• Annual Conference on Human and Language Technology
• /
• 1991.10a
• /
• pp.267-278
• /
• 1991

본 논문은 한글 정보원을 Markov source로 구현하였을 때 요구되는 terra byte 이상의 방대한 기억 공간의 점유를 해결하기 위해, 이에 대한 통계자료를 조사하고 이를 기초로 기억 공간을 줄일 수 있는 방안을 제안하였다. 제안된 방식에 의해 한글 정보원을 천이확률에 따라서 구현시 paged list 구조로 7차 이상의 Markov 한국어 정보원을 수백 Kbyte의 기억 공간으로 구현 할 수 있었다. 그리고, Markov 한국어 정보원의 활용도를 넓히기 위하여 backward Markov 정보원을 제안하였다. 본 연구에서 제안한 방법은 한글 문장에서 손실된 단어의 수정뿐만이 아니라 기타 Markov source를 한글에 적용하는 모든 분야에 기초적인 자료로 활용될 것으로 기대된다.

• Journal of Korean Institute of Industrial Engineers
• /
• v.17 no.1
• /
• pp.117-126
• /
• 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 Korean Institute of Telematics and Electronics B
• /
• v.32B no.12
• /
• pp.1663-1669
• /
• 1995

This paper is a study on the recognition of Korean proverb using neural network and Markov model. The neural network uses, at the stage of training neurons, features such as the rate of zero crossing, short-term energy and PLP-Cepstrum, covering a time of 300ms long. Markov models were generated by the recognized phoneme strings. The recognition of words and proverbs using Markov models have been carried out. Experimental results show that phoneme and word recognition rates are 81. 2%, 94.0% respectively for Korean proverb recognition experiments.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 1982.10a
• /
• pp.51-55
• /
• 1982

The analysis of Korean language by the first order markov source is carried out. The calculated entropy of the first order Markov source is also included. The results presented here are new data. The data can be useful in designing the keyboard pattern of terminal and the automatic discrimination of monosyllable in Korean language.

• Journal of the Korean Mathematical Society
• /
• v.41 no.2
• /
• pp.309-318
• /
• 2004

We consider a nonlinear AR-ARCH type process subject to Markov-switching and give sufficient conditions for geometric ergodicity of the process. Existence of moments is also obtained.

• Journal of Korea Water Resources Association
• /
• v.42 no.3
• /
• pp.213-225
• /
• 2009

A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.5
• /
• pp.1013-1019
• /
• 2016

This paper describes a novel Context-awareness Markov Chain Prediction (CMCP) algorithm based on movement prediction using Markov chain in Delay Tolerant Network (DTN). The existing prediction models require additional information such as a node's schedule and delivery predictability. However, network reliability is lowered when additional information is unknown. To solve this problem, we propose a CMCP model based on node behaviour movement that can predict the mobility without requiring additional information such as a node's schedule or connectivity between nodes in periodic interval node behavior. The main contribution of this paper is the definition of approximate speed and direction for prediction scheme. The prediction of node movement forwarding path is made by manipulating the transition probability matrix based on Markov chain models including buffer availability and given interval time. We present simulation results indicating that such a scheme can be beneficial effects that increased the delivery ratio and decreased the transmission delay time of predicting movement path of the node in DTN.

• Journal of the military operations research society of Korea
• /
• v.11 no.1
• /
• pp.111-129
• /
• 1985

Markov chains with large transition probability matrices occur in many applications such as manpowr models. Under certain conditions the state space of a stationary discrete parameter finite Markov chain may be partitioned into subsets, each of which may be treated as a single state of a smaller chain that retains the Markov property. Such a chain is said to be 'lumpable' and the resulting lumped chain is a special case of more general functions of Markov chains. There are several reasons why one might wish to lump. First, there may be analytical benefits, including relative simplicity of the reduced model and development of a new model which inherits known or assumed strong properties of the original model (the Markov property). Second, there may be statistical benefits, such as increased robustness of the smaller chain as well as improved estimates of transition probabilities. Finally, the identification of lumps may provide new insights about the process under investigation.

• Journal of the Korean Mathematical Society
• /
• v.38 no.2
• /
• pp.227-274
• /
• 2001

In this paper we intended to discuss the following topics: (1) Notation, generalities, Markov processes. The close relationship between (generators of) Markov processes and the martingale problem is exhibited. A link between the Korovkin property and generators of Feller semigroups is established. (2) Feynman-Kac semigroups: 0-order regular perturbations, pinned Markov measures. A basic representation via distributions of Markov processes is depicted. (3) Dirichlet semigroups: 0-order singular perturbations, harmonic functions, multiplicative functionals. Here a representation theorem of solutions to the heat equation is depicted in terms of the distributions of the underlying Markov process and a suitable stopping time. (4) Sets of finite capacity, wave operators, and related results. In this section a number of results are presented concerning the completeness of scattering systems (and its spectral consequences). (5) Some (abstract) problems related to Neumann semigroups: 1st order perturbations. In this section some rather abstract problems are presented, which lie on the borderline between first order perturbations together with their boundary limits (Neumann type boundary conditions and) and reflected Markov processes.

• Proceedings of the KIEE Conference
• /
• 2005.11b
• /
• pp.230-233
• /
• 2005

This paper proposes power qualify assessment using Markov Chain applied to Ergodic theorem. The Ergodic theorem introduces the state of aperiodic, recurrent, and non-null. The proposed method using Markov Chain presents very well generated harmonic characteristics according to the traction's operation of electric railway system. In case of infinite iteration, the characteristic of Markov Chain that converges on limiting probability Is able to expected harmonic currents posterior transient state. TDD(Total Demand Distortion) is also analyzed in expected current of each harmonic. The TDD for power quality assesment is calculated using Markov Chain theory in the Inceon international airport IAT power system.