• Title/Summary/Keyword: 마코프 모델링

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Constructing Human Mobility Model from Positioning Data using Hidden Markov Model (은닉 마코프 모델링 기법을 사용한 위치 정보에서 인간 이동 모델 도출)

  • Ryu, Seung Ho;Song, Ha Yoon;Kim, Hyunuk
    • Proceedings of the Korea Information Processing Society Conference
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    • 2012.11a
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    • pp.1277-1280
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    • 2012
  • GPS장비의 보급으로 인한 위치정보 수집이 용이해짐에 따라서 보다 현실적인 인간의 이동패턴을 구할 수 있게 되었다. 그에 따라 GPS 장비나 스마트폰을 이용하여 일정기간의 위치정보를 수집하였다. 수집된 위치 데이터를 이용하여 자주 방문하는 장소를 기점으로 인간 이동패턴을 은닉 마코프 방법을 이용하여 도출하였다.. 결과적으로 은닉 마코프 모델의 Baum-Welch 알고리즘으로 생성된 모델은 장소간 이동에 대해서는 효과적으로 표현을 하였음을 확인하다.

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Markov Modeling of Multiclass Loss Systems (멀티클래스 손실시스템의 마코프 모델링)

  • Na, Seong-Ryong
    • The Korean Journal of Applied Statistics
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    • v.23 no.4
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    • pp.747-757
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    • 2010
  • This paper studies the Markov modeling of multiclass loss systems supporting several kinds of customers. The concept of unit for loss systems is introduced and the method of equal probability allocation among units is especially considered. Equilibrium equations and limiting distribution of the loss systems are studied and loss probabilities are computed. We analyze an example of a simple system to gain an insight about general systems.

Multiclass loss systems with several server allocation methods (여러 서버배정방식의 멀티클래스 손실시스템 연구)

  • Na, Seongryong
    • The Korean Journal of Applied Statistics
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    • v.29 no.4
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    • pp.679-688
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    • 2016
  • In this paper, we study multiclass loss systems with different server allocation methods. The Markovian states of the systems are defined and their effective representation is investigated. The limiting probabilities are derived based on the Markovian property to determine the performance measures of the systems. The effects of the assignment methods are compared using numerical solutions.

Fast Text Line Segmentation Model Based on DCT for Color Image (컬러 영상 위에서 DCT 기반의 빠른 문자 열 구간 분리 모델)

  • Shin, Hyun-Kyung
    • The KIPS Transactions:PartD
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    • v.17D no.6
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    • pp.463-470
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    • 2010
  • We presented a very fast and robust method of text line segmentation based on the DCT blocks of color image without decompression and binary transformation processes. Using DC and another three primary AC coefficients from block DCT we created a gray-scale image having reduced size by 8x8. In order to detect and locate white strips between text lines we analyzed horizontal and vertical projection profiles of the image and we applied a direct markov model to recover the missing white strips by estimating hidden periodicity. We presented performance results. The results showed that our method was 40 - 100 times faster than traditional method.

Performance Improvement in Speech Recognition by Weighting HMM Likelihood (은닉 마코프 모델 확률 보정을 이용한 음성 인식 성능 향상)

  • 권태희;고한석
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.2
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    • pp.145-152
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    • 2003
  • In this paper, assuming that the score of speech utterance is the product of HMM log likelihood and HMM weight, we propose a new method that HMM weights are adapted iteratively like the general MCE training. The proposed method adjusts HMM weights for better performance using delta coefficient defined in terms of misclassification measure. Therefore, the parameter estimation and the Viterbi algorithms of conventional 1:.um can be easily applied to the proposed model by constraining the sum of HMM weights to the number of HMMs in an HMM set. Comparing with the general segmental MCE training approach, computing time decreases by reducing the number of parameters to estimate and avoiding gradient calculation through the optimal state sequence. To evaluate the performance of HMM-based speech recognizer by weighting HMM likelihood, we perform Korean isolated digit recognition experiments. The experimental results show better performance than the MCE algorithm with state weighting.