• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.2
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• pp.52-62
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• 2013

A stochastic Markov process (MP) model has been developed for evaluating the probability of failure of the armor unit of rubble-mound breakwaters as a function of time. The mathematical MP model could have been formulated by combining the counting process or renewal process (CP/RP) on the load occurrences with the damage process (DP) on the cumulative damage events, and applied to the armor units of rubble-mound breakwaters. Transition probabilities have been estimated by Monte-Carlo simulation (MCS) technique with the definition of damage level of armor units, and very well satisfies some conditions constrained in the probabilistic and physical views. The probabilities of failure have been also compared and investigated in process of time which have been calculated according to the variations of return period and safety factor being the important variables related to design of armor units of rubble-mound breakwater. In particular, it can be quantitatively found how the prior damage levels can effect on the sequent probabilities of failure. Finally, two types of methodology have been in this study proposed to evaluate straightforwardly the repair times which are indispensable to the maintenance of armor units of rubble-mound breakwaters and shown several simulation results including the cost analyses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1887-1898
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• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.10 no.2
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• pp.29-35
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• 1991

In this paper, we discuss the method of recognizing allophone unit isolated words using hidden Markov model(HMM). Frist we constructed allophone lexicon by extracting allophones from training data and by training allophone HMMs. And then to recognize isolated words using allophone HMMs, it is necessary to construct word dictionary which contains information of allophone sequence and inter-allophone transition probability. Allophone sequences are represented by allophone HMMs. To see the effects of inter-allophone transition probability and to determine optimal probabilities, we performend some experiments. And we showed that small number of traing data and simple train procedure is needed to train word HMMs of allophone sequences and that not less performance than word unit HMM is obtained.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.111-118
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• 2014

This paper presents an efficient Markov feature extraction method for detecting splicing forged images. The Markov states used in our method are composed of the difference between DCT coefficients in the adjacent blocks. Various first-order Markov state transition probabilities are extracted as features for splicing detection. In addition, we propose a feature reduction algorithm by analysing the distribution of the Markov probability. After training the extracted feature vectors using the SVM classifier, we determine whether the presence of the image splicing forgery. Experimental results verify that the proposed method shows good detection performance with a smaller number of features compared to existing methods.

• Journal of Environmental Science International
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• v.6 no.2
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• pp.95-106
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• 1997

The characteristics on the transtion probabilities and periodicity for the daily precipitation occurrence in Korean peninsula are investigated by applying the Markov chain properties to daily precipitation occurrence. In order to examine the responses of Markov Chain properties to the applied period and their magnitudes, three cases (Case A: 1956~ 1985 at 14 stations, Case B: 1965~ 1994 at 14 stations, and Case C: 1985~ 1994 at 63 stations) are considered In this study. The transition probabilities from wet day to wet day for all cases are about 0.50 and in summer, especially July, are higher. In addition, considering them in each station we can find that they are the highest at Ullung-do and lowest at Inchon for all cases. The annual equilibrium probabilities of a wet day appear 0.31 In Case A, 0.30 Case B, and 0. 29 Case C, respectively. This may explain that as the data-period used becomes shorter, the higher the equilibrium probability is. The seasonal distributions of equilibrium probabilities are appeared the lowest(0.23~0.28) in winter and the highest(more than 0.39) in spring and monthly in .truly and in October, repectively. The annual mean wet duration for all cases is 2.04 days in Case A, 1.99 Case B, and 1.89 Case C, repectively. The weather cycle obtained from the annual mean wet and dry duration is 6.54~6.59 days, which are closely associated with the movement of synoptic systems. And the statistical tests show that the transitions of daily precipitation occurrence for all cases may have two-state first Markov chain property, being the stationarity in time and heterogeneity in space.

• Communications of the Korean Mathematical Society
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• v.9 no.3
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• pp.701-710
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• 1994

Suppose ${X_n}$ is a Markov process taking values in some arbitrary space $(S, \varphi)$ with n-stemp transition probability $$ P^{(n)}(x, B) = Prob(X_n \in B$\mid$X_0 = x), x \in X, B \in \varphi.$$ We shall call a Markov process with transition probabilities $P{(n)}(x, B)$ $\phi$-irreducible for some non-trivial $\sigma$-finite measure $\phi$ on $\varphi$ if whenever $\phi(B) > 0$, $$ \sum^{\infty}_{n=1}{2^{-n}P^{(n)}}(x, B) > 0, for every x \in S.$$ A non-trivial $\sigma$-finite measure $\pi$ on $\varphi$ is called invariant for ${X_n}$ if $$ \int{P(x, B)\pi(dx) = \pi(B)}, B \in \varphi $$.

• Journal of Communications and Networks
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• v.16 no.2
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• pp.193-201
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• 2014

In cognitive radio networks, the packet transmissions of the secondary users (SUs) can be interrupted randomly by the primary users (PUs). That is to say, the PU packets have preemptive priority over the SU packets. In order to enhance the quality of service (QoS) for the SUs, we propose a spectrum access strategy with an ${\alpha}$-Retry policy. A buffer is deployed for the SU packets. An interrupted SU packet will return to the buffer with probability ${\alpha}$ for later retrial, or leave the system with probability (1-${\alpha}$). For mathematical analysis, we build a preemptive priority queue and model the spectrum access strategy with an ${\alpha}$-Retry policy as a two-dimensional discrete-time Markov chain (DTMC).We give the transition probability matrix of the Markov chain and obtain the steady-state distribution. Accordingly, we derive the formulas for the blocked rate, the forced dropping rate, the throughput and the average delay of the SU packets. With numerical results, we show the influence of the retrial probability for the strategy proposed in this paper on different performance measures. Finally, based on the trade-off between different performance measures, we construct a cost function and optimize the retrial probabilities with respect to different system parameters by employing an iterative algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.549-555
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• 2000

We propose an approximate algorithm to analyze the queuing system with n bursty and heterogeneous arrival processes. Each input process is modeled by Interrupted Bernoulli Process(IBP). We approximate N arrival processes by a single state variable and subsequently simplify the transition probability matrix of the Markov chain associated with these N arrival processes. Using this single state variable of arrival processes, we describe the state of the queuing system and analyze the system numerically with the reduced transition probability matrix. We compute the queue length distribution, the delay distribution, and the loss probability. Comparisons with simulation data show that the approximation algorithm has a good accuracy.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.77-87
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• 2019

The first grade zones in Ecology and Nature Map are important regions for the conservation of the ecosystem, but it would be degraded by various anthropogenic factors. This study analyzes the relationship between potential land cover change and degradation of the first grade zones using land cover transition probability. As a result, it was shown that most of the first grade zones with degraded were converted from forest to urban(5.1%), cropland(27.2%), barren(11.0%) and grass(27.5%) in Gangwon and forest to urban(18.0%), cropland(15.3%), grass(28.4%), barren(12.3%) in Gyeonggi. The result of the logistic regression analysis showed that the probability of degradation of first grade zone was higher in area where was expected the higher probability of urban, cropland, barren, grass transition. The barren transition probability was the most influential and grass was the next highest. There were regional differences in the probability of urban transition and cropland transition, and the urban transition probability was more influential in Gyeonggi-do. This is because development pressure such as housing site development is high in Gyeonggi-do. Due to the limitations of the Act on Mountain Districts Management, even in the first grade zones, the grade may be degraded. Therefore, if Ecology and Nature Map are used to prevent deforestation or conversion of mountainous districts, it may contribute to the preservation of the ecosystem.