• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.3
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• pp.229-239
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• 2015

In light of the significant increase in the quantities of goods transported and the development of the shipping industry, the frequency of usage of port structures has increased; yet, the government's budget for the shipping & port of SOC has been reduced. Port structures require systematically effective maintenance and management trends that address their growing frequency of usage. In order to construct a productive maintenance system, it is essential to develop deterioration models of port structures that consider various characteristics, such as location, type, use, constructed level, and state of maintenance. Processes for developing such deterioration models include examining factors that cause the structures to deteriorate, collecting data on deteriorating structures, and deciding methods of estimation. The techniques used for developing the deterioration models are multiple regression analysis and Markov chain theory. Multiple regression analysis can reflect changes over time and Markov chain theory can apply status changes based on a probabilistic method. Along with these processes, the deterioration models of open-type and gravity-type wharfs were suggested.

• 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 the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.111-127
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• 2004

We consider a supply chain model with a make-to-order production facility and a single supplier. The model we treat here is a special case of a two-echelon inventory model. Unlike classical two-echelon systems, the demand process at the supplier is affected by production process at the production facility as well as customer order arrival process. In this paper, we address that how the demand variability impacts on the optimal replenishment policy. To this end, we incorporate Erlang and phase-type demand distributions into the model. Formulating the model as a Markov decision problem, we investigate the structure of the optimal replenishment policy. We also implement a sensitivity analysis on the optimal policy and establish its monotonicity with respect to system cost parameters.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.214-216
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• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.515-521
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• 2011

In this paper we consider failure detection equipment that which find failures in repairable systems and enable repair operations. In practical situations, failure detection equipment may come across troubles that can cause the omissions in detecting system failures and have a serious effect on system reliability. We analyze this effect through the appropriate modeling of Markov processes.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.363-375
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• 2004

In this paper, We show that the bandpass random signals of the form ∑$_{\alpha}$$\alpha$$_{\alpha}$ a Sin(2$\pi$f$_{\alpha}$t + b$_{\alpha}$) where a$_{\alpha}$ being a random number in [0,1], f$_{\alpha}$ a random integer in a given frequency band, and b$_{\alpha}$ a random number in [0, 2$\pi$], generate Gaussian white noise signals and hence they are adequate for simulating Continuous Markov processes. We apply the result to the fluctuation-dissipation formula for the Johnson noise and show that the probability distribution for the long term average of the power of the Johnson noise is a X$^2$ distribution and that the relative error of the long term average is (equation omitted) where N is the number of blocks used in the average.error of the long term average is (equation omitted) where N is the number of blocks used in the average.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.93-99
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• 2011

In this paper, we propose a policy-gradient routing scheme under Reinforcement Learning that can be used adaptive QoS routing. A policy-gradient RL routing can provide fast learning of network environments as using optimal policy adapted average estimate rewards gradient values. This technique shows that fast of learning network environments results in high success rate of routing. For prove it, we simulate and compare with three different schemes.

• 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.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.773-788
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• 2000

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.616-629
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• 2015

A mobile terminal will expect a number of handoffs within its call duration. In the event of a mobile call, when a mobile node moves from one cell to another, it should connect to another access point within its range. In case there is a lack of support of its own network, it must changeover to another base station. In the event of moving on to another network, quality of service parameters need to be considered. In our study we have used the Markov decision process approach for a seamless handoff as it gives the optimum results for selecting a network when compared to other multiple attribute decision making processes. We have used the network cost function for selecting the network for handoff and the connection reward function, which is based on the values of the quality of service parameters. We have also examined the constant bit rate and transmission control protocol packet delivery ratio. We used the policy iteration algorithm for determining the optimal policy. Our enhanced handoff algorithm outperforms other previous multiple attribute decision making methods.