• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.732-735
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• 1999

A GA-based optimization technique is adopted in the paper to obtain optimal future control inputs for predictive control systems. For reliable future predictions of a process, we identify the underlying process with an NNARX model structure and investigate to reduce the volume of neural network based on the Lipschitz index and a criterion. Since most industrial processes are subject to their constraints, we deal with the input-output constraints by modifying some genetic operators and/or using a penalty strategy in the GAPC. Some computer simulations are given to show the effectiveness of the GAPC method compared with the adaptive GPC algorithm.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.190-193
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• 2003

The DOA(Direction Of Arrival) estimation to select a best beam for receiving a particular signal in switched beam antenna systems, and to shape the optimal beam in adaptive array antenna systems, is typically performed under the assumption that the target user motion is almost negligible. In this paper, we model the user as the time-varying source and adopt the LPA(Local Polynomial Approximation) tracking algorithm, proposed by Katkovnik, to solve the time-varying DOA estimation problem. Then, we compare the power spectrum functions between the LPA beamformer and the conventional beamformer, also, the normalized SNRs of each beamformer. The results show that the LPA beamformer is robuster than the conventional beamformer in tine-varying environments. In addition, in case of the conventional beamformer, more array elements give rise to more degradation in the aspect of SNR.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.5
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• pp.765-776
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• 1995

This paper describes a speaker adaptation method to improve the recognition performance of MLP(multiLayer Perceptron) based HMM(Hidden Markov Model) speech recognizer. In this method, we use lst-order linear transformation network to fit data of a new speaker to the MLP. Transformation parameters are adjusted by back-propagating classification error to the transformation network while leaving the MLP classifier fixed. The recognition system is based on semicontinuous HMM's which use the MLP as a fuzzy vector quantizer. The experimental results show that rapid speaker adaptation resulting in high recognition performance can be accomplished by this method. Namely, for supervised adaptation, the error rate is signifecantly reduced from 9.2% for the baseline system to 5.6% after speaker adaptation. And for unsupervised adaptation, the error rate is reduced to 5.1%, without any information from new speakers.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1680-1686
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• 1995

GAs, effective stochastic search algorithms based on the model of natural evolution and genetics, have been successfully applied to various optimization problems. When population size is not large, GAs often suffer from the phenomenon of premature convergence in which all chromosomes in the population lose the diversity of genes before they find the optimal solution. In this paper, we propose that a new heuristic that maintains the diversity of genes by adding some chromosomes with random mutation and selective mutation into population during evolution. And population size changes dynamically with supplement of new chromosomes. Experimental results for several test functions show that when population size is rather small and the length of chromosome is not long, this method is effective.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1119-1126
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• 1995

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1333-1343
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• 2016

Spectrum sharing in centrally controlled cognitive radio (CR) networks has been widely studied, however, research on channel access for distributively controlled individual cognitive users has not been fully characterized. This paper conducts an analysis of random channel access of cognitive users controlled in a distributed manner in a CR network. Based on the proposed estimation method, each cognitive user can estimate the current channel condition by using its own Markov-chain model and can compute its own blocking probability, collision probability, and forced termination probability. Using the proposed scheme, CR with distributed control (CR-DC), CR devices can make self-controlled decisions based on the status estimations to adaptively control its system parameters to communicate better.

• Journal of KSNVE
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• v.8 no.3
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• pp.441-449
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• 1998

A self-tuning control scheme, incorporated with the simplified 1st-order ARMAX(Auto-Regressive Moving Average eXogenous) model, for single rod hydraulic cylinder which has varying dynamic characteristics is presented here. An adaptive controller is developed for the system that uses feedforward and optimal feedback control for simultaneous parameter identification and tracking control. Through experimental results, the performance comparison of the self-tuning controller with a fixed gain proportional controller clearly shows its superior ability in handling load changes in quiescent states.

• Journal of the Korean Society of Visualization
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• v.2 no.2
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• pp.3-7
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• 2004

In this paper, computational aeroacoustics (CAA) method is used for flow-noise analysis and flow-noise visualization. High order high resolution scheme of optimized high order compact is used to resolve the small acoustic quantities and large flow quantities at the same time. An adaptive nonlinear artificial dissipation model and generalized characteristic boundary condition are also used. Aeolion tone noise, cavity noise, and jet noise are investigated. The visualizations of flow-noise are successful and characteristics of noise are studied. It is observed that the propagation directivity of noise is different with that of flow. With the help of CAA method, the visualization of noise is possible.

• International journal of advanced smart convergence
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• v.1 no.1
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• pp.6-13
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• 2012

An improved algorithm for image quality assessment is presented. First a simple model of human visual system, consisting of a nonlinear function and a 2-D filter, processes the input images. This filter has one user-defined parameter, whose value depends on the reference image. This way the algorithm can adapt to different scenarios. In the next step the average value of locally computed correlation coefficients between the two processed images is found. This criterion is closely related to the way in which human observer assesses image quality. Finally, image quality measure is computed as the average value of locally computed correlation coefficients, adjusted by the average correlation coefficient between the reference and error images. By this approach the proposed measure differentiates between the random and signal dependant distortions, which have different effects on human observer. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.647-648
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• 2015

최근 최저효율제를 대비한 프리미엄급 고효율 전동기의 필요성이 대두됨에 따라 유도전동기를 대체할 수 있는 고효율 전동기로써 영구자석 매입형 동기 릴럭턴스 모터(PMA-SynRM)가 각광받고 있다. 하지만 모터의 속도와 토크를 제어하기 위해 회전자 속도와 위치 정보를 측정할 수 있는 센서가 필요하고, 이 때 센서 설치의 공간적 문제, 센서의 유지/보수 및 시스템의 추가 비용 발생 등 많은 문제점들이 발생한다. 따라서 센서를 제거하기 위한 센서리스 위치 및 속도 제어에 관한 연구가 폭 넓게 진행되고 있다. 하지만 모터를 운전함에 따라 저항이나 역기전력 상수, 인덕턴스 등의 제정수의 변동이 발생하게 되고 센서리스 제어 시 이러한 변동에 의해서 추정 속도 및 위치에 오차가 발생하게 된다. 본 논문에서는 이러한 오차의 보상을 위해 MRAS(Model Reference Adaptive System) 방식을 적용한 센서리스 제어를 제안한다.