• Journal of the Korean Statistical Society
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• v.35 no.3
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• pp.251-260
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• 2006

The proportional hazards model (PHM) can be associated with a non- homogeneous Markov chain (NHMC) in the sense that sample alignments in the PHM correspond to trajectories of the NHMC. As a result the partial likelihood widely used for the PHM is a probabilistic function of the trajectories of the NHMC. In this paper, we show that, as the total number of subjects involved increases, the trajectories of the NHMC, i.e. sample alignments in the PHM, converges to the solution of an ordinary differential equation which, subsequently, characterizes the almost sure limit of the partial likelihood.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1183-1189
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• 2008

The general formulas of the operating characteristic(OC) function, average sample number(ASN) and average outgoing quality(AOQ) for the modified n-level skip-lot sampling plan(MMSkSP2) were derived using Markov chain properties by Cho(2008). In this paper, the OC curve, ASN and AOQ of a reference plan, modified two-level, three-level and five-level skip-lot sampling plans are compared.

• Bulletin of the Korean Mathematical Society
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• v.56 no.6
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• pp.1497-1510
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• 2019

In this paper we compute the stopping times in the game Rock-Paper-Scissors. By exploiting the recurrence relation we compute the mean values of stopping times. On the other hand, by constructing a transition matrix for a Markov chain associated with the game, we get also the distribution of the stopping times and thereby we compute the mean stopping times again. Then we show that the mean stopping times increase exponentially fast as the number of the participants increases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2499-2522
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• 2017

Multi-hop networks are a low-setup-cost solution for enlarging an area of network coverage through multi-hop routing. Carrier sense multiple access with collision avoidance (CSMA/CA) is frequently used in multi-hop networks. Multi-hop networks face multiple problems, such as a rise in contention for the medium, and packet loss under heavy-load, saturated conditions, which consumes more bandwidth due to re-transmissions. The number of re-transmissions carried out in a multi-hop network plays a major role in the achievable quality of service (QoS). This paper presents a statistical, analytical model for the end-to-end delay of contention-based medium access control (MAC) strategies. These strategies schedule a packet before performing the back-off contention for both differentiated heterogeneous data and homogeneous data under saturation conditions. The analytical model is an application of Markov chain theory and queuing theory. The M/M/1 model is used to derive access queue waiting times, and an absorbing Markov chain is used to determine the expected number of re-transmissions in a multi-hop scenario. This is then used to calculate the expected end-to-end delay. The prediction by the proposed model is compared to the simulation results, and shows close correlation for the different test cases with different arrival rates.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.183-187
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• 2005

In this paper, we propose a heuristic method to select features using a Two-Phase Markov Blanket-based (TPMB) algorithm. The first phase, filtering phase, of TPMB algorithm works by filtering the obviously redundant features. A non-linear correlation method based on Information theory is used as a metric to measure the redundancy of a feature [1]. In second phase, approximating phase, the Markov Blanket (MB) of a system is estimated by employing the concept of cross entropy to identify the MB. We perform experiments on microarray data and report two popular dataset, AML-ALL [3] and colon tumor [4], in this paper. The experimental results show that the TPMB algorithm can significantly reduce the number of features while maintaining the accuracy of the classifiers.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.435-444
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• 1995

We propose a new approach to the calculation of the exact cells loss probability in a shared buffer ATM multiplexer, which is loaded with homogeneous discrete-time ON-OFF sources. Renewal cycles are identified in regard to the state of input sources and the buffer state on each renewal circle is modelled as a K(shared buffer size)-state Markov chain. We also analyze the behavior of queue build-up at the shared buffer whose distribution together with the steady-state probabilities of the Markov chain leads to the exact cell loss probability. Our approach to obtaining the exact cell loss probability seems to be more efficient than most of other existing ones since our underlying Markov chain has less number of states.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3C
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• pp.224-232
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• 2012

Priority MAC is an important issue in every communication system when we consider differentiated service applications. In this paper, we propose a mechanism to support priority MAC based on multi-parameters for IEEE 802.15.7 visible light communication (VLC). By using three parameters such as number of backoff times (NB), backoff exponent (BE) and contention window (CW), we provide priority for multi-level differentiated service applications. We consider beacon-enabled VLC personal area network (VPAN) mode with slotted version for random access algorithm in this paper. Based on a discrete-time Markov chain, we analyze the performance of proposed mechanism under non-saturation environments. By building a Markov chain model for multi-parameters, this paper presents the throughput and transmission delay time for VLC system. Numerical results show that we can apply three parameters to control the priority for VLC MAC protocol.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1864-1872
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• 2003

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.185-195
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• 2002

Previous research has shown that the flow of patients around departments of geriatric medicine and ex-patients in the community may be-modelled by the application of a mixed-exponential distribution. In this proper we considered a ave-compartment model using a continuous-time Markov process to describe the flow of patients. Using a M/ph/c queuing model, we present a way of optimizing the number of beds in order to maintain an acceptable delay probability a sufficiently low level. Finally, we constructed a Java computer simulation, using data from St George's Hospital, London.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.335-339
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• 1998

This paper presents a closed loop identification algorithm in time domain. This algorithm can be used for identification of unstable system and for model validation of system which is difficult to derive analytical model. In time domain, projection filter, which projects a finite number of input output data of a system into its current space, is used to relate the state space model with a finite difference model. Then recursive relations between the Markov parameters and the ARX model coefficients are derived to identify the system, controller and Kalman filter Markov parameters recursively, which are finally used to identify the system, controller and Kalman filter gains. The NASA LAMSTF is used to validate the algorithms developed.