• Journal of the Korean Data and Information Science Society
• /
• v.10 no.1
• /
• pp.119-133
• /
• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.3
• /
• pp.852-861
• /
• 2000

Software reliability growth models are used in testing stages of software development to model the error content and time intervals betwewn software failures. In this paper, using priors for the number of fault with the negative binomial distribution nd the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carolo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.2
• /
• pp.553-560
• /
• 2007

In this paper, we consider two components system which lifetimes have Freund's bivariate exponential model with equal failure rates. We propose Bayesian multiple comparisons procedure for the failure rates of I Freund's bivariate exponential populations based on Dirichlet process priors(DPP). The family of DPP is applied in the form of baseline prior and likelihood combination to provide the comparisons. Computation of the posterior probabilities of all possible hypotheses are carried out through Markov Chain Monte Carlo(MCMC) method, namely, Gibbs sampling, due to the intractability of analytic evaluation. The whole process of multiple comparisons problem for the failure rates of bivariate exponential populations is illustrated through a numerical example.

• Korean Journal of Remote Sensing
• /
• v.24 no.1
• /
• pp.45-55
• /
• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.

• Journal of the military operations research society of Korea
• /
• v.29 no.2
• /
• pp.100-110
• /
• 2003

Equipment replacement policy may not be defined with certainty, because physical states of any technological system may not be determined with foresight. This paper presents Markov Decision Process(MDP) model for army equipment which is subject to the uncertainty of deterioration and ultimately to failure. The components of the MDP model is defined as follows: ⅰ) state is identified as the age of the equipment, ⅱ) actions are classified as 'keep' and 'replace', ⅲ) cost is defined as the expected cost per unit time associated with 'keep' and 'replace' actions, ⅳ) transition probability is derived from Weibull distribution. Using the MDP model, we can determine the optimal replacement policy for an army equipment replacement problem.

• Journal of the military operations research society of Korea
• /
• v.34 no.2
• /
• pp.163-174
• /
• 2008

The military is an organization where reliability and availability take much more importance than in any other organization. And, in line with a recent trend of putting emphasis on 'system readiness', not only functions but also availability of a weapon system has become one of achievement targets. In this regard, the military keeps spares for important facility and equipment, which is called as Maintenance Float (M/F), in order to enhance reliability and availability in case of an unforeseen event. The military has calculated yearly M/F requirements based on the number of equipment and utilization rate. However, this method of calculation has failed to meet the intended targets of reliability and availability due to lack of consideration on the characteristics of equipment malfunctions and maintenance unit's capability. In this research, we present an analysis model that can be used to determine an optimal M/F inventory level based on queuing and absorbed Markov chain theories. And, we applied the new analysis model to come out with an optimal volume of K-1 tank M/F for the OO division, which serves as counterattack military unit. In our view, this research is valuable because, while using more tractable methodology compared to previous research, we present a new analysis model that can describe decision making process on M/F level more satisfactorily.

• Journal of KIISE
• /
• v.44 no.10
• /
• pp.1026-1033
• /
• 2017

In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.2
• /
• pp.5-11
• /
• 1996

The purpose of this paper is to propose a method improving the performance of a semi-continuous hidden Markov model(SCHMM) speaker adaptation system which uses Bayesian Parameter reestimation approach. The performance of Bayesian speaker adaptation could be degraded in case that the features of a new speaker are severely different from those of a reference codebook. The excessive codewords of the reference codebook still remain after adaptation proess. which cause confusion in recognition process. To solve such problems, the proposed method uses formant information which is extracted from the cepstral coefficients of the reference codebook and adaptation data. The reference codebook is adapted to represent the formant distribution of a new speaker and it is used for Bayesian speaker adaptation as an initial codebook. The proposed method provides accurate correspondence between reference codebook and adaptation data. It was observed that the excessive codewords were not selected during recognition process. The experimental results showed that the proposed method improved the recognition performance.

• Journal of the Institute of Convergence Signal Processing
• /
• v.1 no.1
• /
• pp.1-6
• /
• 2000

In this paper, the hybrid system of HMM and neural network is proposed and show better recognition rate of the post-process procedure which minimizes the process error of recognition than that of HMM(Hidden Markov Model) only used. After the HMM training by training data, testing data that are not taken part in the training are sent to HMM. The output probability from HMM output by testing data is used for the training data of the neural network, post processor. After neural network training, the hybrid system is completed. This hybrid system makes the recognition rate improvement of about $4.5\%$ in MLP and about $2\%$ in RBFN and gives the solution to training time of conventional hybrid system and to decrease of the recognition rate due to the lack of training data in real-time speech recognition system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.2900-2922
• /
• 2021

In this paper, the power spectral density(PSD) for Multilevel Minimum Shift Keyed signal with modulation index h = 1/2 (M-ary MSK) are derived using the mathematical method of the Markov Chain model. At first, according to an essential requirement of the phase continuity characteristics of MSK signals, a complete model of the whole process of signal generation is built. Then, the derivations for autocorrelation functions are carried out precisely. After that, we verified the correctness and accuracy of the theoretical derivation by comparing the derived results with numerical simulations using MATLAB. We also divided the spectrum into four components according to the derivation. By analyzing these figures in the graphic, each component determines the characteristics of the spectrum. It is vital for enhanced spectral characteristics. To more visually represent the energy concentration of the main flap and the roll-down speed of the side flap, the specific out-of-band power of M-ary MSK is given. OMLCD(Optimum Maximum Likelihood Coherent Detection) of M-ary MSK is adopted to compare the signal received with prepared in advance in a code element T to go for the best. And M-ary MSK BER(Bit Error Rate) is compared with the same ary PSK (Phase Shift Keying) with M=2,4,6,8. The results show the detection method could improve performance by increasing the length of L(memory inherent) in the phase continuity.