• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.3
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• pp.134-141
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• 2003

PID controllers with constant gains have been widely used in various control systems due to its powerful performance and easy implementation. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a nonlinear variable PR controller with immune feedback mechanism. An immune feedback mechanism is based on the functioning of biological T-cells, they include both an active term, which controls response speed. and an inhibitive term, which controls stabilization effect. Therefore, the proposed nonlinear PID controller is based on immune responses of biological. immune feedback mechanism which is the cell mediated immunity and In order to choose the optimal nonlinear PID controller games, we also propose the tuning algorithm of nonlinear function parameter in immune feedback mechanism. To verify performance of the proposed algorithm, the speed control of nonlinear DC motor are performed. Front the simulation results, we have found that the proposed algorithm is more superior to the conventional constant fain PID controller.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.203-204
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• 1998

ECG analysis is main techniques for diagnosing heart disease. In recent, some studies have been performed about detection of QT interval. But, it's difficult to detect QT interval because T wave is evasive. In this paper, we have detected peak point and end point of T wave and calculated QT interval. And the result has been compared with the other algorithm after detection of QT interval.

• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.515-530
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• 2022

A factor graph and belief propagation can be used for finding stochastic values of link weights in biological networks. However it is not easy to follow the process of use and so we presented the process with a toy network of three nodes in our prior work. We extend this work more generally and present numerical example for a network of 100 nodes.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.217-218
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• 1998

We propose a new digital beamforming algorithm using an interpolation filter in ultrasonic imaging systems. We compared the performances of the proposed algorithm to those of the conventional digital bemforming algorisms, post-beamformer and phase rotation beamformer, by a computer simulation and experiments. The results show that the proposed algorithm has better performance than the others.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.820-822
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• 1996

This paper proposes a method for seeking the scheme of substation restoration by using genetic algorithm. Genetic algorithm (GA), first introduced by John Holland, is becoming an important tool in machine learning and function optimization. GA is a searching or optimization algorithm based on Darwinian biological evolution principle. As a test system, we assume a simple substation system and for the transformer fault, the result is obtained.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.27-33
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• 2008

The clustering of gene expression data is used to analyze the results of microarray studies. This clustering is one of the frequently used methods in understanding degrees of biological change and gene expression. In biological research, MCL algorithm is an algorithm that clusters nodes within a graph, and is quick and efficient. We have modified the existing MCL algorithm and applied it to microarray data. In applying the MCL algorithm we put forth a simulation that adjusts two factors, namely inflation and diagonal tent and converted them by making use of Markov matrix. Furthermore, in order to distinguish class more clearly in the modified MCL algorithm we took the average of each row and used it as a threshold. Therefore, the improved algorithm can increase accuracy better than the existing ones. In other words, in the actual experiment, it showed an average of 70% accuracy when compared with an existing class. We also compared the MCL algorithm with the self-organizing map(SOM) clustering, K-means clustering and hierarchical clustering (HC) algorithms. And the result showed that it showed better results than ones derived from hierarchical clustering and K-means method.

• Journal of Communications and Networks
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• v.17 no.4
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• pp.384-393
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• 2015

This paper proposes an insect behavior-inspired routing algorithm for large-scale wireless mesh networks. The proposed algorithm is adapted from the behavior of an insect called Bombyx mori, a male silkmoth. Its unique behavior is its flying technique to find the source of pheromones. The algorithm consists of two steps: the shortest-path algorithm and the zigzag-path algorithm. First, the shortest-path algorithm is employed to transmit data. After half of the total hops, the zigzag-path algorithm, which is based on the movement of the male B. mori, is applied. In order to adapt the biological behavior to large-scale wireless mesh networks, we use a mesh topology for implementing the algorithm. Simulation results show that the total energy used and the decision time for routing of the proposed algorithm are improved under certain conditions.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.376-378
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• 2004

Genetic algorithm was motivated by biological evaluation and has been applied to many industrial applications as a powerful tool for mathematical optimizations. In this paper, a new genetic optimization algorithm is proposed. The proposed method is based on Mendel's law, especially dominance and recessive property. Homologous chromosomes are introduced to implement dominance and recessive property compared with the standard genetic algorithm. Because of this property of suggested genetic algorithm, homologous chromosomes looks like the chromosomes for the standard genetic algorithm, so we can use most of existing genetic operations with little effort. This suggested method searches the larger solution area with the less probability of the premature convergence than the standard genetic algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.107-114
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• 1994

Hardware implementation of the on-chip learning artificial neural networks is important for real-time processing. A pRAM model is based on probabilistic firing of a biological neuron and can be implemented in the VLSI circuit with learning capability. We derive a backpropagation learning algorithm for the pRAM networks and present its circuit implementation with stochastic computation. The simulation results confirm the good convergence of the learning algorithm for the pRAM networks.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.303-308
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• 1989

A new estimationn mothod of single-EP(evoked potential) using adaptive algorithm and paralnetrlc model is proposed. Since the EEG(eletroencephalogram) signal is stationary in short time interval the AR(autoregressive) parameters of the EEG are estimated by the Burg algorithm using the EEG of prestimulus interval. After stimulus, the single-EP is estimated by adaptive algorithm. The validity of this method is verified by the simulation for generated auditory single-EP based on parametric model.