• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.484-486
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• 2001

본 논문에서는 의사 2차원 은닉 마르코프 모델의 구조로 생성한 마르코프 체인형 확률모형에 의한 인쇄체문자 이미지의 모델링에 대해 논한다. 이미지 데이터에서 바로 모델을 실시간 생성하며 문자 인식 및 검출에 응용할 수 있다. 실험에 의하면, 이 방법을 통해 특정 낱말이 포함된 문장에서 숫자를 인식, 한글을 검출할 수 있음을 확인하였다.

• 중국학논총
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• no.72
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• pp.123-143
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• 2021

自2000年以來, 中國通過經濟的高速增長迅速成爲世界第二大經濟體。与其他發展中國家一樣, 中國通過以政府爲主導的經濟增長方式推動了城市化進程進而助長了住房需求的增加和住房价格的上漲, 幷由此造成貧富差距不斷加大等各种社會問題。本硏究主要分析影響中國住房供給因素的中國住房生態系統。 通過自2001-2019年馬爾可夫鏈模型和效應模型的分析結果表明, 不同地區的城市化發展程度和收入的變化等与住房供需的相關因素, 存在有意義的顯著差异。特别是人口密度和收入最高的地區, 大部分在20年的持續城市化和收入差距的影響下没有發生變化, 幷且大部分在集群内移動。考慮住房城市化變化因素和收入變化因素, 20年投資供需价格變化彈性約爲0.628, 銷售需求价格變化彈性約爲0.748。换言之, 在中國住房也是具有財務屬性的商品。基于這一論点, 如果中國政府實施符合住房供需關系的住房供給政策, 卽可縮小貧富差距, 也將能够實現收入再分配和促進經濟增長兩个目標。

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.94-99
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• 2015

With the recent machine learning paradigm of using nonparametric Bayesian statistics and statistical inference based on random sampling, the Dirichlet distribution finds many uses in a variety of graphical models. It is a multivariate generalization of the gamma distribution and is defined on a continuous (K-1)-simplex. This paper presents a sampling method for a Dirichlet distribution for the problem of dividing an integer X into a sequence of K integers which sum to X. The target samples in our problem are all positive integer vectors when multiplied by a given X. They must be sampled from the correspondingly gridded simplex. In this paper we develop a Markov Chain Monte Carlo (MCMC) proposal distribution for the neighborhood grid points on the simplex and then present the complete algorithm based on the Metropolis-Hastings algorithm. The proposed algorithm can be used for the Markov model, HMM, and Semi-Markov model for accurate state-duration modeling. It can also be used for the Gamma-Dirichlet HMM to model q the global-local duration distributions.

• Journal of the Korean Geographical Society
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• v.51 no.4
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• pp.525-539
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• 2016

This study attempts to predict the changes in the spatial distribution of occupational employment in Korea by applying Markov Chain Model. For the purpose we analyze the job-related migration pattern and estimate the transition probability with the last six years job-related migration data. By applying the Chapman-Kolmogorov equation based on the transition probability, we predict the changes in the spatial distribution of occupational employment for the next ten years. The result reveals that the employment of professional jobs is predicted to increase at every city and region except Seoul, while the employment of elementary labor jobs is predicted to increase slightly in Seoul. In particular, Gangwon-do and Chuncheongdo are predicted to increase in the employment of all occupational jobs.

• Journal of the Korean Geographical Society
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• v.52 no.1
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• pp.123-141
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• 2017

