• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.90-101
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• 2000

In this paper, we propose a controller for differentially steered wheeled mobile robots. The controller uses input-output linearization algorithm and artificial neural network to stabilize the dynamic model and compensate uncertainties. The proposed neural network part has 6 inputs, 1 hidden layer, 2 torque outputs and features fast online learning and good performance on structure error learning basis. Simulation results show that the proposed controller perform precisely tracking of reference path and is robust to uncertainties.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.82-88
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• 2007

Service engineering is a new discipline which aims at basically understanding of service and a concrete methodology using engineering approach for service design. In this paper is introduced the serviset graph [7] which is a comprehensive concept to represent service products, processes, and operation activities. The proposed concept is a degree of commonality index in a service product in order to deal with service mass customization using the input-output analysis in economic model.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.107-113
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• 1991

This article develops a control model for an open queueing network in terms of both the input and the output processes with stochastic intensities. The input and the output intensities are subject to some capacity limits and optimum control is characterized by a threshold type with a finite upper barrier. A discounted profit is used as a decision criteria, which is revenue minus operating and holding cost.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.471-476
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• 1994

In this paper a new controller is proposed which gives the resultant system the appointed input-output properties, low sensitivity and robust stability. The proposed controller consists of a reference model and a robust compensator. The reference model determines the input-output properties of the total system and is constructed by using the nominal model of the plant. We can design the reference model by applying design techniques which pay attention to steady robustness and no attention to sensitivity and robust stability, and need all state variables of the plant. The robust compensator is obtained as a solution of the mixed sensitivity problem in H infinity control theory. Therefore, low sensitivity and robust stability are guaranteed in the resultant system. The simulation experiments show that the proposed controller is effective and useful.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.404-407
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• 2001

In this paper, hydro-turbine system is modelled. Real input and output signals are acquired from the hydro-turbine system and the parameters of the model are estimated using input-output data, the model adjustment technique and a genetic algorithm(GA). To verify feasibility of the propose(1 model, computer simulations using GA have been carried out. The results show excellent characteristics of the proposed modeling and identification of the hydro-turbine system.

• Journal of Electrical Engineering and information Science
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• v.2 no.4
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• pp.28-36
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• 1997

In this paper, robust stability analysis for the fuzzy feedback linearization regulator is presented. Well-known Takagi-Sugeno fuzzy model is used as the MISO nonlinear plant model. Uncertainty and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainty and disturbances, robust stability of the close system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed by using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. The proposed stability analysis is illustrated by a simple example.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.395-401
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• 1996

In this paper a new neural network model, time delayed counterpropagation neural networks (TDCPN) which have high recognition rate and short total learning time, is proposed for electromyogram(EMG) recognition. Signals the proposed model increases the recognition rates after learned the regional temporal correlation of patterns using time delay properties in input layer, and decreases the learning time by using winner-takes-all learning rule. The ouotar learning rule is put at the output layer so that the input pattern is able to map a desired output. We test the performance of this model with EMG signals collected from a normal subject. Experimental results show that the recognition rates of the suggested model is better and the learning time is shorter than those of TDNN and CPN.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.2
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• pp.451-457
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• 1998

This paper describes a modelling method for SCR(selective catalystic reduction) system in refuse incineration plant. We consider the SCR system as a single input and single output system. For modelling the SCR system, an auto regressive exogeneous(ARX) modelling method is used. In this case, we should design the white noise input for modelling and put it on the system as an input$(NH_3)$, and take an outlet NOx as an output. From these two relations, we design the ARX model with 45 second delay time and transform to a discrete system with sampling time of 0.5 second. Using the obtained SCR model, we verify that the outlet NOx is deeply related with stoker`s moving in boiler of refuse incineration plant.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.353-357
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• 2008

One objective in developing the next generation of wireless communication systems is to increase data rates and reliability. A promising way to achieve this is to combine multiple-input and multiple-output signal processing with a space-time coding scheme, which offers higher coding and diversity gains and improves the spectrum efficiency and reliability of a wireless communication system. It is noted, however, that time delay differences and phase differences among different channels increase symbol interference and degrade system performance. In this letter, we investigate phase differences and their effects on multiple-input and multiple-output systems, and propose a compensation algorithm for the Rayleigh fading model to minimize their effects.