• 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.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.507-518
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• 2017

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.81-96
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• 2006

We consider a rental company that dynamically manages Its capacity level through capacity addition and return While serving customer with its own capacity, the company expands its capacity by renting items from an outside source so that it can avoid lost opportunities of rental which occur when stock is not sufficient. If stock becomes sufficiently large enough to cope with demands, the company returns expanded capacity to the outside source. Formulating the model into a Markov decision problem, we identify an optimal capacity management Policy which states when the company should expand its capacity and when it should return expanded capacity after capacity addition. Since it is intractable to analytically find the optimal capacity management policy and the optimal size of capacity expansion, we present a numerical procedure that finds these optimal values based on the value iteration method. Numerical analysis is implemented and we observe monotonic properties of the optimal performance measures by system parameters, which are meaningful in developing effective heuristic policies.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.826-836
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• 2016

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2036-2039
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• 2003

This paper focuses on the study of the robustness of face authentication methods under illumination changes. Four different face authentication methods are tried. These methods are as follows; Principal Component Analysis, Gaussian Mixture Models, 1-Dimensional Hidden Markov Models, 2-Dimensional Hidden Markov Models. Experiment results involving an artificial illumination change to face images are compared with each others. Face feature vector extraction method based on the 2-Dimensional Discrete Cosine Transform is used. Experiments to evaluate the above four different face authentication methods are carried out on the Olivetti Research Laboratory(ORL) face database. For the pseudo 2D HMM, the best EER (Equal Error Rate) performance is observed.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.11
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• pp.440-446
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• 1984

This thesis investigates the quantitative aspect of epidemic phenomena utilizing the analytical method of discrete time systems based on the theory of Markov processes. In particular, the pattern on the epidemic character of Influenza was analyzed by the mathematical model of Influenza system, which is derived according to the ecologic relationship between five epidemiolgic states of individuals. The quantitative aspects of the model was characterized by digital computer simulations. The main results were obtained as follows: 1) A Markovian model of influenza system represents accurate spead curve. 2) The latent period of influenza has the standard deviation of 1.98 and also the incubation period is 2.68. 3) If the value of susceptibilities in the pre-epidemic period is less than 20% of the population, the epidemic will occur sporadically. 4) The initial value of susceptibilties obtained by this markov theory is less about 10% of total population than the obtained value according to the deterministic model.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.4
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• pp.295-304
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• 2014

We analyze theMMPP/G/1/K queue with a modified state-dependent service rate. The service time of customers upon service initiation is changed if the number of customers in the system reaches a threshold. Then, the changed service time is continued until the system becomes empty completely, and this process is repeated. We analyze this system using an embedded Markov chain and a supplementary variable method, and present the queue length distributions at a customer's departure epochs and then at an arbitrary time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.244-250
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• 2014

The Most Serious Engine Faults Are Those That Occur Within The Engine. Traditional Engine Fault Diagnosis Is Highly Dependent On The Engineer'S Technical Skills And Has A High Failure Rate. Neural Networks And Support Vector Machine Were Proposed For Use In A Diagnosis Model. In This Paper, Noisy Sound From Faulty Engines Was Represented By The Mel Frequency Cepstrum Coefficients, Zero Crossing Rate, Mean Square And Fundamental Frequency Features, Are Used In The Hidden Markov Model For Diagnosis. Our Experimental Results Indicate That The Proposed Method Performs The Diagnosis With A High Accuracy Rate Of About 98% For All Eight Fault Types.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.2
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• pp.381-389
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• 1994

In the communication network, multimedia service such as high quality voice, high speed data, image etc. will be added to the existing service. This service generates new requirements for the communication networks. The priority control mechanism can be used to control multimedia traffics generated by many communication systems. The priority mechanism which assigns prioirities to generated cells according to service quality is one of the traffic control. The priority assignment can be divided by priority criterion for each traffic characteristics such as loss sensitivity and delay sensitivity. In this paper, we alnalyzed the partial buffur sharing (PBS) mechani느 as a traffic control reducing the cell loss, and proposed analysis method. We analyzed the PBS mechanism using classical approach as a Markov chain. In order to validata proposed analysis method, simulation is performed using simulation package SIMSCRIPT 11.5. From this results, we confirmed that proposed analysis method can be verified. Also, we presented cell loss probability of ATM network when this results are to be applied to ATM networks.

• Journal of Applied Reliability
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• v.15 no.4
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• pp.233-240
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• 2015

We analyse reliability of multi-state UH-60 helicopter hydraulic pump system with a multi-functional standby component using Markov analysis method. The system consists of seven components: 2 main pumps, 1 standby pump, 2 primary servos, and 2 tail rotor servos. The standby pump can take over when one more than components fail. Therefore the standby pump is multi-functional standby component. The system has four states: good, deteriorated, dangerous, and failed. The components have 2 states: working and failed. We assume the system is unrepairable when the components fail. We estimate failure distributions and rates using collected failure time data in field. And we classify multi-state of the system according to emergency procedure of UH-60A student handout. We obtain the reliabilities of multi-state system using Visual Basic program because the differential equations is extremely complicated and tedious to solve.