• Journal of Korean Institute of Industrial Engineers
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• v.11 no.2
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• pp.1-9
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• 1985

In this study, two stage transfer line with limited repair capability is modeled to formulate optimal dynamic repair priority policy. The method of Markov Chains is used to analyze the analytical model of this line. An efficient algorithm is developed, utilizing the block tridiagonal structure of the transition probability matrix, to obtain the steady state probabilities and system performance measures, such as the steady state production rate of the line and the average in-process inventory in the interstage buffer.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.12
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• pp.1-8
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• 1997

In this paper, a new method of state code assignment for reduction of switching activities of state transition in asynchronous circuits is proposed. The algorithm is based on a on-hot code and modifies it to reduce switching activities. To estimate switching activities as a cost functions we introduce state transition probability (STP). AS a results, the proposed algorithm has an advantage of 60% over with the conventional code assignment in terms of switching and code length of state assignment.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.2
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• pp.131-139
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• 1999

An easy and efficient method is proposed for a computation of reliability of preprocessing filters in the fire control system when the sensor data are frequently unreliable depending on the operation environment. It computes state transition probability matrix after modeling filter states as a Markov process, and computing false alarm and detection probability of each filter state under the given sensor failure probability. It shows that two important indices such as distributed state probability and error variance can be derived easily for a reliability assessment of the given sensor fusion system.

• Bulletin of the Korean Chemical Society
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• v.26 no.11
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• pp.1735-1737
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• 2005

We obtain probabilities at a crossing of two linearly time-dependent potentials that are constantly coupled to the other by solving a time-dependent Schrödinger equation. We find that the system which was initially localized at one state evolves to split into both states at the crossing. The probability splitting depends on the coupling strength $V_0$ such that the system stays at the initial state in its entirety when $V_0$ = 0 while it is divided equally in both states when $V_0 \rightarrow {\infty}$ . For a finite coupling the probability branching at the crossing is not even and thus a complete probability transfer at $t \rightarrow {\infty}$ is not achieved in the linear potential crossing problem. The Landau-Zener formula for transition probability at $t \rightarrow {\infty}$ is expressed in terms of the probabilities at the crossing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.1010-1019
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• 2004

In this paper, we represent Nakagami-m fading channel as finite-State Markov Channel (FSMC) and analyze the performance of wireless communication system with varying the fading channel condition. In FSMC model, the received signal's SNR is divided into finite intervals and these intervals are formed into Markov chain states. Each state is modeled by a BSC and the transition probability is dependent upon the physical characterization of the channel. The steady state probability and average symbol error rate of each state and transition probability are derived by numerical analysis and FSMC model is formed with these values. We found that various fading channels can be represented with FSMC by changing state transition index. In fast fading environment in which state transition index is large, the channel can be viewed as i.i.d. channel and on the contrary, in slow fading channel where state transition index is small, the channel can be represented by simple FSMC model in which transitions occur between just adjacent states. And we applied the proposed FSMC model to analyze the coding gain of random error correcting code on various fading channels via computer simulation.

• Journal of Korean Society of Forest Science
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• v.67 no.1
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• pp.52-59
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• 1984

A Gentan probability q(j) is the probability that a newly planted forest will be felled at age-class j. A future change in growing stock and yield of the forests can be predicted by means of this probability. On the other hand a state of the forests is described in terms of an n-vector whose components are the areas of each age-class. This vector, called age-class vector, flows in a n-1 dimensional simplex by means of $n{\times}n$ matrices, whose components are the age-class transition probabilities derived from the Gentan probabilities. In the simplex there exists a fixed point, into which an arbitrary forest age vector sinks. Theoretically this point means a normal state of the forest. To each age-class-transition matrix there corresponds a single normal state; this means that there are infinitely many normal states of the forests.

• Journal of Korea Game Society
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• v.14 no.5
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• pp.127-136
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• 2014

NPC in game is an important factor to increase the fun of the game by cooperating with player or confrontation with player. NPC's behavior patterns in the previous games are limited. Also, there is not much difference in NPC's ability among the existing games because it's designed to FSM. Therefore, players who have matched with NPCs which have the characteristics may have difficulty to play. This paper is for improving the problem and production and evaluation of the game NPC behavior model based on wolves hunting model in real life. To achieve it, first, the research surveys and studies behavior states for wolves to capture prey in the real world. Secondly, it is implemented using the Unity3D engine. Third, this paper compares the implemented state transition probability to state transition probability in real world, state transition probability in general game. The comparison shows that the number of state transitions of NPCs increases, proportions of implemented NPC behavior patterns converges to probabilities of state transition in real-world. This means that the aggressive behavior pattern of NPC implemented is similar to the wolf hunting behavior pattern of the real world, and it can thereby provide more player experience.

• Bulletin of the Korean Chemical Society
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• v.11 no.6
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• pp.557-560
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• 1990

The transition from one stable steady state branch to another stable steady state branch in a simple metabolic system with positive feedback is discussed with the aid of the bimodal Gaussian probability distribution method. Fluctuations lead to transitions from one stable steady state branch to the other, so that the bimodal Gaussian evolves to a new distribution. We also obtain the fractional occupancies in the two stable steady states in terms of a parameter characterizing conditions of the system.

• Bulletin of the Korean Chemical Society
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• v.15 no.8
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• pp.627-631
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• 1994

For the Schlogl model with the first order transition under the influence of the multiplicative noise singular at the unstable steady state, the effects of the parameters on the stationary probability distributions obtained by the Ito and Stratonovich methods are discussed and compared in detail.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.