• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.515-522
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• 1999

A repair process of a system consisting of both perfect repairs and minimal repairs is introduced. The type of repair, when the system fails, is determined by an embedded two state Markov chain. We study several stochastic properties of the process including the preservation of ageing properties and the monotonicities of the time between successive repairs. After assigning repair costs to the process, we also show that an optimal repair policy uniquely exists, if the underlying life distribution of the system has DMRL.

• Communications for Statistical Applications and Methods
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• v.21 no.6
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• pp.513-520
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• 2014

We study numerical methods to obtain the stationary probabilities of continuous-time Markov chains whose embedded chains are periodic. The power method is applied to the balance equations of the periodic embedded Markov chains. The power method can have the convergence speed of exponential rate that is ambiguous in its application to original continuous-time Markov chains since the embedded chains are discrete-time processes. An illustrative example is presented to investigate the numerical iteration of this paper. A numerical study shows that a rapid and stable solution for stationary probabilities can be achieved regardless of periodicity and initial conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2036-2040
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• 2013

In this paper, we consider two dynamic spectrum access schemes in cognitive network with two independent and identically distributed channels. Under the first scheme, secondary users switch channel only after transmission failure. On the other hand, under the second one, they switch channel only after successful transmission. We develop a mathematical model to investigate the performance of the second one and analyze the model using 3-dimensional embedded Markov chain. Numerical results and simulations are presented to compare between the two schemes.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.1-10
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• 2011

As requirements of embedded system get complicated, the tool for analyzing the reliability of embedded software is being needed. A probabilistic modeling is used as the way of analyzing the reliability of a software and to apply it to embedded software controlling multiple devices. So, it is necessary to specialize that to embedded software. Also, existing reliability analyzers should measure the transition probability of each condition in different ways and doesn't consider reusing the model once used. In this paper, we suggest a reliability analyzer for embedded software using embedded software Markov chin model and a unit testing tool. Embedded software Markov chain model is model specializing Markov chain model which is used for analyzing reliability to an embedded software. And a unit testing tool has host-target structure which is appropriate to development environment of embedded software. This tool can analyze the reliability more easily than existing tool by automatically measuring the transition probability between units for analyzing reliability from the result of unit testing. It can also directly apply the test result updated by unit testing tool by representing software model as a XML oriented document and has the advantage that many developers can access easily using the web oriented interface and SVN store. In this paper, we show reliability analyzing of a example by so doing show usefulness of reliability analyzer.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.36-41
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• 2005

페트리 네트(Petri Nets)는 이산 사건 시스템을 모델링할 수 있는 그래픽하고, 수학적인 도구이다. 본 연구는 유연제조 시스템을 확률적인 페트리 네트(Stochastic Petri Nets)중의 하나인 임베디드 마코프 체인(Embeded Markov Chain)에 도입하고, 임베디드 마코프 체인의 방법 중에 하나인 일반화된 확률적 페트리 네트(Generalized Stochastic Perti Nets)에 적용시켰다. 그리고 결과치의 정확성을 알아내기 위하여, 페트리 네트 시뮬레이션과 아레나를 사용하여 실행하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4180-4196
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• 2017

In this paper, we analyze average capacity of an amplify-and-forward (AF) cooperative communication system model in multi-relay multiuser networks. In contrast to conventional cooperative networks, relays in the considered network have no embedded energy supply. They need to rely on the energy harvested from the signals broadcasted by the source for their cooperative information transmission. Based on this structure, a two-step selection scheme is proposed considering both channel state information (CSI) and battery status of relays. Assuming each relay has infinite or finite energy storage for accumulating the energy, we use the infinite or finite Markov chain to capture the evolution of relay batteries and certain simplified assumptions to reduce computational complexity of the Markov chain analysis. The approximate closed-form expressions for the average capacity of the proposed scheme are derived. All theoretical results are validated by numerical simulations. The impacts of the system parameters, such as relay or user number, energy harvesting threshold and battery size, on the capacity performance are extensively investigated. Results show that although the performance of our scheme is inferior to the optimal joint selection scheme, it is still a practical scheme because its complexity is much lower than that of the optimal scheme.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.455-464
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• 2005

In this paper, we analyze a queueing system with overload control by arrival rates. This paper is motivated by overload control to prevent congestion in telecommunication networks. The arrivals occur dependent upon queue length. In other words, if the queue length increases, the arrivals may be reduced. By considering the burstiness of traffics in telecommunication networks, we assume the arrival to be a Markov-modulated Poisson process. The analysis by the embedded Markov chain method gives to us the performance measures such as loss and delay. The effect of performance measures on system parameters also is given throughout the numerical examples.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.63-75
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• 2002

Most attempts at Bayesian analysis of neural networks involve hierarchical modeling. We believe that similar results can be obtained with simpler models that require less computational effort, as long as appropriate restrictions are placed on parameters in order to ensure propriety of posterior distributions. In particular, we adopt a model first introduced by Lee (1999) that utilizes an improper prior for all parameters. Straightforward Gibbs sampling is possible, with the exception of the bias parameters, which are embedded in nonlinear sigmoidal functions. In addition to the problems posed by nonlinearity, direct sampling from the posterior distributions of the bias parameters is compounded due to the duplication of hidden nodes, which is a source of multimodality. In this regard, we focus on sampling from the marginal posterior distribution of the bias parameters with Markov chain Monte Carlo methods that combine traditional Metropolis sampling with a slice sampler described by Neal (1997, 2001). The methods are illustrated with data examples that are largely confined to the analysis of nonparametric regression models.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.5
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• pp.243-248
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• 2013

Reliability evaluation is important task in embedded system. It can avoid potential failures and manage the vulnerable components of embedded system effectively. Dynamic fault tree analysis is one of the reliability evaluation methods. It can represent dynamic characteristics of a system such as fault & error recovery, sequence-dependent failures. In this paper, the steering system, which is embedded system in vehicles, is represented using dynamic fault tree. We evaluate the steering system using approximation algorithm based on Simpson's rule. A set of simulation results shows that proposed method overcomes the low accuracy of classic approximation method without requiring no excessive calculation time of the Markov chain method.

• Journal of Satellite, Information and Communications
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• v.7 no.3
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• pp.8-15
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• 2012

Adaptive transmission technique is an effective means to counter-measure rain attenation that is one of the most significant factors degrading link quality in satellite communication systems. This paper introduces a simulator for adaptive transmission technique to compensate rain attenuation. In the simulator, a dynamic rain attenuation model is loaded, which was developed to synthesize Korean rain attenuation dynamics at a frequency band of Ka. It is a Markov chain model with rain attenuation parameters extracted from the rain attenuation data measured per second. In addition, various transmission schemes are embedded so that a user defined simulations can be performed. This paper demonstrates simulation results of adaptive schemes in comprison with fixed schemes, and show the efficiency of the adaptive schemes to compensate the rain attenuation.