Statistical Society
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• v.5 no.1
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• pp.35-48
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• 1976

Since the solution vector for input-output forecasting models is not directly obtainable, several iterative procedures have been proposed and utilized. As is often the case in numerical analysis, the question of the consistency between the original system and the converged system of the proposed iteration has been ignored, and no one has tried to express the converged solution explicitly. This paper examines this question and points out the inconsistencies between various well-known iterative procedures used to solve input-output models and the original input-output system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.139.3-139
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• 2001

The free-model concept is introduced as an alternative intelligent system technique to design a controller with input and output data only. The idea of free model comes from the Taylor series approximation, where an output can be estimated when such data as position, velocity, and acceleration are known. The parameters in the free model can be estimated using the input-output data and a controller can be designed based on the free model. The free model thus developed is shown to be controllable, observable, and robust. The accuracy of the free-model approximation can be improved by increasing the observation window and the order of the free model. The LQR method is applied to the free model to design power system stabilizers ...

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.73-77
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• 1986

Optimal input design problem for linear regression model with constrained output variance has been considered. It is shown that the optimal input signal for the linear regression model can also be realized as an ARMA process. Monte-Carlo simulation results show that the optimal stochastic input leads to comparatively better estimation accuracy than white input signal.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.2
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• pp.167-181
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• 1995

We develop an analytical model to estimate the time a workpiece spends in both input and output queues in trip-based material handling systems. The waiting times in the input queues are approximated by M/G/1 queueing system and the waiting times in the output queues are estimated using the method discussed in Bozer, Cho, and Srinivasan [2]. The analytical results are tested via simulation experiment. The result indicates that the analytical model estimates the expected waiting times in both the input and output queues fairly accurately. Furthermore, we observe that a workpiece spends more time waiting for a processor than waiting for a device even if the processors and the devices are equally utilized. It is also noted that the expected waiting time in the output queue with fewer faster devices is shorter than that obtained with multiple slower devices.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.5
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• pp.35-42
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• 1995

his paper is to develop a GIS application model (GISCELWAB) for the spatial simulation of surface runoff from a small watershed. The model was constituted by three submodels : The input data extraction model (GISINDATA) which prepares cell-based input data automatically for a given watershed, the cell water balance model (CELWAB) which calculates the water balance for a cell and simulates surface runoff of watershed simultaneously by the interaction of cells, and the output data management model (GISOUTDISP) which visualize the results of temporal and spatial variation of surface runoff. The input data extraction model was developed to solve the time-consuming problems for the input-data preparation of distributed hydrologic model. The input data for CELWAB can be obtained by extracting ASCII data from a vector map. The output data management model was developed to convert the storage depth and discharge of cells into grid map. This model enables to visualize the spatial formulation process of watershed storage depth and surface runoff wholly with time increment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.397-401
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• 1994

This paper proposes an identification method for a thin plate where multiple actuators and sensors are bonded. Since a thin plate has small damping ratios of all modes, each mode can be identified seperately with a bandpass filter for each modal signal. With the bandpass filter and the characteristics of the plate, the Multi-Input Multi-Output (MIMO) model of the plate can be converted to several Multi-Input Single-Output(MISO) models of second order linear difference equations of the modes. Parameters for each mode are obtained by using the Least Square method. Form there MISO models, the MIMO model is obtained in the form of the state space. Experiments were performed for an all-clamped plate with two pairs of piezoelectric actuators and sensors. The outputs of the identified model and the experimental data match well.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.2
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• pp.137-143
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• 1989

System modeling from input-output data is generally carried out in two steps. The first step is to determine the form of the model. In the second step, the parameters of the model in an appropriate form are estimated from input-output data. This paper presents a method, via single term Walsh functions, for simultaneous estimation of the order and the parameters of linear systems from input-output data. The estimation of the model order is based on minimizing an error function, which is defined by Desai and Fairman. Unknown system parameters are recursively estimated by the least square method.

• Journal of Korean Society of Rural Planning
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• v.16 no.1
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• pp.9-20
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• 2010

The aim of this paper is to identify Korean agricultural and agri-manufacturing cluster using a multi-regional input-output model. This paper derives a representative set of five agricultural and agri-manufacturing clusters in Korea in terms of spatial and industrial interdependency. The results show that agriculture and agri-manufacturing clusters agglomerated in Seoul Metropolitan Area and Chungcheong Area are linked both production and manufacture functions, whereas Gangwon Area is more focused on production and Jeolla Area is more concentrated on manufacture.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.10
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• pp.1031-1036
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• 2015

Proportional-integral-derivative (PID) controllers are widely used in industrial sites. Most tuning methods for PID controllers use an empirical and experimental approach; thus, the experience and intuition of a designer greatly affect the tuning of the controller. The representative methods include the closed-loop tuning method of Ziegler-Nichols (Z-N), the C-C tuning method, and the Internal Model Control tuning method. There has been considerable research on the tuning of PID controllers for single-input single-output systems but very little for multi-input multi-output systems. It is more difficult to design PID controllers for multi-input multi-output systems than for single-input single-output systems because there are interactive control loops that affect each other. This paper presents a tuning method for the PID controller for a two-input two-output system. The proposed method uses a real-coded genetic algorithm (RCGA) as an optimization tool, which optimizes the PID controller parameters for minimizing the given objective function. Three types of objective functions are selected for the RCGA, and each PID controller parameter is determined accordingly. The performance of the proposed method is compared with that of the Z-N method, and the validity of the proposed method is examined.