• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.243-250
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• 2008

This paper proposes a system which extracts necessary information from call-for-paper (CFP) documents using a hidden Markov model (HMM). Even though a CFP does not follow a strict form, there is, in general, a relatively-fixed sequence of information within most CFPs. Therefore, a hiden Markov model is adopted to analyze CFPs which has an advantage of processing consecutive data. However, when CFPs are intuitively modeled with a hidden Markov model, a problem arises that the boundaries of the information are not recognized accurately. In order to solve this problem, this paper proposes a two-phrase hidden Markov model. In the first step, the P-HMM (Phrase hidden Markov model) which models a document with phrases recognizes CFP documents locally. Then, the D-HMM (Document hidden Markov model) grasps the overall structure and information flow of the document. The experiments over 400 CFP documents grathered on Web result in 0.49 of F-score. This performance implies 0.15 of F-measure improvement over the HMM which is intuitively modeled.

• 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 Biosystems Engineering
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• v.29 no.5 s.106
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• pp.441-450
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• 2004

This study was performed to estimate mid and long term demands of a tractor, a rice transplanter, a combine and a grain dryer by using logistic curve function and Markov chain model. Field survey was done to decide some parameters far logistic curve function and Markov chain model. Ceiling values of tractor and combine fer logistic curve function analysis were 209,280 and 85,607 respectively. Based on logistic curve function analysis, total number of tractors increased slightly during the period analysed. New demand for combine was found to be zero. Markov chain analysis was carried out with 2 scenarios. With the scenario 1(rice price $10\%$ down and current supporting policy by government), new demand for tractor was decreased gradually up to 700 unit in the year 2012. For combine, new demand was zero. Regardless of scenarios, the replacement demand was increased slightly after 2003. After then, the replacement demand is decreased after the certain time. Two analysis of logistic owe function and Markov chain model showed the similar trend in increase and decrease for total number of tractors and combines. However, the difference in numbers of tractors and combines between the results from 2 analysis got bigger as the time passed.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.5
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• pp.326-338
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• 2006

In this paper, we propose the weighted Markov model for recommending the users' prefered contents in the environment with considering the users' transition of their content consumption mind according to the kind of contents providing in time. In general, TV viewers have an intention to consume again the preferred contents consumed in recent by them. In order to take into the consideration, we modify the preference transition matrix by providing weights to the consecutively consumed contents for recommending the users' preferred contents. We applied the proposed model to the recommendation of TV viewer's genre preference. The experimental result shows that our method is more efficient than the typical methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.6
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• pp.105-114
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• 2007

This study is to predict the spatial expansion of urban areas by applying CA(Cellular Automata)-Markov technique considering MCE(multi-criteria evaluation) and MOLA(multi-objective land allocation) of factor analysis. For the 10 administration districts$(3677.3km^2)$ including the whole Anseong-cheon watershed, the past six temporal land use data(1973, 1981, 1985, 1990, 1994, 2000) from Landsat satellite images were prepared. During this period, the urban area increased $233.71km^2$. Using the 36 indices composed of topological characteristics, population and land use change, the final factor map of MOLA was produced through 5 maps of MCE. Using 1990 and 1994 land use data, the 2000 predicted urban area of CA-Markov with factor map showed 0.06% improvement of absolute error comparing with that of CA-Markov without factor map. By the CA-Markov technique considering factor map, the 2030 and 2060 urban area increased $58.94km^2(0.78%)\;and\;60.14km^2(0.81%)$ respectively comparing with 2000 urban area$(313.19km^2)$. The 2030 and 2060 paddy area decreased $93.28km^2(2.54%)\;and\;93.65km^2(2.55%)$ respectively comparing with 2000 paddy area$(1383.23km^2)$.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.772-779
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• 2006

The new model proposed in this paper is the hidden Markov mesh model or the 2D HMM with the causality of top-down and left-right direction. With the addition of the causality constraint, two algorithms for the evaluation of a model and the maximum likelihood estimation of model parameters have been developed theoretically which are based on the forward-backward algorithm. It is a more natural extension of the 1D HMM than other 2D models. The proposed method will provide a useful way of modeling highly variable image patterns such as offline cursive characters.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.2
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• pp.16-22
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• 1983

In this paper, we obtained PFME(probability for maximum entropy) and entropy when a conditional probability was given in a binary list order Markov Information Source. And, when steady state probability was constant, the influence of change of a conditional probability on entropy was examined, too.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.1-12
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• 1994

We consider the markov process ${X_n}$ on R which is genereated by $X_{n+1} = f(X_n) + \epsilon_{n+1}$. Sufficient conditions for irreducibility and geometric ergodicity are obtained for such Markov processes. In additions, when ${X_n}$ is geometrically ergodic, the functional central limit theorem is proved for every bounded functions on R.

• Journal of the Korean Mathematical Society
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• v.38 no.1
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• pp.25-35
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• 2001

In this paper we consider an age dependent branching process whose particles move according to a Markov process with continuous state space. The Markov process is assumed to the stationary with independent increments and positive recurrent. We find some sufficient conditions for he Markov motion process such that the empirical distribution of the positions converges to the limiting distribution of the motion process.