• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.79-94
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• 2018

The purpose of this study is to specify a probabilistic tracking mechanism for customer luxury purchase implemented by hidden Markov model, Bayesian inference, customer satisfaction and net promoter score. In this paper, we have designed a probabilistic model based on customer's actual data containing purchase or non-purchase states by tracking the SPC chain : customer satisfaction -> customer referral -> purchase/non-purchase. By applying hidden Markov model and Viterbi algorithm to marketing theory, we have developed the statistical model related to probability theories and have found the best purchase pattern scenario from customer's purchase records.

• Spatial Information Research
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• v.5 no.2
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• pp.177-183
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• 1997

S1he concept of a class in the land-use classification system can be equally applied to a class in the land-use-change classification. The maximum likelihood method using linear discriminant function and Markov transition matrix method were integrated to a synthetic modeling effort in order to project spatial allocation of land-use-change and quantitative assignment of that prediction as a whole. The algorithm of both the multivariate discriminant function and the Markov chain matrix were discussed and the test of synthetic model on the study area was resulted in the projection of '90 year as well as '95 year land -use classification. The accuracy and the issue of modeling improvement were discussed eventually.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1555-1562
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• 2017

A loose part monitoring system is used to identify unexpected loose parts in a nuclear reactor vessel or steam generator. It is still necessary for the mass estimation of loose parts, one function of a loose part monitoring system, to develop a new method due to the high estimation error of conventional methods such as Hertz's impact theory and the frequency ratio method. The purpose of this study is to propose a mass estimation method using a Markov decision process and compare its performance with a method using an artificial neural network model proposed in a previous study. First, how to extract feature vectors using discrete cosine transform was explained. Second, Markov chains were designed with codebooks obtained from the feature vector. A 1/8-scaled mockup of the reactor vessel for OPR1000 was employed, and all used signals were obtained by impacting its surface with several solid spherical masses. Next, the performance of mass estimation by the proposed Markov model was compared with that of the artificial neural network model. Finally, it was investigated that the proposed Markov model had matching error below 20% in mass estimation. That was a similar performance to the method using an artificial neural network model and considerably improved in comparison with the conventional methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.1010-1019
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• 2004

In this paper, we represent Nakagami-m fading channel as finite-State Markov Channel (FSMC) and analyze the performance of wireless communication system with varying the fading channel condition. In FSMC model, the received signal's SNR is divided into finite intervals and these intervals are formed into Markov chain states. Each state is modeled by a BSC and the transition probability is dependent upon the physical characterization of the channel. The steady state probability and average symbol error rate of each state and transition probability are derived by numerical analysis and FSMC model is formed with these values. We found that various fading channels can be represented with FSMC by changing state transition index. In fast fading environment in which state transition index is large, the channel can be viewed as i.i.d. channel and on the contrary, in slow fading channel where state transition index is small, the channel can be represented by simple FSMC model in which transitions occur between just adjacent states. And we applied the proposed FSMC model to analyze the coding gain of random error correcting code on various fading channels via computer simulation.

• Journal of Korea Water Resources Association
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• v.38 no.8 s.157
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• pp.595-604
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• 2005

Precipitation simulation for making the data size larger is an important task for hydrologic analysis. The simulation can be divided into two major categories which are the parametric and nonparametric methods. Also, precipitation simulation depends on time intervals such as daily or hourly rainfall simulations. So far, Markov model is the most favored method for daily precipitation simulation. However, most models are consist of state transition probability by using the homogeneous Markov chain model. In order to make a state vector, the small size of data brings difficulties, and also the assumption of homogeneousness among the state vector in a month causes problems. In other words, the process of daily precipitation mechanism is nonstationary. In order to overcome these problems, this paper focused on the nonparametric method by using uni-variate and multi-variate when simulating a precipitation instead of currently used parametric method.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.2 no.2
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• pp.125-135
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• 1998

In this paper we define the entropy rate and stationary Markov chain and we show the monotonicity of entropy per element and prove that the random tree $T_n$ grows linearly with n.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.347-362
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• 1999

강수 형태 및 강수량을 동시에 고려하는 1차의 마코브 연쇄-의존 모델을 고차의 모델로 확장하였다. 남한의 53개 지역의 강수량 자료에 대해 계절별로 마코브 연쇄의 차수를 결정하였고, 고차의 마코브 연쇄-의존 모델을 적용하여 강수량의 분포특성을 살펴 보았다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.1
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• pp.133-142
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• 2000

In this note we define typical set and differential entropy for continuous random variables. Using Markov chain, we show that the various properties of the mutual information and entropies (theorems 3.2 and 3.4 ) and show the properties of typical set in continuous random variables( lemma 4.2 and theorem 4.3.)

• Bulletin of the Korean Mathematical Society
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• v.38 no.3
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• pp.495-504
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• 2001

In this paper we consider the (generalized) autoregressive model with conditional heteroscedasticity (ARCH/GARCH models). We willing give conditions under which strict stationarity, ergodicity and the functional central limit theorem hold for the corresponding models.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.285-290
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• 2000

We noted a property of a stationary distribution on the matrix C, which is the covariance matrix of order statistics of standard normal distribution That is the sup norm of th powers of C is ee' divided by its dimension. The matrix C can be taken as a transition probability matrix in an acyclic Markov chain.