• Communications for Statistical Applications and Methods
• /
• v.5 no.3
• /
• pp.733-742
• /
• 1998

For industrial and medical lifetime data, the generalized log-gamma regression model is considered. Then the Bayesian analysis for the generalized log-gamma regression with censored data are explained and following the data augmentation (Tanner and Wang; 1987), the censored data is replaced by simulated data. To overcome the complicated Bayesian computation, Makov Chain Monte Carlo (MCMC) method is employed. Then some modified algorithms are proposed to implement MCMC. Finally, one example is presented.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.15 no.4
• /
• pp.442-449
• /
• 2012

Cognitive Radio(CR) is the technology that allocates the frequency by using dynamic spectrum access. We proposed a model to calculate the optimal level of the cognitive radiotelegraph, where secondary users opportunistically share the spectrum with primary users through the spectrum sensing. When secondary user with cognitive radio detects the arrival of a primary user in its current channel, the secondary user moves to the idle channel or be placed in the virtual queue. We assume that the primary users have finite buffers and the population of secondary users is finite. Using a two-dimensional Makov model with preemptive priority queueing, we could derive the blocking and waiting probability as well as the optimal level of cognitive radiotelegraph under a various range of parameter circumstances.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.20 no.4
• /
• pp.1241-1250
• /
• 1996

The characteristics of fatigue cumulative damage and fatigue life of 8-harness satin woven CFRP composites with a circular hole under constant amplitude and 2-level block loading are estimated by Stochastic Makov chain model. It is found in this study that the fatigue damage accumulation behavior is very random and the fatigue damage is accumulated as two regions under constant amplitude fatigue loading. In constant amplitude fatigue loading the predicted mean number of cycles to a specified damage state by Markov chain model shows a good agreement with the test result. The predicted distribution of the fatigue cumulative damage by Markov chain model is similar to the test result. The fatigue life predictions under 2-level block loading by Markov chain model revised are good fitted to the test result more than by 2-parameter Weibull distribution function using percent failure rule.

• Proceedings of the Acoustical Society of Korea Conference
• /
• spring
• /
• pp.70-73
• /
• 1999

본 논문에서는 한국어에 대한 실시간 음소 인식을 통한 Lip Synch 구현에 필수요소인 Viseme(Visual Phoneme)을 한국어의 음운학적 접근 방법을 통해 제시하고, Lip Synch에서 입술의 모양에 결정적인 영향을 미치는 모음에 대한 모음 인식 실험 및 결과 분석을 한다.모음인식 실험에서는 한국어 음소 51개 각각에 대해 3개의 State로 이루어진 CHMM (Continilous Hidden Makov Model)으로 모델링하고, 각각의 음소가 병렬로 연결되어진 음소네트워크를 사용한다. 입력된 음성은 12차 MFCC로 특징을 추출하고, Viterbi 알고리즘을 인식 알고리즘으로 사용했으며, 인식과정에서 Bigrim 문법과 유사한 구조의 음소배열 규칙을 사용해서 인식률과 인식 속도를 향상시켰다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.17 no.11
• /
• pp.1199-1205
• /
• 1992

In this paper, a Korean word recognition method which usese Neural Network and Hidden Markov Models(HMM) is proposed to improve a recognition rate with a small amount of learning data. The method reduces the fluctuation due to personal differences which is a problem to a HMM recognition system. In this method, effective recognizer is designed by the complement of each recognition result of the Hidden Markov Models(HMM) and Neural Network. In order to evaluate this model, word recognition experiment is carried out for 28 cities which is DDD area names uttered by two male and a female in twenties. As a result of testing HMM with 8 state, codeword is 64, the recognition rate 91[%], as a result of testing Neural network(NN) with 64 codeword the recognition rate is 89[%]. Finally, as a result of testing NN-HMM with 64 codeword which the best condition in former tests, the recognition rate is 95[%].

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.2
• /
• pp.225-233
• /
• 2011

Stock market prices, economic indices, trends and changes of social phenomena, etc. are categorized as time series data. Research on time series data has been prevalent for a while as it could not only lead to valuable representation of data but also provide future trends as well as changes in direction. We take a conventional model based approach, known as Markov chain modeling for the prediction on stock market prices. To improve prediction accuracy, we apply Markov modeling over carefully selected intervals of training data to fit the trend under consideration to the model. Another method we take is to apply clustering to data and build models of the resultant clusters. We confirmed that clustered models are better off in predicting, however, with the loss of prediction rate.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.6
• /
• pp.558-565
• /
• 2021

The Republic of Korea Navy has deployed naval fleets in the East, West, and South seas to effectively respond to threats from North Korea and its neighbors. However, it is difficult to allocate proper missions due to high uncertainties, such as the year of introduction for the ship, the number of mission days completed, arms capabilities, crew shift times, and the failure rate of the ship. For this reason, there is an increasing proportion of expenses, or mission alerts with high fatigue in the number of workers and traps. In this paper, we present a simulation model that can optimize the assignment of naval vessels' missions by using a continuous time absorbing Markov chain that is easy to model and that can analyze complex phenomena with varying event rates over time. A numerical analysis model allows us to determine the optimal mission durations and warship quantities to maintain the target operating rates, and we find that allocating optimal warships for each mission reduces unnecessary alerts and reduces crew fatigue and failures. This model is significant in that it can be expanded to various fields, not only for assignment of duties but also for calculation of appropriate requirements and for inventory analysis.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.18 no.1
• /
• pp.110-122
• /
• 1993

When voice is transmitted through packet switching network, there needs a overload control, that is, a control for the congestion which lasts short periods and occurrs in local extents. In this thesis, we analyzed the performance of the statistical packet voice multiplexer using the overload control strategy by bit dropping. We assume that the voice is coded accordng to (4,2) embedded ADPCM and that the voice packet is generated and transmitted according to the procedures in the CCITT recomendation G. 764. For the performance analysis, we must model the superposed packet arrival process to the multiplexer as exactly as possible. It is well known that interarrival times of the packets are highly correlated and for this reason MMPP is more suited for the modelling in the viewpoint of accuracy. Hence the packet arrival process in modeled as MMPP and the matrix geometric method is used for the performance analysis. Performance analysis is similar to the MMPP IG II queueing system. But the overload control makes the service time distribution G dependent on system status or queue length in the multiplexer. Through the performance analysis we derived the probability generating function for the queue length and using this we derived the mean and standard deviation of the queue length and waiting time. The numerical results are verified through the simulation and the results show that the values embedded in the departure times and that in the arbitrary times are almost the same. Results also show bit dropping reduces the mean and the variation of the queue length and those of the waiting time.