This study predicted urban green spaces for 2020 based on two scenarios keeping or freeing the green-belt in the Daegu metropolitan city using a hybrid Cellular Automata(CA)-Markov model and analyzed the spatial dynamics of urban green spaces between 2009 and 2020 using a land cover change detection technique and spatial metrics. Markov chain analysis was employed to derive the transition probability for projecting land cover change into the future for 2020 based on two land cover maps in 1998 and 2009 provided by the Ministry of Environment. Multi-criteria evaluation(MCE) was adopted to develop seven suitability maps which were empirically derived in relation to the six restriction factors underlying the land cover change between the years 1998 and 2009. A hybrid CA-Markov model was then implemented to predict the land cover change over an 11 year period to 2020 based on two scenarios keeping or freeing the green-belt. The projected land cover for 2009 was cross-validated with the actual land cover in 2009 using Kappa statistics. Results show that urban green spaces will be remarkably fragmented in the suburban areas such as Dalseong-gun, Seongseo, Ansim and Chilgok in the year 2020 if the Daegu metropolitan city keeps its urbanization at current pace and in case of keeping the green-belt. In case of freeing the green-belt, urban green spaces will be fragmented on the fringes of the green-belt. It is thus required to monitor urban green spaces systematically considering the spatial change patterns identified by this study for sustainably managing them in the Daegu metropolitan city in the near future.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of Korea Multimedia Society
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• v.3 no.6
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• pp.566-574
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• 2000

Although a numbed of variants of 2D HMM have been proposed in the literature, they are, in a word, too simple to model the variabilities of images for diverse classes of objects; they do not realize the modeling capability of the 1D HMM in 2D. Thus the author thinks they are poor substitutes for the HMM in 2D. The new model proposed in this paper is a hidden Markov lattice or, we can dare say, a 2D HMM with the causality of top-down and left-right direction. Then with the addition of a lattice constraint, the two algorithms for the evaluation of a model and the maximum likelihood estimation of model parameters are developed in the theoretical perspective. It is a more natural extension of the 1D HMM. The proposed method will provide a useful way of modeling highly variable patterns such as offline cursive characters.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.4
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• pp.694-711
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• 2016

This study analyzes the spatial characteristics of knowledge industry which has shown relatively rapid growth in the low-growth economy situation in recent years. In particular, we catch hold of the locational characteristics of the knowledge service industry which occupies the highest ratio by professional-expert jobs favoured by young generations, as well as estimate their occupational employment opportunities. By applying Location Quotient(LQ) and LISA, we reveal the spatial distribution patterns of publishing business, information service business and education service business in the Seoul Metropolitan area, and examine the changes in the spatial patterns during the last ten years. In order to understand the socio-economic factors which explain their locations, we apply the stepwise multiple regression analysis. Furthermore, we predict the changes distribution of Knowledge service industrial employment by applying Markov Chain Model. As the result, we found their clusters at the specific locations, while there is the significant variations in the socio-economic variables related their locations respectively. The related job opportunities of the knowledge service businesses in the Seoul Metropolitan area are predicted steady growth trend for the next four years, even though dull or stagnant trend is expected for other industries. This study provides basic resources to the planning for young generation employment problem.

• Smart Media Journal
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• v.1 no.2
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• pp.12-22
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• 2012

Today we are witnessing an increasingly widespread use of cameras in our lives for video surveillance, robot vision, and mobile phones. This has led to a renewed interest in computer vision in general and an on-going boom in human activity recognition in particular. Although not particularly fancy per se, human gait is inarguably the most common and frequent action. Early on this decade there has been a passing interest in human gait recognition, but it soon declined before we came up with a systematic analysis and understanding of walking motion. This paper presents a set of DBN-based models for the analysis of human gait in sequence of increasing complexity and modeling power. The discussion centers around HMM-based statistical methods capable of modeling the variability and incompleteness of input video signals. Finally a novel idea of extending the discrete state Markov chain with a continuous density function is proposed in order to better characterize the gait direction. The proposed modeling framework allows us to recognize pedestrian up to 91.67% and to elegantly decode out two independent gait components of direction and posture through a sequence of experiments.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.219-224
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• 2015

This research estimates the Mixture of Normals of households saving rate in Korea. Our sample is MDSS, micro-data in 2014 and Gibbs algorithm is used to estimate the Mixture of Normals. Evidences say some results. First, Gibbs algorithm works very well in estimating the Mixture of Normals. Second, Saving rate data has at least two components, one with mean zero and the other with mean 29.4%. It might be that households would be separated into high saving group and low saving group. Third, analysis of Mixture of Normals cannot answer that question and we find that income level and age cannot explain our results.