• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1B
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• pp.50-58
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• 2012

The objective of this study is to develop the theoretical model of the telecommunication system service availability from the user perspective. We assume non-homogeneous Poisson process for the call arrival process and continuous time Markov chain for the system state. The proposed model effectively describes the user model of the user-perceived service reliability by including the time-varying call arrival rate. We also include the operational failure state where the user cannot receive any service even though the system is functioning.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.1-8
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• 2011

This paper proposes a variable sampling interval multivariate $T^2$ control chart with sampling at fixed times, where samples are taken at specified equally spaced fixed time points and additional samples are allowed between these fixed times when indicated by the preceding $T^2$ statistics. At fixed sampling points, the $T^2$ statistics are composed of all quality characteristics and a part of quality characteristics are selected to obtain $T^2$ statistics at additional sampling points. A Markov chain approach is used to evaluate the performance of the proposed chart. Numerical studies for the performance of the proposed chart show that the proposed chart reduces the observations obtained from a process and detects the assignable cause of a process with low correlated quality characteristics quickly.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.141-148
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• 2003

Customer retention is a common concern for many industries and a critical issue for the survival in today's greatly compressed marketplace. Current customer retention models only focus on detection of potential defectors based on the likelihood of defection by using demographic and customer profile information. In this paper, we propose a dynamic procedure for defection detection and prevention using past and current customer behavior by utilizing SOM and Markov chain. The basic idea originates from the observation that a customer has a tendency to change his behavior (i.e. trim-out his usage volumes) before his eventual withdrawal. This gradual pulling out process offers the company the opportunity to detect the defection signals. With this approach, we have two significant benefits compared with existing defection detection studies. First, our procedure can predict when the potential defectors could withdraw and this feature helps to give marketing managers ample lead-time for preparing defection prevention plans. The second benefit is that our approach can provide a procedure for not only defection detection but also defection prevention, which could suggest the desirable behavior state for the next period so as to lower the likelihood of defection. We applied our dynamic procedure for defection detection and prevention to the online gaming industry. Our suggested procedure could predict potential defectors without deterioration of prediction accuracy compared to that of the MLP neural network and DT.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.3
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• pp.179-196
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• 1986

In this paper, the peformance of a statistical packet voice/data multiplexer is studied. In ths study we assume that in the packet voice/data multiplexer two separate finite queues are used for voice and data traffics, and that voice traffic gets priority over data. For the performance analysis we divide the output link of the multiplexer into a sequence of time slots. The voice signal is modeled as an (M+1) - state Markov process, M being the packet generation period in slots. As for the data traffic, it is modeled by a simple Poisson process. In our discrete time domain analysis, the queueing behavior of voice traffic is little affected by the data traffic since voice signal has priority over data. Therefore, we first analyze the queueing behavior of voice traffic, and then using the result, we study the queueing behavior of data traffic. For the packet voice multiplexer, both inpur state and voice buffer occupancy are formulated by a two-dimensional Markov chain. For the integrated voice/data multiplexer we use a three-dimensional Markov chain that represents the input voice state and the buffer occupancies of voice and data. With these models, the numerical results for the performance have been obtained by the Gauss-Seidel iteration method. The analytical results have been verified by computer simylation. From the results we have found that there exist tradeoffs among the number of voice users, output link capacity, voic queue size and overflow probability for the voice traffic, and also exist tradeoffs among traffic load, data queue size and oveflow probability for the data traffic. Also, there exists a tradeoff between the performance of voice and data traffics for given inpur traffics and link capacity. In addition, it has been found that the average queueing delay of data traffic is longer than the maximum buffer size, when the gain of time assignment speech interpolation(TASI) is more than two and the number of voice users is small.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1043-1050
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• 2022

Markov chains and Monte Carlo Simulation were applied to account for the probabilistic nature of pavement deterioration over time using data collected in the field. The primary purpose of this study was to evaluate pavement network performance of Western Australia (WA) by applying the existing pavement management tools relevant to WA road construction networks. Two approaches were used to analyze the pavement networks: evaluating current pavement performance data to assess WA State Road networks and predicting the future states using past and current pavement data. The Markov chains process and Monte Carlo Simulation methods were used to predicting future conditions. The results indicated that Markov chains and Monte Carlo Simulation prediction models perform well compared to pavement performance data from the last four decades. The results also revealed the impact of design, traffic demand, and climate and construction standards on urban pavement performance. This study recommends an appropriate and effective pavement engineering management system for proper pavement design and analysis, preliminary planning, future pavement maintenance and rehabilitation, service life, and sustainable pavement construction functionality.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.353-363
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• 2020

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.

• Communications of the Korean Mathematical Society
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• v.14 no.4
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• pp.805-814
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• 1999

We consider an MMPP/G/1/K finite queue with two-level threshold overload control. This model has frequently arisen in the design of the integrated communication systems which support a wide range applications having various Quality of Service(QoS) requirements. Through the supplementary variable method, se derive the queue length distribution.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.455-464
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• 2005

In this paper, we analyze a queueing system with overload control by arrival rates. This paper is motivated by overload control to prevent congestion in telecommunication networks. The arrivals occur dependent upon queue length. In other words, if the queue length increases, the arrivals may be reduced. By considering the burstiness of traffics in telecommunication networks, we assume the arrival to be a Markov-modulated Poisson process. The analysis by the embedded Markov chain method gives to us the performance measures such as loss and delay. The effect of performance measures on system parameters also is given throughout the numerical examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1036-1057
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• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.549-555
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• 2000

We propose an approximate algorithm to analyze the queuing system with n bursty and heterogeneous arrival processes. Each input process is modeled by Interrupted Bernoulli Process(IBP). We approximate N arrival processes by a single state variable and subsequently simplify the transition probability matrix of the Markov chain associated with these N arrival processes. Using this single state variable of arrival processes, we describe the state of the queuing system and analyze the system numerically with the reduced transition probability matrix. We compute the queue length distribution, the delay distribution, and the loss probability. Comparisons with simulation data show that the approximation algorithm has a good accuracy.