• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.153-156
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• 2001

This paper discusses a stochastic stabilization of Takagi-Sugeno (75) fuzzy system with Markovian input delay. The finite Markovian process is adopted to model the input delay of the overall control system. It is assumed that the zero and hold devices are used for control input. The continuous-time 75 fuzzy system with the Markovian input delay is discretized for easy handling delay, accordingly, the discretized 75 fuzzy system is represented by a discrete-time 75 fuzzy system with jumping parameters. The stochastic stabilizibility of the jump 75 fuzzy system is derived and formulated in terms of linear matrix inequalities (LMls).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.11 no.17
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• pp.81-85
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• 1988

This paper presents a model that considers combinations of rework, repair, replacement and scrapping. Policy-Iteration method of inspection is proposed for a serial manufacturing system whose repair cost, scrap cost and inspection cost. when it fails, can be formulated by Markovian approach. Policy-Iteration stops when new inspection policy is the same as previous inspection policy. A numerial example is presented.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.42-47
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• 2005

A single-server queueing model with the Batch Markovian Arrival Process and disaster ow correlated with the arrival process is analyzed. The numerically stable algorithm for calculating the steady state distribution of the system is presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.298-303
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• 2004

This paper deals with a stochastic stabilization of observer-based output-feedback control Takagi-Sugeno (T-S) fuzzy system with Markovian input delay. The finite Markovian process is adopted to model the input delay of the overall control system. It is assumed that the zero and hold devices are used for control input. The continuous-time T-S fuzzy system with the Markovian input delay is discretized for easy handling delay, accordingly, the discretized T-S fuzzy system is represented by a discrete-time T-S fuzzy system with jumping parameters. The stochastic stabilizability of the jump T-S fuzzy system is derived and formulated in terms of linear matrix inequalities (LMIs). The usefulness of the proposed algorithm is also certificated by simulation of 2 degree of freedom helicopter model.

• IE interfaces
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• v.24 no.4
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• pp.323-329
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• 2011

We develop a stochastic model to predict the score of a soccer match. We describe the scoring process of the soccer match as a markovian arrival process (MAP). To do this, we define a two-state underlying Markov chain, in which the two states represent the offense and defense states of the two teams to play. Then, we derive the probability vector generating function of the final scores. Numerically inverting this generating function, we obtain the desired probability distribution of the scores. Sample numerical examples are given at the end to demonstrate how to utilize this result to predict the final score of the match.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.139-144
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• 2011

We consider the stabilization problem for a class of networked control systems with random delays in the discrete-time domain. The controller-to-actuator and sensor-to-controller time-delays are modeled as two Markov chains, and the resulting closed-loop systems are Markovian jump nonlinear systems with two modes. The T-S (Takagi-Sugeno) fuzzy model is employed to represent a nonlinear system with Markovian jump parameters. The aim is to design a fuzzy controller such that the closed-loop Markovian jump fuzzy system is stochastically stable. The necessary and sufficient conditions on the existence of stabilizing fuzzy controllers are established in terms of LMIs (Linear Matrix Inequalities). It is shown that fuzzy controller gains are mode-dependent. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method.

• Journal of applied mathematics & informatics
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• v.5 no.3
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• pp.785-796
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• 1998

In this paper we propose a model of HIv population through method of phases with non-Markovian evolution of immi-gration. The analysis leads to an explicit differnetial equations for the generating functions of the total population size. The detection process of antibodies (against the antigen of virus) is analysed and an explicit expression for the correlation functions are provided. A measure of bunching is also introduced for some particular choice of parameters.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.997-1006
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• 2008

This paper proposes a new model by combining an infinite-server queueing model for multi-task processing software system with a perfect debugging model based on Markov process with two types of faults suggested by Lee et al. (2001). We apply this model for module and integration testing in the testing process. Also, we compute several measure, such as the expected number of tasks whose processes can be completed and the task completion probability are investigated under the proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.459-464
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• 2001

This paper discusses a stochastic stabilization of Takagi-Sugeno(TS) fuzzy system with Markovian input delay. The finite Markovian process is adopted to model the input delary of the overall control system. It is assumed that the zero and hold devices are used for control input. The continuous-time TS fuzzy system with the Markovian input delay is discretized for easy handling delay, according, the discretized TS fuzzy system is represented by a discrete-time TS fuzzy system with jumping parameters. The stochastic stabilizibility of the jump TS fuzzy system is derived and formulated in terms of linear matrix inequalities (LNIS)

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.57-64
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• 2000

In this paper we consider a Markovian perfect debugging model for which the software failure is caused by two types of faults, one which is easily detected and the other which is difficult to detect. When a failure occurs, a perfect debugging is immediately performed and consequently one fault is reduced from fault contents. We also treat the debugging time as a variable to develop a new debugging model. Several measures, including the distribution of first passage time to the specified number of removed faults, are also obtained using the proposed debugging model, Numerical examples are provided for illustrative purposes.