• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.4
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• pp.110-124
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• 1996

This study was conducted to simulate long seres of synthetic monthly flows by multi-season first order Markov model with selection of best fitting frequency distribution, harmonic synthetic and harmonic regression models and to make a comparison of statistical parameters between observes and synthetic flows of five watersheds in Geum river system. The results obtained through this study can be summarized as follow. 1. Both gamma and two parameter lognormal distributions were found to be suitable ones for monthly flows in all watersheds by Kolmogorov-Smirnov test. 2. It was found that arithmetic mean values of synthetic monthly flows simulated by multi-season first order Markov model with gamma distribution are much closer to the results of the observed data in comparison with those of the other models in the applied watersheds. 3. The coefficients of variation, index of fluctuation for monthly flows simulated by multi-season first order Markov model with gamma distribution are appeared closer to those of the observed data in comparison with those of the other models in Geum river system. 4. Synthetic monthly flows were simulated over 100 years by multi-season first order Markov model with gamma distribution which is acknowledged as a suitable simulation modal in this study.

• Korean Journal of Cognitive Science
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• v.24 no.2
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• pp.95-109
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• 2013

With the rapid evolution of the personal device environment, the demand for natural language applications is increasing. This paper proposes a morpheme segmentation and part-of-speech tagging model, which provides the first step module of natural language processing for many languages; the model is designed for mobile devices with limited hardware resources. To reduce the number of morpheme candidates in morphological analysis, the proposed model uses a method that adds highly possible morpheme candidates to the original outputs of a conventional left-longest-match-preference method. To reduce the computational cost and memory usage, the proposed model uses a method that simplifies the process of calculating the observation probability of a word consisting of one or more morphemes in a conventional hidden Markov model.

• ETRI Journal
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• v.5 no.2
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• pp.3-8
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• 1983

This paper derives the Markov model to calculate the reliability of the Digital Switching System being developed by KETRI. Using the failure states extracted from the system in the course of the modelling, we calculated the reliability of both the service grade and the function of the system. Especially, by including the repair rate into the model, we took optimum advantage of theMarkov process and solved the difficulties in the calculation by reducing the number of states of the system.

• Journal of the Korea Society for Simulation
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• v.13 no.3
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• pp.1-10
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• 2004

The cell loss probability required in the ATM network is in the range of 10$^{-9}$ ∼10$^{-12}$ . If Monte Carlo simulation is used to analyze the performance of the ATM node, an enormous amount of computer time is required. To obtain large speed-up factors, importance sampling may be used. Since the Markov-modulated processes have been used to model various high-speed network traffic sources, we consider discrete time single server queueing systems with Markov-modulated arrival processes which can be used to model an ATM node. We apply importance sampling based on the Large Deviation Theory for the performance evaluation of, MMBP/D/1/K, ∑MMBP/D/1/K, and two stage tandem queueing networks with Markov-modulated arrival processes and deterministic service times. The simulation results show that the buffer overflow probabilities obtained by the importance sampling are very close to those obtained by the Monte Carlo simulation and the computer time can be reduced drastically.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.167-172
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• 2005

This paper introduces a method which improves the performance of the identification of splice sites in the genomic DNA sequence of eukaryotes. This method combines a low order Markov model in series with a neural network for the predictions of splice sites. The lower order Markov model incorporates the biological knowledge surrounding the splice sites as probabilistic parameters. The Neural network takes the Markov encoded parameters as the inputs and produces the prediction. Two types of neural networks are used for the comparison. This method reduces the computational complexity and shows encouraging accuracy in the predictions of splice sites when applied to several standard splice site dataset.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.3
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• pp.83-91
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• 1999

In order to build and manage an ATM network effectively under several types of control methods, it is necessary to estimate the performance of the equipments in various viewpoints, especially of ATM multiplexer. As for the method to model the input stream into the ATM multiplexer, many researches have been done to characterize it by, such as, fluid flow, MMPP(Markov Modulated Poisson Process), or MMDP (Markov Modulated Deterministic Process). We introduce an MRP(Markov Renewal Process) to model the input stream which has proper structure to represent the burst traffic with high correlation. In this paper, we build a model for aggregated heterogeneous ON-OFF sources of ATM traffic by MRP. We make discrete time MR/D/1/B queueing system, whose input process is the superposed MRP and present a performance analysis by finding CLP(Cell Loss Probability). A simulation is done to validate our algorithm.

• Journal of Applied Reliability
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• v.16 no.4
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• pp.313-322
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• 2016

Purpose: We introduce ways to employ Markov chain model to evaluate the effect of preventive maintenance process. While the preventive maintenance process decreases the failure rate of each subsystems, it increases the downtime of the system because the system can not work during the maintenance process. The goal of this paper is to introduce ways to analyze this trade-off. Methods: Markov chain models are employed. We derive the availability of the system consisting of N repairable subsystems by the methods under various maintenance policies. Results: To validate our methods, we apply our models to the real maintenance data reports of military truck. The error between the model and the data was about 1%. Conclusion: The models developed in this paper fit real data well. These techniques can be applied to calculate the availability under various preventive maintenance policies.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.649-659
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• 2013

We use a Markov chain model to analyze the Korean Baseball League. We derive the distributions of the number of runs scored and the number of batters that complete their turn at bat in a baseball game using the time inhomogeneous Markov chain. The model is tested with real data produced from the 2011 Korean Baseball League.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1305-1310
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• 2008

Traditional maintenance planning is based on a constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is desirable to be scheduled by RCM(Reliability-Centered Maintenance) evaluation. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system and avoiding or removing maintenance actions that are not strictly necessary. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by the stepwise Markov model in detail. Also, because the system is not continuously monitored, the inspection is considered. In case study, simulation results about RCM will be shown using the real historical data of combustion turbine generating unit in Korean power systems.