• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1279-1281
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• 1999

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied to achieving a best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1234-1239
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• 1990

In this paper, we propose a new type of decentralized learning automata for the control finite state Markov chains with unknown transition probabilities and rewards. In our scheme a .betha.-type learning automaton is associated with each state in which two or more actions(desisions) are available. In this decentralized learning automata system, each learning automaton operates, requiring only local information, to improve its performance under local environment. From simulation results, it is shown that the decentralized learning automata will converge to the optimal policy that produces the most highly total expected reward with discounting in all initiall states.

• Journal of Korea Multimedia Society
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• v.8 no.5
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• pp.670-678
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• 2005

A Cellular Automaton(CA) is a dynamical system in which space and time are discrete, the state of each cell is unite and is updated by local interaction. Since the characteristics of CA is diffusion and local interaction, CA is used by crypto-systems and VLSI structure. In this study, we proposed a hash function based on the concept of 2-dimensional cellular automata and analyzed the proposed hash function.

• Journal of the Korean Mathematical Society
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• v.36 no.4
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• pp.747-756
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• 1999

In ring theory it is well-known that a ring R is (von Neumann) regular if and only if all right R-modules are flat. But the analogous statement for this result does not hold for a monoid S. Hence, in sense of S-acts, Liu (]10]) showed that, as a weak analogue of this result, a monoid S is regular if and only if all left S-acts satisfying condition (E) ([6]) are flat. Moreover, Bulmann-Fleming ([6]) showed that x is a regular element of a monoid S iff the cyclic right S-act S/p(x, x2) is flat. In this paper, we show that the analogue of this result can be held for automata and them characterize regular semigroups by flat automata.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1328-1333
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• 2006

For fault diagnosis of machines and equipments, knowledge-based method has been used widely but has some limitations for complex systems. These can be covered by model-based method. As one kind of model-based method, Qualitative modeling diagnosis method is developed in this research. The developed method uses output signal only. In this method quantization of the output signal mattes automata which can characterize the flow of the signal pattern to normal and fault respectively. As an example of the qualitative diagnosis method, an electric tool which has faults at gear and bearing were examined in this research. The result shows that the developed method can diagnose the fault clearly for the two fault cases.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.4
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• pp.185-194
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• 2000

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied in order to achieve the best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.279-284
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• 2005

Knowledge based fault diagnosis has a limitation in determining the cause and scheme for the fault, because it detects faults from signal pattern only Therefore, model-based fault diagnosis is requested to determine the fault by analyzing output of the equipment from its dynamic model. This research shows a method how to devise the automaton of system as a model for normal and faulty condition through the reduction of handling data by quantization of vibration signals and the example which is concerning to the bearing of ATM. The developed model based fault diagnosis was applied to detect the faulty bearing of ATM, which results.

• Journal of the Chungcheong Mathematical Society
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• v.29 no.4
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• pp.553-563
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• 2016

We characterize periodicities of some characteristic matrices of cellular automata congured with rule 60 and intermediate boundary condition.

• Korean Journal of Mathematics
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• v.18 no.4
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• pp.357-368
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• 2010

In this note, we will characterize group orders of hybrid CA configured with rules 60 and 195 and that configured with rules 102 and 153.

• Journal of applied mathematics & informatics
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• v.1 no.1
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• pp.75-78
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• 1994

In this paper we define free torsion-free and torsion-free completely on an automaton. We prove some properties of them which are important