• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.549-556
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• 2020

This paper proposes several suboptimal methods of designing a controller for a networked control system with state feedback where delay due to transmission error and transmission delay is modeled as a Markov process. A stability condition for a control system with Markov delay is found through an equivalent relationship that corresponding delay-dependent Lyapunov-Krasovskii functional has the same form of the Lyapunov function of an augmented control system. Several suboptimal methods of designing a controller from the stability condition are proposed to reduce complexity. A simple numerical experiment shows that a restricted subspace method which limits the search space of a matrix variable to a block diagonal form provides the best tradeoff between the complexity and performance.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.1
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• pp.117-126
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• 1991

When {X(t), t ${\geq}$ 0} is a Markov process representing time-varying system states, we develop efficient bounding methods for some time-dependent performance measures. We use the discretization technique for stochastically monotone Markov processes and a combination of discretization and uniformization for Markov processes with the stochastic convexity(concavity) property. Sufficient conditions for stochastic monotonocity and stochastic convexity of a Markov process are also mentioned. A simple example is given to demonstrate the validity of the bounding methods.

• Journal of the Korea Convergence Society
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• v.5 no.1
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• pp.9-15
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• 2014

Traffic accidents are caused by a variety of factors. Among the factors that cause traffic accidents are weather conditions at the time. There is a difference in the percentage of deaths according to traffic accidents, due to the weather conditions. In order to reduce the number of deaths due to traffic accidents, to predict the incidence of traffic accidents that occur in response to weather conditions is required. In this paper, it propose a model to predict the incidence of traffic accidents caused by weather conditions. Predictive modeling was applied to the theory of Markov processes. By applying the actual data for the proposed model, to predict the incidence of traffic accidents, it was compared with the number of occurrences in practice. In this paper, it is to support the development of traffic accident policy with the change of weather.

• Nuclear Engineering and Technology
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• v.25 no.1
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• pp.154-165
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• 1993

A stochastic method using continuous time Markov process is presented to model the one-dimensional convective nuclide transport in geologic media, which have usually heterogeneous feature in physical/geochemical parameters such as velocity, dispersion coefficient, and retardation factor resulting poor description by conventional deterministic advection-dispersion model. The primary desired quantities from a stochastic model are the mean values and variance of the state variables as a function of time. The time-dependent probability distributions of nuclides are presented for each discretized compartment given the volumetric groundwater flux and the intensity of transition. Since this model is discrete in medium space, physical/geochemical parameters which affect nuclide transport can be easily incorporated for the heterogeneous media as well as remarkably layered media having spatially varied parameters. Even though the Markov process model developed in this study was shown to be sensitive to the number of discretized compartments showing numerical dispersion as the number of compartments are increased, this could be easily calibrated by comparing with the analytical deterministic 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)

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• Nuclear Engineering and Technology
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• v.25 no.4
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• pp.529-538
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• 1993

A stochastic way using continuous time Markov process is presented to model the one-dimensional nuclide transport in fractured rock matrix as an extended study for previous work ［1］. A nuclide migration model by the continuous time Markov process for single planar fractured rock matrix, which is considered as a transient system where a process by which the nuclide is diffused into the rock matrix from the fracture may be no more time homogeneous, is compared with a conventional deterministic analytical solution. The primary desired quantities from a stochastic model are the expected values and variance of the state variables as a function of time. The time-dependent probability distributions of nuclides are presented for each discretized compartment of the medium given intensities of transition. Since this model is discrete in medium space, parameters which affect nuclide transport could be easily incorporated for such heterogeneous media as the fractured rock matrix and the layered porous media. Even though the model developed in this study was shown to be sensitive to the number of discretized compartment showing numerical dispersion as the number of compartments are decreased, with small compensating of dispersion coefficient, the model agrees well to analytical solution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.549-555
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• 2000

We propose an approximate algorithm to analyze the queuing system with n bursty and heterogeneous arrival processes. Each input process is modeled by Interrupted Bernoulli Process(IBP). We approximate N arrival processes by a single state variable and subsequently simplify the transition probability matrix of the Markov chain associated with these N arrival processes. Using this single state variable of arrival processes, we describe the state of the queuing system and analyze the system numerically with the reduced transition probability matrix. We compute the queue length distribution, the delay distribution, and the loss probability. Comparisons with simulation data show that the approximation algorithm has a good accuracy.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.85-94
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• 2009

In this work we propose an energy-efficient transmission strategy for wireless sensor networks that operate in a strict energy-constrained environment. Our transmission algorithm consists of two components: a binary-decision based transmission and a channel-aware backoff adjustment. In the binary-decision based transmission, we obtain the optimum threshold for successful transmission via Markov decision process (MDP) formulation. A channel-aware backoff adjustment, the second component of our proposal, is introduced to favor sensor nodes seeing better channel in terms of transmission priority. Extensive simulations are performed to verify the performance of our proposal over fading wireless channels.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.147-159
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• 1991

Stochastic convexity(concvity) of a stochastic process is a very useful concept for various stochastic optimization problems. In this study we first establish stochastic convexity of a certain class of Markov additive processes through the probabilistic construction based on the sample path approach. A Markov additive process is obtained by integrating a functional of the underlying Markov process with respect to time, and its stochastic convexity can be utilized to provide efficient methods for optimal design or for optimal operation schedule of a wide range of stochastic systems. We also clarify the conditions for stochatic monotonicity of the Markov process, which is required for stochatic convexity of the Markov additive process. This result shows that stochastic convexity can be used for the analysis of probabilistic models based on birth and death processes, which have very wide application area. Finally we demonstrate the validity and usefulness of the theoretical results by developing efficient methods for the optimal replacement scheduling based on the stochastic convexity property.