• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1133-1140
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• 2013

A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.10
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• pp.895-900
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• 2016

Remaining useful life (RUL) estimation of the Li-ion battery has gained great interest because it is necessary for quality assurance, operation planning, and determination of the exchange period. This paper presents the RUL estimation of an Li-ion battery for an energy storage system using exponential function for the degradation model and Markov Chain Monte Carlo (MCMC) approach for parameter estimation. The MCMC approach is dependent upon information such as model initial parameters and input setting parameters which highly affect the estimation result. To overcome this difficulty, this paper offers a guideline for model initial parameters based on the regression result, and MCMC input parameters derived by comparisons with a thorough search of theoretical results.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.99-110
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• 2000

In this study we proposed a simple method for generating multi-site daily rainfall based on the 1-order Markov chain and considering the spatial correlation. The occurrence of rainfall is simulated by a simple 1st-order Markov chain and its intensity to be chosen randomly from the observed data. The spatial correlation between sites could be conserved as the rainfall intensity at each site is to be chosen consistently with the target site in time through generation. It is found that the generated daily rainfall data reproduce genera] characteristics of the observed data such as average, standard deviation, average number of wet and dry days, but the clustering level in time is somewhat loosened. Thus, the lag-I correlation coefficient of the generated data gave smaller value than the observed, also the average lengths of wet run and dry run and the wet-to-wet and dry-to-dry probabilities were a bit less than the observed. This drawback seems to be overcome somewhat by choosing a proper site representing overall basin characteristics or by use of more detailed states of rainfall occurrence.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.1-13
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• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.877-888
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• 2017

Spectral analysis is used to determine the frequency of time series data. We first determine the frequency of the series through the power spectrum or the periodogram and then calculate the period of a cycle that may exist in a time series. Estimating the frequency using a Bayesian technique has been developed and proven to be useful; however, the Bayesian estimator for the frequency cannot be analytically solved through mathematical equations and may be handled numerically or computationally. In this paper, we make an inference on the Bayesian frequency through both resampling a parameter by Markov chain Monte Carlo (MCMC) methods and resampling data by bootstrap methods for a time series. We take the Korean real estate price index as an example for Bayesian frequency estimation. We have found a difference in the periods between the sale price index and the long term rental price index, but the difference is not statistically significant.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.619-629
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• 2002

Water in the atmosphere is circulated by reciprocal action of various factors in the climate system. Otherwise, any climate phenomenon could not occur of itself. Thus, we have tried to understand the climate change by analysis of the factors. In this study, the fuzzy theory which is useful to express inaccurate and approximate nature in the real world is used for forecasting precipitation influenced by the factors. Forecasting models used in this study are neuro-fuzzy system and a Markov chain and those are applied to precipitation forecasting of illinois. Various atmosphere circulation factors(like soil moisture and temperature) influencing the climate change are considered to forecast precipitation. As a forecasting result, it can be found that the considerations of the factors are helpful to increase the forecastibility of the models and the neuro-fuzzy system gives us relatively more accurate forecasts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1522-1545
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• 2012

Call admission control (CAC) plays an important role in mobile cellular network to guarantee the quality of service (QoS). In this paper, a dynamic hybrid CAC scheme with integrated cutoff priority and handoff queue for mobile cellular network is proposed and some performance metrics are derived. The unique characteristic of the proposed CAC scheme is that it can support any number of service types and that the cutoff thresholds for handoff calls are dynamically adjusted according to the number of service types and service priority index. Moreover, timeouts of handoff calls in queues are also considered in our scheme. By modeling the proposed CAC scheme with a one-dimensional Markov chain (1DMC), some performance metrics are derived, which include new call blocking probability ($P_{nb}$), forced termination probability (PF), average queue length, average waiting time in queue, offered traffic utilization, wireless channel utilization and system performance which is defined as the ratio of channel utilization to Grade of Service (GoS) cost function. In order to validate the correctness of the derived analytical performance metrics, simulation is performed. It is shown that simulation results match closely with the derived analytic results in terms of $P_{nb}$ and PF. And then, to show the advantage of 1DMC modeling for the performance analysis of our proposed CAC scheme, the computing complexity of multi-dimensional Markov chain (MDMC) modeling in performance analysis is analyzed in detail. It is indicated that state-space cardinality, which reflects the computing complexity of MDMC, increases exponentially with the number of service types and total channels in a cell. However, the state-space cardinality of our 1DMC model for performance analysis is unrelated to the number of service types and is determined by total number of channels and queue capacity of the highest priority service in a cell. At last, the performance comparison between our CAC scheme and Mahmoud ASH's scheme is carried out. The results show that our CAC scheme performs well to some extend.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.1-17
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• 2015

