• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1088-1095
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• 1999

This paper presents short-term hydro scheduling method for hydrothermal coordination by genetic algorithms. Hydro scheduling problem has many constraints with fixed final reservoir volume. In this paper, the difficult water balance constraints caused by hydraulic coupling satisfied throughout dynamic decoding method. Adaptive penalizing method was also proposed to handle the infeasible solutions that violate various constraints. In this paper, we proposed GA to solve hydrothermal scheduling with appropriate decoding method and dynamic penalty method. The effectiveness of the proposed method is demonstrated in the case study.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.640-645
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• 2004

This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.12-20
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• 1993

In a conventional adaptive echo canceller, an ADF(Adaptive Digital Filter) with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to compute the coefficients, and NET detector using energy comparison method prevents the ADF to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yields more accurate detection of the start point of the NET signal.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.104-111
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• 2002

This paper presents a fuzzy rule extraction method using EM(Expectation-Maximization) algorithm and a design method of adaptive neuro-fuzzy control. EM algorithm is used to estimate a maximum likelihood of a GMM(Gaussian Mixture Model) and cluster centers. The estimated clusters is used to automatically construct the fuzzy rules and membership functions for ANFIS(Adaptive Neuro-Fuzzy Inference System). Finally, we applied the proposed method to the water temperature control system and obtained better results with respect to the number of rules and SAE(Sum of Absolute Error) than previous techniques such as conventional fuzzy controller.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.237-241
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• 2005

In Bluetooth version 1.0, the frequency hopping algorithm was such that there was one piconet, using a specific frequency, resolving the frequency depending on the part of the digits of the device clock and the Bluetooth address. Basic pattern was a kind of a round-robin using 79 frequencies in the ISM band. At this point, a problem occurs if there were more than two devices using the same frequency within specific range. In this paper, we proposed a software-based adaptive frequency hopping method so that more than two wireless devices can stay connected without frequency crash. Suggested method was implemented with HDL(Hardware Description Language) and automatically synthesized and laid out. Implemented adaptive frequency hopping circuit operated well in 24MHz correctly.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.11
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• pp.9-21
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• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.11
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• pp.575-582
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• 2001

This paper presents a calculating method of switching angle for adaptive switched reluctance motor (SRM) drive of a drill. The operation of the SRM is completely characterized by the flux linked by one phase winding which depends only on the current in that same phase winding and the rotor position. An efficiently adaptive SRM drive is possible on appropriately scheduling the commutation angles with accurate rotor position, supplied current value and speed information. An adaptive SRM drive with reduction torque ripple should be controlled by an optimized phase current control along with rotor position. Therefore, we are suggested a computing method of switching turn-on and off angles for adaptationally SRM operation with varied rotor speed and load. To probe the computing method, we have some simulation and experiment, it is shown a good result that can be computing the optimized switching angles for an electric drill motor.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.479-488
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• 2009

The ${\kappa}$-Nearest Neighbors Classification(KNNC) is a popular non-parametric classification method which assigns a fixed number ${\kappa}$ of neighbors to every observation without consideration of the local feature of the each observation. In this paper, we propose an Adaptive Nearest Neighbors Classification(ANNC) as an alternative to KNNC. The proposed ANNC method adapts the number of neighbors according to the local feature of the observation such as density of data. To verify characteristics of ANNC, we compare the number of misclassified observation with KNNC by Monte Carlo study and confirm the potential performance of ANNC method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.485-500
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• 2000

In this paper the adaptive crack propagation analysis based on the estimated local and global error in the element-free Galerkin (EFG) method is presented. It is possible to keep consistency and accuracy of analysis in each propagation step by adaptive analysis. The adaptivity analysis in crack propagation is achieved by adding and removing the node along the background integration cell that are refined or recovered as estimated error. These errors are obtained by calculating the difference between the values of the projected stresses and original EFG stresses. To evaluate the performance of proposed adaptive procedure, the convergence behavior is investigated lot several examples. The results of these examples show the efficiency of proposed scheme in crack propagation analysis.