• Journal of the Korean Society of Systems Engineering
• /
• v.14 no.1
• /
• pp.44-51
• /
• 2018

The purpose of RAM analysis in weapon systems is to reduce life cycle costs, along with improving combat readiness by meeting RAM target value. We analyzed the sensitivity of the RAM analysis parameters to the use of the operating system by using the Markov Process based model (MPS, Markov Process Simulation) developed for RAM analysis. A Markov process-based RAM analysis model was developed to analyze the sensitivity of parameters (MTBF, MTTR and ALDT) to the utility of the 81mm mortar. The time required for the application to reach the steady state is about 15,000H, which is about 2 years, and the sensitivity of the parameter is highest for ALDT. In order to improve combat readiness, there is a need for continuous improvement in ALDT.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.1-8
• /
• 2022

Supporting real-time flows with delay and throughput constraints is an important challenge for future wireless networks. In this paper, we develop an optimal scheduling scheme to optimally choose the packets to transmit. The optimal transmission strategy is based on an observable Markov decision process. The novelty of the work focuses on a priority-based probabilistic packet scheduling strategy for efficient packet transmission. This helps in providing guaranteed services to real time traffic in Heterogeneous Wireless Networks. The proposed scheduling mechanism is able to optimize the desired performance. The proposed scheduler improves the overall end-to-end delay, decreases the packet loss ratio, and reduces blocking probability even in the case of congested network.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.14 no.3
• /
• pp.523-534
• /
• 1994

The purpose of this study is to develop computer simulation model that produce precipitation patterns from stochastic model. In the paper(I) of this study, the alternate renewal process(ARP) is used for the daily precipitation series. In this paper(Il), stochastic simulation models for the daily precipitation series are developed by combining Markov chain for the precipitation occurrence process and continuous probability distribution for the precipitation amounts on the wet days. The precipitation occurrence is determined by first order Markov chain with two states(dry and wet). The amounts of precipitation, given that precipitation has occurred, are described by a Gamma, Pearson Type-III, Extremal Type-III, and 3 parameter Weibull distribution. Since the daily precipitation series shows seasonal variation, models are identified for each month of the year separately. To illustrate the application of the simulation models, daily precipitation data were taken from records at the seven locations of the Nakdong and Seomjin river basin. Simulated data were similar to actual data in terms of distribution for wet and dry spells, seasonal variability, and precipitation amounts.

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

• Journal of Korea Water Resources Association
• /
• v.35 no.5
• /
• pp.583-595
• /
• 2002

The purposes of this study are to perform the management and monitoring of droughts for Mokpo area via the monthly Palmer index(PDSI), the data is obtained from the Mokpo meteorological station, and the used data are in the period of 1906 to 1999. Monthly Palmer index is classified into 7 stochastic classes and its dynamic change of monthly transition probability estimated by Markov chain is investigated. We also estimate the steady state probability of the classified PDSI. The 4th class shows the highest frequency of 49.6% out of 7 classes and the 7th class which is the most extreme drought show that a stochastic transition probability is more or less larger than an empirical one. Also, we found that the monthly steady state probability could be used for the forecasting of changing pattern of drought magnitude for the study area.

• Communications of the Korean Mathematical Society
• /
• v.11 no.1
• /
• pp.191-199
• /
• 1996

The strong shift equivalence of nonnegative integral square matrices is a necessary and sufficient condition for the topological conjugacy of topological Markov chains. In this paper we study the relation between strong shift equivalence and matrix conjugation.

• Structural Engineering and Mechanics
• /
• v.63 no.1
• /
• pp.47-53
• /
• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• The Korean Journal of Applied Statistics
• /
• v.34 no.1
• /
• pp.9-23
• /
• 2021

In the following paper we introduce a variational Bayes method that approximates posterior distributions with mean-field method. In particular, we introduce automatic differentiation variation inference (ADVI), which approximates joint posterior distributions using the product of Gaussian distributions after transforming parameters into real coordinate space, and then apply it to pharmacokinetic models that are models for the study of the time course of drug absorption, distribution, metabolism and excretion. We analyze real data sets using ADVI and compare the results with those based on Markov chain Monte Carlo. We implement the algorithms using Stan.

• Bulletin of the Korean Mathematical Society
• /
• v.54 no.1
• /
• pp.17-28
• /
• 2017

In this paper, we deal with a class of mean-field backward stochastic differential equations (BSDEs) related to finite state, continuous time Markov chains. We obtain the existence and uniqueness theorem and a comparison theorem for solutions of one-dimensional mean-field BSDEs under Lipschitz condition.

• Communications for Statistical Applications and Methods
• /
• v.8 no.1
• /
• pp.109-115
• /
• 2001

Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.