• Bulletin of the Korean Chemical Society
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• v.14 no.1
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• pp.95-100
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• 1993

A noise-induced transition is presented for a bistable system subjected to a multiplicative random force, which is singular at the unstable state. The stationary probability distribution is obtained from the Fokker-Planck equation and the effects of the singularity is analyzed. On the basis of noise-induced phase transition with Gaussian white noise, the relaxation time and the transition rate of the system are evaluated up to the first order correction of D. In the parameter region v < l, the transition rates decrease as the exponent v goes to 1 and as the coefficient of the linear term of the kinetic equation increases.

• Journal of Wind Energy
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• v.14 no.1
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• pp.37-43
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• 2023

In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.

• 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 Information Processing Systems
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• v.10 no.4
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• pp.602-617
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• 2014

The performance of a cognitive radio network (CRN) solely depends on how precisely the secondary users can sense the presence or absence of primary users. The incorporation of a spatial false alarm makes deriving the probability of a correct decision a cumbersome task. Previous literature performed this task for the case of a received signal under a Normal probability density function case. In this paper we enhance the previous work, including the impact of carrier frequency, the gain of antennas on both sides, and antenna heights so as to observe the robustness against noise and interference and to make the correct decision of detection. Three small scale fading channels: Rayleigh, Normal, and Weibull were considered to get the real scenario of a CRN in an urban area. The incorporation of a maximal-ratio combining and selection combing with a variation of the number of received antennas have also been studied in order to achieve the correct decision of spectral sensing, so as to serve the cognitive users. Finally, we applied the above concept to a traffic model of the CRN, which we based on a two-dimensional state transition chain.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.17-24
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• 1994

This paper is a study on Korean word recognition and suggest the method that stabilizes the state-transition in the HMM by applying the `inertia' to the feature vector sequences. In order to reduce the quantized distortion considering probability distribution of input vectors, we used SOFM, an unsupervised learning method, as a vector quantizer, By applying inertia to the feature vector sequences, the overlapping of probability distributions for the response path of each word on the self organizing feature map can be reduced and the state-transition in the Hmm can be Stabilized. In order to evaluate the performance of the method, we carried out experiments for 50 DDD area names. The results showed that applying inertia to the feature vector sequence improved the recognition rate by 7.4% and can make more HMMs available without reducing the recognition rate for the SOFM having the fixed number of neuron.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.1-9
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• 2007

A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow's precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1819-1826
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• 2009

To enhance the automatic performance of existing predicting and planning algorithms that require a predefined probability of the states' transition, this paper proposes a multiple sequence generation system. When interacting with unknown environments, a virtual agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. We describe a sequential behavior generation method motivated from the change in the agent's state in order to help the virtual agent learn how to adapt to unknown environments. In a sequence learning process, the sensed states are grouped by a set of proposed motivation filters in order to reduce the learning computation of the large state space. In order to accomplish a goal with a high payoff, the learning agent makes a decision based on the observation of states' transitions. The proposed multiple sequence behaviors generation system increases the complexity and heightens the automatic planning of the virtual agent for interacting with the dynamic unknown environment. This model was tested in a virtual library to elucidate the process of the system.

• Journal of the military operations research society of Korea
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• v.17 no.2
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• pp.44-53
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• 1991

This paper is aimed to develop a Lanchester type combat model for the direct-fire engagement. This model incorporates number of combatants, inter-firing time, detection time by movement, detection probability by the signature of fire, where the inter-firing time and the detection time are assumed to follow a negative exponential distribution. The approach to modeling is as follows : in the process of an engagement, a combatant takes one of the states('observing' state or 'firing' state), a combatant is initially in the observing state, if the combatant detects a target, he changes his state from 'observing' to 'firing' and will cause attrition to the opposing forces. Thus this transition mechanism is embodied into the differential equation form with each transition rate. A limited examination of the validity has been conducted by comparison with the Monte-Carlo simulation model 'BAGSIM', and with a traditional Deterministic Lanchester model.

• Computers and Concrete
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• v.34 no.2
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• pp.231-245
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• 2024

A 3D discrete element model integrating the rough surface contact concept with the flat-joint model is suggested to examine the mechanical characteristics of the interfacial transition zone (ITZ) in concrete. The essential components of our DEM procedure include the calculation of the actual contact area in an element contact-pair related to the bonded factor using a Gaussian probability distribution of asperity height, as well as the determination of the contact probability-relative displacement form using the least square method for further computing the force-displacement of ITZs. The present formulations are implemented in MUSEN, an open source development environment for discrete element analysis that is optimized for high performance computation. The model's meso-parameters are calibrated by using uniaxial compression and splitting tensile simulations, as well as laboratory tests of concrete from the literature. The present model's DEM predictions accord well with laboratory experimental tests of pull-out concrete specimens published in the literature.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.109-118
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• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.