• Journal of Advanced Marine Engineering and Technology
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• v.28 no.4
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• pp.611-617
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• 2004

Pixel classification is one of basic image processing issues. The general characteristics of the pixels belonging to various classes are discussed and the radical principles of pixel classification are given. At the same time. a pixel classification scheme based on image direction measure is proposed. As a typical application instance of pixel classification, an adaptive multi-level median filter is presented. An image can be classified into two types of areas by using the direction information measure, that is. smooth area and edge area. Single direction multi-level median filter is used in smooth area. and multi-direction multi-level median filter is taken in the other type of area. What's more. an adaptive mechanism is proposed to adjust the type of the filters and the size of filter window. As a result. we get a better trade-off between preserving details and noise filtering.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.345-351
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• 2004

This Paper describes a nonlinear adaptive noise filter using neural network for digital controller system. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental reaults show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary input is divided by unit and each divided part is processed for very short time than all the processed data are unified to whole data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.7
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• pp.1414-1420
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• 1994

In this paper the existence of chaotic signal is probed in adaptive dead beat control law for nonlinear dynamic system. These chaotic signal makes the system unstable and difficult to control, but it broaden the range of application, confirms the robustness of system and gives a lot of information. Considering of low correlation between chaotic signals, robust adaptive control method which uses for parameter estimation is proposed. With this algorithm the parameters converges stable rapidly. Finally the superiority of it is proved by computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.11
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• pp.520-527
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• 2001

This paper suggests a new methodology of evolutionary computations - an Adaptive Evolutionary Algorithm (AEA) for solving the Economic Load Dispatch (ELD) problem which has piecewise quadratic cost functions and prohibited operating zones with many local minima. AEA uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations: global search capability of GA and local search capability of ES. In the reproduction procedure, proportions of the population by GA and the population by ES are adaptively modulated according to the fitness. Case studies illustrate the superiority of the proposed methods to existing conventional methods in power generation cost and computation time. The results demonstrate that the AEA can be applied successfully in the solution of ELD with piecewise quadratic cost functions and prohibited operating zones

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.149-159
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• 2008

This paper considers the problem of adaptive fault-tolerant guaranteed cost controller design via dynamic output feedback for a class of linear time-delay systems against actuator faults. A new variable gain controller is established, whose gains are tuned by the designed adaptive laws. More relaxed sufficient conditions are derived in terms of linear matrix inequalities (LMIs), compared with the corresponding fault-tolerant controller with fixed gains. A real application example about river pollution process is presented to show the effectiveness of the proposed method.

• Proceedings of the IEEK Conference
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• summer
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• pp.97-102
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• 2004

The very complicated proplem in spatial processing is effects of phading (Multipath and Delay Spread) and co-channel interference (CCI). The phading is one of principal causes, that form inter-symbol interference (ISI). Spatial-Temproral Adaptive Filter (STAF) has been taken as a solution of this problem, because it can suppress both these types of interference. But the performance of STAF exposes some elemental limitations, in which are the slow convergence of adaptive process and computational complexity. The cause of this is that, STAF must treat a large quantity of information in both space and time. The way that master these limitation is a use of Subband Spatial-Temproral Adaptive Filter (SSTAF). SSTAF reduce computational complexity by pruning off samples of signal and thus it lost some information in time. This draw on attennation of output SINR of SSTAF. The article analyse a optimal solution of this problem by introducing SSTAF with nonuniform weighted distribution.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.264-270
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• 2003

Application of the Filtered-X LMS adaptive filter to active noise control requires to estimate the transfer characteristics between the output and the error signal of the adaptive canceler. In this paper, we derive an adaptive control algorithm and analyze its convergence behavior when the acoustic noise is assumed to consist of multiple sinusoids. The results of the convergence analysis of the Filtered-X LMS algorithm indicate that the effects of parameter estimation inaccuracy on the convergence behavior of the algorithm are characterize by two distinct components : Phase estimation error and estimated magnitude. In particular, the convergence of the Filtered-X LMS algorithm is shown to be strongly affected by the accuracy of the phase response estimate. Simulation results of the algorithm are presented which support the theoretical convergence analysis.

• Journal of Power Electronics
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• v.14 no.3
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• pp.608-620
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• 2014

In this paper, an adaptive neural phase-locked loop (AN-PLL) based on adaptive linear neuron is proposed for grid-connected doubly fed induction generator (DFIG) synchronization. The proposed AN-PLL architecture comprises three stages, namely, the frequency of polluted and distorted grid voltages is tracked online; the grid voltages are filtered, and the voltage vector amplitude is detected; the phase angle is estimated. First, the AN-PLL architecture is implemented and applied to a real three-phase power supply. Thereafter, the performances and robustness of the new AN-PLL under voltage sag and two-phase faults are compared with those of conventional PLL. Finally, an application of the suggested AN-PLL in the grid-connected DFIG-decoupled control strategy is conducted. Experimental results prove the good performances of the new AN-PLL in grid-connected DFIG synchronization.

• Journal of Navigation and Port Research
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• v.31 no.7
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• pp.563-568
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• 2007

In this paper, an adaptive neural network controller and its application to automatic berthing control of ship is presented. The neural network controller is trained online using adaptive interaction technique without any teaching data and off-line training phase. Firstly, the neural networks used to control rudder and propeller during automatic berthing process are presented. Secondly, computer simulations of automatic ship berthing are carried out in Pusan bay to verify the proposed controller under the influence of wind disturbance and measurement noise. The results of simulation show good performance of the developed berthing control system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.838-842
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• 2011

This paper proposes an online adaptive neuro-controller for a wind energy conversion system (WECS) that is a highly nonlinear system intrinsically. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing neural network in the controller design in this paper. The proposed adaptive neural control scheme using radial-basis function network (RBFN) needs no system parameters to meet control objectives. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.