• International Journal of Highway Engineering
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• v.19 no.5
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• pp.1-11
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• 2017

PURPOSES : This study is aimed at development of a stochastic pavement deterioration forecasting model using National Highway Pavement Condition Index (NHPCI) to support infrastructure asset management. Using this model, the deterioration process regarding life expectancy, deterioration speed change, and reliability were estimated. METHODS : Eight years of Long-Term Pavement Performance (LTPP) data fused with traffic loads (Equivalent Single Axle Loads; ESAL) and structural capacity (Structural Number of Pavement; SNP) were used for the deterioration modeling. As an ideal stochastic model for asset management, Bayesian Markov multi-state exponential hazard model was introduced. RESULTS:The interval of NHPCI was empirically distributed from 8 to 2, and the estimation functions of individual condition indices (crack, rutting, and IRI) in conjunction with the NHPCI index were suggested. The derived deterioration curve shows that life expectancies for the preventive maintenance level was 8.34 years. The general life expectancy was 12.77 years and located in the statistical interval of 11.10-15.58 years at a 95.5% reliability level. CONCLUSIONS : This study originates and contributes to suggesting a simple way to develop a pavement deterioration model using the total condition index that considers road user satisfaction. A definition for level of service system and the corresponding life expectancies are useful for building long-term maintenance plan, especially in Life Cycle Cost Analysis (LCCA) work.

• 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.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.147-158
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• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Journal of Korea Multimedia Society
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• v.12 no.4
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• pp.521-529
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• 2009

In this paper, we present a vision-based human primitive motion recognition method. It models the reference motion patterns, recognizes a user's motion, and measures the similarity between the reference action and the user's one. In order to recognize a motion, we provide a pattern modeling method based on the Hidden Markov Models. In addition, we provide a similarity measurement method between the reference motion and the user's one using the editing distance algorithm. Experimental results show that the recognition rate of ours is above 93%. Our method can be used in the motion recognizable games, the motion recognizable postures, and the rehabilitation training systems.

• IE interfaces
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• v.16 no.spc
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• pp.21-27
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• 2003

Objective of this paper is to develop the user mobility model(UMM) which is used for the performance analysis of location update and paging algorithm and at the same time, consider the user mobility pattern(UMP) in zone-based cellular networks. User mobility pattern shows correlation in space and time. UMM should consider these correlations of UMP. K-dimensional Markov chain is presented as a UMM considering them where the states of Markov chain are defined as the current location area(LA) and the consecutive LAs visited in the path. Also, a new two dimensional Markov chain composed of current LA and time interval is presented. Simulation results show that the appropriate size of K in the former UMM is two and the latter UMM reflects the characteristic of UMP well and so is a good model for the analytic method to solve the performance of location update and paging algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.203-211
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• 2010

This study examines a cointegrated vector autoregressive (VAR) model where parameters are subject to switch across the regimes in the term structure of interest rates. To employ the regime switching framework, the Markov-switching vector error correction model (MS-VECM) is allowed to the regime shifts in the vector of intercept terms, the variance-covariance terms, the error correction terms, and the autoregressive coefficient parts. The corresponding approaches are illustrated using the term structure of interest rates in the US Treasury bonds over the period of 1958 to 2009. Throughout the modeling procedure, we find that the MS-VECM can form a statistically adequate representation of the term structure of interest rate in the US Treasury bonds. Moreover, the regime switching effects are analyzed in connection with the historical government monetary policy and with the recent global financial crisis. Finally, the results from the comparisons both in information criteria and in forecasting exercises with and without the regime switching lead us to conclude that the models in the presence of regime dependence are superior to the linear VECM model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.4
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• pp.319-325
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• 2007

In case of more than 50km baseline length, the correlation between receivers is reduced. Therefore, there are still some rooms for improvement of its positional accuracy. In this paper, the stochastic modeling of the ionospheric delay is applied and its effects are analyzed. The data processing has been performed by constructing a Kalman filter with states of positions, ambiguities, and the ionospheric delays in the double differenced mode. Considering the medium or long baseline length, both double differenced GPS phase and code observations are used as observables and LAMBDA has been applied to fix the ambiguities. The ionospheric delay is stochastically modeled by well-known 1st order Gauss-Markov process. And the correlation time and variation of 1st order Gauss-Markov process are calculated. This paper gives analyzed results of developed algorithm compared with commercial software and Bernese.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1621-1629
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• 2016

This paper studies Bayesian pooling for analysis of categorical data from small areas. Many surveys consist of categorical data collected on a contingency table in each area. Statistical inference for small areas requires considerable care because the subpopulation sample sizes are usually very small. Typically we use the hierarchical Bayesian model for pooling subpopulation data. However, the customary hierarchical Bayesian models may specify more exchangeability than warranted. We, therefore, investigate the effects of pooling in hierarchical Bayesian modeling for the contingency table from small areas. In specific, this paper focuses on the methods of direct or indirect pooling of categorical data collected on a contingency table in each area through Dirichlet priors. We compare the pooling effects of hierarchical Bayesian models by fitting the simulated data. The analysis is carried out using Markov chain Monte Carlo methods.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.288-291
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• 2002

We characterize multipath fading channel dynamics at the packet level and analyze the corresponding data queueing performance in various environments. We identify the similarity between wire-line queueing analysis and wireless network per-formance analysis. The second order channel statistics, i.e. channel power spectrum, is fecund to play an important role in the modeling of multipath fading channels. However, it is identified that the first order statistics, i.e. channel CDF also has significant impact on queueing performance. We use a special Markov chain, so-called CMPP, throughout this paper.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.22-29
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• 2016

Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.