• Title, Summary, Keyword: Markov

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A Study on Markov Chains Applied to informetrics (마코프모형의 계량정보학적 응용연구)

  • Moon, Kyung-Hwa
    • Journal of Information Management
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    • v.30 no.2
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    • pp.31-52
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    • 1999
  • This paper is done by studying two experimental cases which utilize the stochastic theory of Markov Chains, which is used for forecasting the future and by analyzing recent trend of studies. Since the study of Markov Chains is not applied to the Informetrics to a high degree in Korea. It is also proposed that there is a necessity for further study on Markov Chains and its activation.

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Prediction of Future Land use Using Times Series Landsat Images Based on CA (Cellular Automata)-Markov Technique (시계열 Landsat 영상과 CA-Markov기법을 이용한 미래 토지이용 변화 예측)

  • Lee, Yong-Jun;Pack, Geun-Ae;Kim, Seong-Joon
    • Proceedings of the KSRS Conference
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    • pp.55-60
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    • 2007
  • The purpose of this study is to evaluate the temporal land cover change by gradual urbanization of Gyeongan-cheon watershed. This study used the five land use of Landsat TM satellite images(l987, 1991, 2001, 2004) which were classified by maximum likelihood method. The five land use maps examine its accuracy by error matrix and administrative district statistics. This study analyze land use patterns in the past using time.series Landsat satellite images, and predict 2004 year land use using a CA-Markov combined CA(Cellular Automata) and Markov process, and examine its appropriateness. Finally, predict 2030, 2060 year land use maps by CA-Markov model were constructed from the classified images.

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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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Harmonics Analysis of Railroad Systems using Markov Chain (Markov Chain을 이용한 철도계통의 고조파 분석)

  • Song, Hak-Seon;Lee, Seung-Hyuk;Kim, Jin-O;Kim, Hyung-Chul
    • Proceedings of the KIEE Conference
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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.

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A Prediction Method using Markov chain in DTN (DTN에서 Markov Chain을 이용한 노드의 이동 예측 기법)

  • Jeon, Il-Kyu;Shin, Gyu-young;Kim, Hyeng-jun;Oh, Young-jun;Lee, Kang-Whan
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.111-112
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    • 2015
  • 본 논문에서는 Delay Tolerant Networks(DTNs)에서 Markov chain으로 노드의 속성 정보 변화율을 분석하여 노드의 이동 경로를 예측하는 알고리즘을 제안한다. 기존 DTN에서 예측기반 라우팅 기법은 노드가 미리 정해진 스케줄에 따라 이동한다. 이러한 네트워크에서는 스케줄을 예측할 수 없는 환경에서 노드의 신뢰성이 낮아진다. 본 논문에서는 일정 구간의 노드의 속성 정보의 시간에 따른 변화율을 Markov chain을 이용하여 노드의 이동 경로를 예측하는 알고리즘을 제안한다. 제안하는 알고리즘은 노드의 속성 정보 중 노드의 속도와 방향성을 근사한 후, 변화율을 분석하고 이로부터 Markov chain을 이용하여 확률전이 매트릭스를 생성하여 노드의 이동 경로를 예측하는 알고리즘이다. 주어진 모의실험 환경에서 노드의 이동 경로 예측을 통해 중계 노드를 선정하여 라우팅 함으로써 네트워크 오버헤드와 전송 지연 시간이 감소함을 보여주고 있다.

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Performance Evaluation of the WiMAX Network Based on Combining the 2D Markov Chain and MMPP Traffic Model

  • Saha, Tonmoy;Shufean, Md. Abu;Alam, Mahbubul;Islam, Md. Imdadul
    • 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.

An Improved Reinforcement Learning Technique for Mission Completion (임무수행을 위한 개선된 강화학습 방법)

  • 권우영;이상훈;서일홍
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.9
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    • pp.533-539
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    • 2003
  • Reinforcement learning (RL) has been widely used as a learning mechanism of an artificial life system. However, RL usually suffers from slow convergence to the optimum state-action sequence or a sequence of stimulus-response (SR) behaviors, and may not correctly work in non-Markov processes. In this paper, first, to cope with slow-convergence problem, if some state-action pairs are considered as disturbance for optimum sequence, then they no to be eliminated in long-term memory (LTM), where such disturbances are found by a shortest path-finding algorithm. This process is shown to let the system get an enhanced learning speed. Second, to partly solve a non-Markov problem, if a stimulus is frequently met in a searching-process, then the stimulus will be classified as a sequential percept for a non-Markov hidden state. And thus, a correct behavior for a non-Markov hidden state can be learned as in a Markov environment. To show the validity of our proposed learning technologies, several simulation result j will be illustrated.

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.

FEYNMAN-KAC SEMIGROUPS, MARTINGALES AND WAVE OPERATORS

  • Van Casteren, Jan A.
    • Journal of the Korean Mathematical Society
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    • v.38 no.2
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    • pp.227-274
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    • 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.

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Thermal Transfer Analysis of Micro Flow Sensor using by Markov Chain MCM (Markov 연쇄 MCM을 이용한 마이크로 흐름센서 열전달 해석)

  • Cha, Kyung-Hwan;Kim, Tae-Yong
    • 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.