• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.772-779
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• 2006

The new model proposed in this paper is the hidden Markov mesh model or the 2D HMM with the causality of top-down and left-right direction. With the addition of the causality constraint, two algorithms for the evaluation of a model and the maximum likelihood estimation of model parameters have been developed theoretically which are based on the forward-backward algorithm. It is a more natural extension of the 1D HMM than other 2D models. The proposed method will provide a useful way of modeling highly variable image patterns such as offline cursive characters.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.681-682
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• 2006

일반적으로 축구 비디오 데이터는 멀티모달과 멀티레이어 속성을 지닌다. 이러한 데이터를 다루기 적합한 모델은 동적 베이지안 네트워크(Dynamic Bayesian Network: DBN) 형태의 위계적 은닉 마르코프 모델(Hierarchical Hidden Markov Model: HHMM)이다. 이러한 HHMM 중 다중속성의 특징들이 서로 상호작용하는 PHHMM(Product Hierarchical Hidden Markov Model)이 있다. 본 논문에서는 PHHMM 을 축구 경기의 Play/Break 이벤트 검색 및 분석에 적용하였고 바람직한 결과를 얻었다.

• Journal of the Korean Mathematical Society
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• v.61 no.4
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• pp.693-711
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• 2024

A Markov-modulated Bernoulli process is a generalization of a Bernoulli process in which the success probability evolves over time according to a Markov chain. It has been widely applied in various disciplines for modeling and analysis of systems in random environments. This paper focuses on providing analytical characterizations of the Markovmodulated Bernoulli process by introducing key metrics, including success period, failure period, and cycle. We derive expressions for the distributions and the moments of these metrics in terms of the model parameters.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.134-139
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• 2008

In this paper, we propose an analysis model for the performance of channel error probability in Stream Control Transmission Protocol (SCTP) using Markov model. In this model it is assumed that the compressor and decompressor work in Unidirectional Mode. And the average throughput of SCTP protocol is obtained by finding the throughputs of when the initial channel state is good or bad.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.135-145
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• 2014

The present study surveys Bayesian modeling structure for inferences about transition probabilities of Markov chain. The motivation of the study came from the data that shows transitional behaviors of emotionally disturbed children undergoing residential treatment program. Dirichlet distribution was used as prior for the multinomial distribution. The analysis with real data was implemented in WinBUGS programming environment. The performance of the model was compared to that of alternative approaches.

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

• 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.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.653-678
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• 2011

WiMAX is intended for fourth generation wireless mobile communications where a group of users are provided with a connection and a fixed length queue. In present literature traffic of such network is analyzed based on the generator matrix of the Markov Arrival Process (MAP). In this paper a simple analytical technique of the two dimensional Markov chain is used to obtain the trajectory of the congestion of the network as a function of a traffic parameter. Finally, a two state phase dependent arrival process is considered to evaluate probability states. The entire analysis is kept independent of modulation and coding schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2253-2258
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• 2008

To design micro flow sensor varying depending on temperature of driving heater in the detector of Oxide semiconductor, Markov chain MCM(MCMCM), which is a kind of stochastic and microscopic method, was introduced. The formulation for the thermal transfer equation based on the FDM to obtain the MCMCM solution was performed and investigated, in steady state case. MCMCM simulation was successfully applied, so that its application can be expanded to a three-dimensional model with inhomogeneous material and complicated boundary.

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.445-456
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• 1996

In this paper, we derive unbiased estimators for the sensitivities of expected performance measures in a Markov renewal process. We restrict our derivation to the performance measures during a busy cycle and apply smoothed perturbation analysis method to find those esti-mators. The results show all the terms in the derived estimators can be obtained from a single sample path.