• KSBB Journal
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• 제6권1호
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• pp.15-25
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• 1991

The optimal glucose concentration for the high-density culture of recombinant yeasts was obtained using dynamic simulation. An adaptive and predictive algoritilm complimented by the rule base was proposed for the control of the fed-batch fermentation process. The measurement of process variables has relatively long sampling period and relatively long time delay characteristics. As one of the solution on these problems, prediction techniques and rule bases were added to a classical recursive identification and control algorithm. Rule bases were used in the determination of control input considering the difference between the predicted value and the measured value. A mathelnatical model was used in the estimation and interpretation of the changes of state variables and parameters. Better performances were obtained by employing the control algorithm proposed in the present study compared to the conventional adaptive control method.

• International journal of advanced smart convergence
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• 제5권4호
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• pp.66-70
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• 2016

When tooth grinding occurs, electrical stimulation is given at the same time, and tooth grinding stops on such stimulation. Electromyography signals are used as control signals of electrical stimulation to disturb tooth grinding. However because of the electrical stimulation, the M-waves are generated and mixed with spontaneous electromyogram. In this study, we designed an optimal filter to remove M-wave and conserve spontaneous electromyogram simultaneously. The inverse power method (IPM) showed that the optimal filter coefficient is the eigenvector corresponding to the minimum eigenvalue of the input covariance matrix. In order to evaluate the performance of the optimal filter, we compared using a conventional band pass filter and adaptive filter using least mean square algorithm. The experimental results show that the optimal filter can effectively remove the M-wave compared to the previously studied prediction error filter.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1505-1506
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• 2008

In this paper, a robust adaptive backstepping control is developed for efficiency optimization of induction motors with uncertainties. The proposed control scheme consists of efficiency flux control(EFC) using a sliding mode adaptive flux observer and robust speed control(RSC) using a function approximation for mechanical uncertainties. In EFC, it is important to find the flux reference to minimize power losses of induction motors. Therefore, we proposed the optimal flux reference using the electrical power loss function. The sliding mode flux observer is designed to estimate rotor fluxes and variation of inverse rotor time constant. In RSC, the unknown function approximation technique employs nonlinear disturbance observer(NDO) using fuzzy neural networks(FNNs). The proposed controller guarantees both speed tracking and flux tracking. Simulation results are presented to illustrate the effectiveness of the approaches proposed.

• Structural Engineering and Mechanics
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• 제3권4호
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• pp.391-410
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• 1995

The application of adaptive finite element method to dynamic problems is investigated. Both the kinetic and strain energy errors induced by space and time discretization were estimated in a consistent manner and controlled by the simultaneous use of the adaptive mesh generation and the automatic time stepping. Also an optimal ratio of spatial discretization error to temporal discretization error was discussed. In this study it was found that the best performance can be obtained when the specified spatial and temporal discretization errors have the same value. Numerical examples are carried out to verify the performance of the procedure.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 춘계학술대회논문집
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• pp.302-307
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• 2001

This research is concerned with the development of a real-time adaptive PPF controller for the active vibration suppression of smart structure. In general, the tuning of the PPF controller is carried out off-line. In this research, the real-time learning algorithm is developed to find the optimal filter frequency of the PPF controller in real time and the efficacy of the algorithm is proved by implementing it in real time. To this end, the adaptive algorithm is developed by applying the gradient descent method to the predefined performance index, which is similar to the method used popularly in the optimization and neural network controller design. The experiment was carried out to verify the validity of the adaptive PPF controller developed in this research. The experimental results showed that adaptive PPF controller is effective for active vibration control of the structure which is excited by either impact or harmonic disturbance. The filter frequency of the PPF controller can be tuned in a very short period of time thus proving the efficiency of the adaptive PPF controller.

• 한국통신학회논문지
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• 제32권9C호
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• pp.826-833
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• 2007

본 논문에서는 CR (Cognitive Radio) 시스템의 무선 적응전송에 대한 연구 기반을 마련하기 위해 CR 시스템 환경 및 시나리오 모델을 제시하고, 제시된 시스템 환경 모델에서 CR 시스템을 위한 간섭온도 인지 기반의 적응 전송 기법을 제안한다. 제안된 CR 적응전송 기법은 CR-APC (CR-Adaptive Power Control)를 사용하여 주 사용자에게 간섭 영향을 미치지 않는 범위 내에서 CR 사용자에게 최대 전송전력을 제공하고, CR-AMC (CR-Adaptive Modulation and Coding)를 적용하여 주어진 채널 상태에서 CR 사용자에게 최적의 전송률을 보장할 수 있는 방식이다. 모의실험 결과, 제안된 CR 적응전송 기법을 사용한 경우 주 사용자에게는 간섭 영향을 거의 미치지 않아 비트오율 성능 열화가 거의 없었으며, CR 사용자에게는 주어진 채널 상태에서 최적의 전송률을 보장할 수 있음을 확인할 수 있다.

• KSBB Journal
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• 제6권3호
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• pp.263-270
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• 1991

The optimal cell concentration and dilution rate for maximum ethanol productivity were obtained using dynamic simulation in cell recycled continuous bioreactor. The good control performance was observed using rule-based STR (self-tuning regulator) compared to conventional STR. Rule-base contained the scheme to implement the STR in an efficient on-off way and the scheme for the controlled variable to reach the optimal value in a short time. Since a mathematical model was used to analyze and estimate the changes of the state variables and the parameters, it was possible to understand the physical meaning of the system.

• Journal of Communications and Networks
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• 제12권3호
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• pp.253-265
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• 2010

The problem of pricing for a telecommunication network is investigated with respect to the users' sensitivity to the pricing structure. A functional optimization problem is formulated, in order to compute price reallocations as functions of data collected in real time during the network evolution. No a-priori knowledge about the users' utility functions and the traffic demands is required, since adaptive reactions to the network conditions are sought in real time. To this aim, a neural approximation technique is studied to exploit an optimal pricing control law, able to counteract traffic changes with a small on-line computational effort. Owing to the generality of the mathematical framework under investigation, our control methodology can be generalized for other decision variables and cost functionals.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1981년도 하계 학술회의초록집
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• pp.146-153
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• 1981

A practical and robust control scheme is suggested for MIMO discrete time processes with real simple poles. This type of control scheme, having the advantages of both the adaptiveness and optimality, may be successfully applicable to structured dynamic controllers for plants whose parameters are slowly time-varying. The identification of the process parameters is under-taken in ARMA form and the optimization of the feedback gain matrix is performed in the state space representation with regard to a standard quadratic criterion.

• KIEE International Transaction on Systems and Control
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• 제2D권2호
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• pp.125-134
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• 2002

This paper proposes a method for identifying temporal pattern clusters to predict events in time series. Instead of predicting future values of the time series, the proposed method forecasts specific events that may be arbitrarily defined by the user. The prediction is defined by an event characterization function, which is the target of prediction. The events are predicted when the time series belong to temporal pattern clusters. To identify the optimal temporal pattern clusters, fuzzy goal programming is formulated to combine multiple objectives and solved by an adaptive differential evolution technique that can overcome the sensitivity problem of control parameters in conventional differential evolution. To evaluate the prediction method, five test examples are considered. The adaptive differential evolution is also tested for twelve optimization problems.