In this paper, we propose a portfolio selection model utilizing a Markov chain for investing in the foreign exchange market based on market forecasts and exchange rate movement predictions. The proposed model is utilized to compute optimum investment portfolio weights for investing in margin-based markets such as the FX margin market. We further present an objective investment algorithm for applying the proposed model in real-life investments. Empirical performance of the proposed model and investment algorithm is evaluated by conducting an experiment in the FX market consisting of the 7 most traded currency pairs, for a period of 9 years, from the beginning of 2005 to the end of 2013. We compare performance with 1) the Dollar Index, 2) a 1/N Portfolio that invests the equal amount in the N target assets, and 3) the Barclay BTOP FX Index. Performance is compared in terms of cumulated returns and Sharpe ratios. The results suggest that the proposed model outperforms all benchmarks during the period of our experiment, for both performance measures. Even when compared in terms of pre- and post-financial crisis, the proposed model outperformed all other benchmarks, showing that the model based on objective data and mathematical optimization achieves superior performance empirically.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1493-1504
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• 2016

As cyber threats have been increased over the Internet, the invasions of personal information are constantly occurring. A malicious user can access the Web site as a normal user using leaked personal information and does illegal activities. This paper proposes an effective method which authenticates a genuine user with real-time. The method use the user's profile which is a record of user's behavior created by Membership Analysis(MA) and Markov Chain Model(MCM). In addition to, user's profile is augmented by a Time Weight(TW) which reflects the user's tendency. This method can detect a malicious user who camouflage normal user. Even if it is a genuine user, it can be determined as an abnomal user if the user acts beyond the record profile. The result of experiment showed a high accuracy, 96%, for the correct user.

• Journal of Communications and Networks
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• v.15 no.1
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• pp.77-86
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• 2013

Cognitive radio (CR) has emerged as one of effective methods to enhance the utilization of existing radio spectrum. Main principle of CR is that secondary users (SUs) are allowed to use the spectrum unused by primary users (PUs) without interfering PU's transmissions. In this paper, PUs operate on a slot-by-slot basis and SUs try to exploit the slots unused by PUs. We propose OSA protocols in the single channel and we propose an opportunistic spectrum access (OSA) protocols in the multi-channel cognitive radio networks with one control channel and several licensed channels where a slot is divided into contention phase and transmission phase. A slot is divided into reporting phase, contention phase and transmission phase. The reporting phase plays a role of finding idle channels unused by PUs and the contention phase plays a role of selecting a SU who will send packets in the data transmission phase. One SU is selected by carrier sense multiple access / collision avoidance (CSMA/CA) with request to send / clear to send (RTS/CTS) mechanism on control channel and the SU is allowed to occupy all remaining part of all idle channels during the current slot. For mathematical analysis, first we deal with the single-channel case and we model the proposed OSA media access control (MAC) protocol by three-dimensional discrete time Markov chain (DTMC) whose one-step transition probability matrix has a special structure so as to apply the censored Markov chain method to obtain the steady state distribution.We obtain the throughput and the distribution of access delay. Next we deal with the multi-channel case and obtain the throughput and the distribution of access delay by using results of single-channel case. In numerical results, our mathematical analysis is verified by simulations and we give numerical results on throughput and access delay of the proposed MAC protocol. Finally, we find the maximum allowable number of SUs satisfying the requirements on throughput and access delay.