• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.10
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• pp.685-691
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• 2017

In this study, the influence of mass flow rate on the isentropic efficiency of the steam turbine nozzle stage is investigated. A realistic three-dimensional numerical model, which is based on the compressible Navier-Stokes equations, is developed for the steam phase. The comprehensive conservation laws and a kinetic model for steam are investigated. With two different models for the three-dimensional geometry of the nozzle stage, the pressure and temperature distributions, velocity, Mach number. and Markov energy loss coefficient are calculated. A maximum efficiency of 96.66％ is found at a mass flow rate of 0.9 kg/s in model A. In model B, a maximum efficiency of 97.32％ is found at a rate of 1.6 kg/s. It is determined that the isentropic nozzle efficiency increases as the Markov energy loss coefficient decreases through a nearly linear relationship.

• Journal of Applied Reliability
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• v.16 no.4
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• pp.295-304
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• 2016

Purpose: This paper presents an application of Markov Process to reliability and availability analysis. In order to do that of analysis, we set up a specific case of Tablet PC and it's usage scenario. The case has it some spares and maintenance and repair processes. Methods: Different configurations of the tablet PC and as well as their functions are defined. The system configuration and calculated failure rates of components are modeled from Windchill Quality Solution. Two models, without a spare and with spare, are created and compared using Markov Process. The Matlab numerical analysis is used to simulate and show the change of state with time. Availability of the system is computed by determining the time the system stays in different states. Results: The mission availability and steady-state condition availability in accordance with the mission are compared and the availability of the system with spares have improved availability than without spares. Simulated data shows that downtime of the system increased which results in greater availability through the consideration of spares. Conclusion: There's many techniques and methods to do reliability and availability analysis and mostly are time-independent assumptions. But Markov Process, even though its steady-state and ergodic properties, can do time analysis any given time periods.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.8
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• pp.524-535
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• 2006

With rapid development of Internet technology, business management in an organization or an enterprise depends on Internet-based technology for the most part. Furthermore, as dependency and cohesiveness of network in the communication facilities are increasing, cyber attacks have been increased against vulnerable resource in the information system. Hence, to protect private information and computer resource, research for damage propagation is required in this situation. However the proposed traditional models present just mechanism for risk management, or are able to be applied to the specified threats such as virus or worm. Therefore, we propose the probabilistic model of damage propagation based on the Markov process, which can be applied to diverse threats in the information systems. Using the proposed model in this paper, we can predict the occurrence probability and occurrence frequency for each threats in the entire system.

• Journal of the Korean Statistical Society
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• v.35 no.1
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• pp.79-90
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• 2006

We consider the subdiagonal bilinear model and ARMA model with subdiagonal bilinear errors. Sufficient conditions for geometric ergodicity of associated Markov chains are derived by using results on generalized random coefficient autoregressive models and then strict stationarity and ,a-mixing property with exponential decay rates for given processes are obtained.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.120-120
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• 1996

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.688-689
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• 2013

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.861-870
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• 1999

An HMM statistical package with graphic user interface is constructed with C++ classes for HMM operations. An isolated word speech recognition experiment is conducted with nine English words.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.809-810
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• 2013

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.83-126
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• 2005

Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. Here we propose a nonparametric estimation approach that combines wavelet methods for non-equispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.225-233
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• 2011

Stock market prices, economic indices, trends and changes of social phenomena, etc. are categorized as time series data. Research on time series data has been prevalent for a while as it could not only lead to valuable representation of data but also provide future trends as well as changes in direction. We take a conventional model based approach, known as Markov chain modeling for the prediction on stock market prices. To improve prediction accuracy, we apply Markov modeling over carefully selected intervals of training data to fit the trend under consideration to the model. Another method we take is to apply clustering to data and build models of the resultant clusters. We confirmed that clustered models are better off in predicting, however, with the loss of prediction rate.