• Title, Summary, Keyword: Markov model

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Development of Statistical Downscaling Model Using Nonstationary Markov Chain (비정상성 Markov Chain Model을 이용한 통계학적 Downscaling 기법 개발)

  • Kwon, Hyun-Han;Kim, Byung-Sik
    • Journal of Korea Water Resources Association
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    • v.42 no.3
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    • pp.213-225
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    • 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.

Two-Dimensional Model of Hidden Markov Mesh

  • Sin, Bong-Kee
    • 한국HCI학회:학술대회논문집
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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.

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Development of Daily Rainfall Simulation Model Based on Homogeneous Hidden Markov Chain (동질성 Hidden Markov Chain 모형을 이용한 일강수량 모의기법 개발)

  • Kwon, Hyun-Han;Kim, Tae Jeong;Hwang, Seok-Hwan;Kim, Tae-Woong
    • Journal of The Korean Society of Civil Engineers
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    • v.33 no.5
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    • pp.1861-1870
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    • 2013
  • A climate change-driven increased hydrological variability has been widely acknowledged over the past decades. In this regards, rainfall simulation techniques are being applied in many countries to consider the increased variability. This study proposed a Homogeneous Hidden Markov Chain(HMM) designed to recognize rather complex patterns of rainfall with discrete hidden states and underlying distribution characteristics via mixture probability density function. The proposed approach was applied to Seoul and Jeonju station to verify model's performance. Statistical moments(e.g. mean, variance, skewness and kurtosis) derived by daily and seasonal rainfall were compared with observation. It was found that the proposed HMM showed better performance in terms of reproducing underlying distribution characteristics. Especially, the HMM was much better than the existing Markov Chain model in reproducing extremes. In this regard, the proposed HMM could be used to evaluate a long-term runoff and design flood as inputs.

An Automatic Summarization of Call-For-Paper Documents Using a 2-Phase hidden Markov Model (2단계 은닉 마코프 모델을 이용한 논문 모집 공고의 자동 요약)

  • Kim, Jeong-Hyun;Park, Seong-Bae;Lee, Sang-Jo;Park, Se-Young
    • Journal of Korean Institute of Intelligent Systems
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    • v.18 no.2
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    • pp.243-250
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    • 2008
  • This paper proposes a system which extracts necessary information from call-for-paper (CFP) documents using a hidden Markov model (HMM). Even though a CFP does not follow a strict form, there is, in general, a relatively-fixed sequence of information within most CFPs. Therefore, a hiden Markov model is adopted to analyze CFPs which has an advantage of processing consecutive data. However, when CFPs are intuitively modeled with a hidden Markov model, a problem arises that the boundaries of the information are not recognized accurately. In order to solve this problem, this paper proposes a two-phrase hidden Markov model. In the first step, the P-HMM (Phrase hidden Markov model) which models a document with phrases recognizes CFP documents locally. Then, the D-HMM (Document hidden Markov model) grasps the overall structure and information flow of the document. The experiments over 400 CFP documents grathered on Web result in 0.49 of F-score. This performance implies 0.15 of F-measure improvement over the HMM which is intuitively modeled.

Application Markov State Model for the RCM of Combustion Turbine Generating Unit (Markov State Model을 이용한 복합화력 발전설비의 최적의 유지보수계획 수립)

  • Lee, Seung-Hyuk;Shin, Jun-Seok;Kim, Jin-O
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.2
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    • pp.248-253
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    • 2007
  • Traditional time based preventive maintenance is used to constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is scheduled by RCM(Reliability-Centered Maintenance) evaluation. 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 a Markov model. In case study, simulation results about RCM are used to the real historical data of combustion turbine generating units in Korean power systems.

Prediction of Mobile Phone Menu Selection with Markov Chains (Markov Chain을 이용한 핸드폰 메뉴 선택 예측)

  • Lee, Suk Won;Myung, Rohae
    • Journal of Korean Institute of Industrial Engineers
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    • v.33 no.4
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    • pp.402-409
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    • 2007
  • Markov Chains has proven to be effective in predicting human behaviors in the areas of web site assess, multimedia educational system, and driving environment. In order to extend an application area of predicting human behaviors using Markov Chains, this study was conducted to investigate whether Markov Chains could be used to predict human behavior in selecting mobile phone menu item. Compared to the aforementioned application areas, this study has different aspects in using Markov Chains : m-order 1-step Markov Model and the concept of Power Law of Learning. The results showed that human behaviors in predicting mobile phone menu selection were well fitted into with m-order 1-step Markov Model and Power Law of Learning in allocating history path vector weights. In other words, prediction of mobile phone menu selection with Markov Chains was capable of user's actual menu selection.

The Realization of Artificial Life to Adapt The Environment by Using The Markov Model

  • Kim, Do-Wan;Park, Wong-Hun;Chung, Jin-Wook;Hoon Kang
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.513-516
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    • 2003
  • In this paper, we designed a Artificial Life(AL) that acts the appropriate actions according to the user's action, environments and AL's feeling. To realize this AL, we used the Markov Model. We consisted of the chromosome by Markov Model and obtained the appropriate actions by Genetic Algorithm.

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A Soccer Video Analysis Using Product Hierarchical Hidden Markov Model (PHHMM(Product Hierarchical Hidden Markov Model)을 이용한 축구 비디오 분석)

  • Kim, Moo-Sung;Kang, Hang-Bong
    • Proceedings of the IEEK Conference
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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.

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Balanced mobility pattern generation using Random Mean Degree modification in Gauss Markov model for Mobile network (이동 네트워크를 위한 가우스 마코프 모델에서 평균 이동각도 조절을 통한 균형잡힌 이동 패턴 생성)

  • 노재환;이병직;류정필;하남구;한기준
    • Proceedings of the Korean Information Science Society Conference
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    • pp.502-504
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    • 2004
  • 이동성이 중요시되는 네트워크에서 특정 프로토콜의 성능 평가를 위해서는 노드의 이동패턴을 정확하게 표현할 수 있는 Mobility Model이 필요하다. 노드의 연속적인 이동패턴을 필요로 하는 Mobile Ad-hoc 네트워크를 위해선 Markov process 기반의 Gauss-Markov Mobility Model이 적절하다. 그러나 맵의 엣지 부근에서 노드 이동의 부적절한 처리로 인해, 기존의 Gauss-Markov Model은 편중된 이동 패턴을 야기한다. 본 논문은 엣지 부근의 평균 이동각도를 랜덤하게 조정함으로써 기존의 모델이 가진 문제를 해결하고, 시뮬레이션을 통해서 이를 검증한다.

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Codebook design for subspace distribution clustering hidden Markov model (Subspace distribution clustering hidden Markov model을 위한 codebook design)

  • Cho, Young-Kyu;Yook, Dong-Suk
    • Proceedings of the KSPS conference
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    • pp.87-90
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    • 2005
  • Today's state-of the-art speech recognition systems typically use continuous distribution hidden Markov models with the mixtures of Gaussian distributions. To obtain higher recognition accuracy, the hidden Markov models typically require huge number of Gaussian distributions. Such speech recognition systems have problems that they require too much memory to run, and are too slow for large applications. Many approaches are proposed for the design of compact acoustic models. One of those models is subspace distribution clustering hidden Markov model. Subspace distribution clustering hidden Markov model can represent original full-space distributions as some combinations of a small number of subspace distribution codebooks. Therefore, how to make the codebook is an important issue in this approach. In this paper, we report some experimental results on various quantization methods to make more accurate models.

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