• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.6
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• pp.641-649
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• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• Proceedings of the Korean Association for Survey Research Conference
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• 2006.06a
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• pp.131-159
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• 2006

This paper consider multinomial group testing which is concerned with classification each of N given units into one of k disjoint categories. In this paper, we propose exact Bayesian, approximate Bayesian, bootstrap methods for estimating individual category proportions using the multinomial group testing model proposed by Bar-Lev et al (2005). By the comparison of Mcan Squre Error (MSE), it is shown that the exact Bayesian method has a bettor efficiency and consistency than maximum likelihood method. We suggest an approximate Bayesian approach using Markov Chain Monte Carlo (MCMC) for posterior computation. We derive exact credible intervals based on the exact Bayesian estimators and present confidence intervals using the bootstrap and MCMC. These intervals arc shown to often have better coverage properties and similar mean lengths to maximum likelihood method already available. Furthermore the proposed models are illustrated using data from a HIV blooding test study throughout California, 2000.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.4
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• pp.312-320
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• 2003

Recently there is a growing interest in mobile cellular network providing multimedia service. However, the link bandwidth of mobile cellular network is not sufficient enough to provide satisfactory services to users. To overcome this problem, an adaptive framework has been proposed. In this study, we propose a new method of estimating DPR(Degradation Period Ratio) in an adaptive multimedia network where the bandwidth of ongoing call can be dynamically adjusted during its lifetime. DPR is a QoS(Quality of Service) parameter which represents the ratio of allocated bandwidth below a pre-defined target to the whole service time of a call. We improve estimation method of DPR using DTMC(Discrete Time Markov Chain) model by calculate mean degradation period, degradation probability more precisely than in existing studies. Under Threshold CAC(Call Admission Control) algorithm, we present analytically how to guarantee QoS to users and illustrate the method by numerical examples. The proposed method is expected to be used as one of CAC schemes in guaranteeing predefined QoS level of DPR.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.11-20
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• 2011

Pictorial Structures is known as an effective method that recognizes and tracks human poses. In this paper, the upper body pose is also tracked by PS and a particle filter(PF). PF is one of dynamic programming methods. But Markov chain-based dynamic motion model which is used in dynamic programming methods such as PF, couldn't predict effectively the highly articulated upper body motions. Therefore PF often fails to track upper body pose. In this paper we propose the key pose-based proposal distribution for proper particle prediction based on the similarities between key poses and an upper body silhouette. In the experimental results we confirmed our 70.51% improved performance comparing with a conventional method.

• Journal of the Society of Naval Architects of Korea
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• v.36 no.4
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• pp.137-146
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• 1999

Inspection becomes to be important in the safety of structure and economical viewpoint, because structural damage accompanies lots of economical cost and social problems. Especially ship structure is composed of a lot of members and it is impossible to inspect all members continuously. The purpose of this paper is to get optimal inspection plan containing inspection time and method. Crack is one of major modes on the structural failure and can lead to collapse of structure. In this paper, the deteriorating process, which contains inspection to detect the crack before the propagation to large crack, is idealized as Markov chain model. Genetic algorithm is also used to accomplish the optimization of inspection plan. Especially, the probabilistic characteristics of cracks are changed, because ship is operating in corrosive environments and the scantling of structural members is reduced due to corrosion. Non-stationary Markov chain model is used to represent the process of corrosion in structural members. In this paper, the characteristics of indivisual inspection plan are compared by numerical examples for the change of corrosion rate, the cost due to scheduled system down and target failure probability. From the numerical example, it can be seen that the improvement of fatigue life for the members with short fatigue life is the most effective way in order to reduce total maintenance cost.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.97-109
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• 2014

This paper considers a reverse supply chain with simultaneous recovery of used products and manufacturing of brand-new ones. Recovered products are downgraded and have to be sold in a market different from that of brand-new products at a different price. In case of a shortage of recovered product inventory, a brand-new item, if available, can be offered at the price of a recovered product. In other words, one-way demand substitution is allowed. We address the joint decision of when to manufacture brand-new product, when to recover returned product, and how to control demand substitution to maximize the hybrid production system's profits. To this end, we propose a Markov decision Process model and investigate the structure of the optimal policy. Performance comparison is numerically implemented between the models with and without downward demand substitution option under different operating conditions of the system parameters.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1299-1306
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• 2011

In this study, crack-growth model parameters subjected to variable amplitude loading are estimated in the form of a probability distribution using the method of Bayesian parameter estimation. Huang's model is employed to describe the retardation and acceleration of the crack growth during the loadings. The Markov Chain Monte Carlo (MCMC) method is used to obtain samples of the parameters following the probability distribution. As the conventional MCMC method often fails to converge to the equilibrium distribution because of the increased complexity of the model under variable amplitude loading, an improved MCMC method is introduced to overcome this shortcoming, in which a marginal (PDF) is employed as a proposal density function. The model parameters are estimated on the basis of the data from several test specimens subjected to constant amplitude loading. The prediction is then made under variable amplitude loading for the same specimen, and validated by the ground-truth data using the estimated parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3328-3349
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• 2020

With rapid development of ubiquitous computing and location-based services (LBSs), human trajectory data and associated activities are increasingly easily recorded. Inappropriately publishing trajectory data may leak users' privacy. Therefore, we study publishing trajectory data while preserving privacy, denoted privacy-preserving activity trajectories publishing (PPATP). We propose S-PPATP to solve this problem. S-PPATP comprises three steps: modeling, algorithm design and algorithm adjustment. During modeling, two user models describe users' behaviors: one based on a Markov chain and the other based on the hidden Markov model. We assume a potential adversary who intends to infer users' privacy, defined as a set of sensitive information. An adversary model is then proposed to define the adversary's background knowledge and inference method. Additionally, privacy requirements and a data quality metric are defined for assessment. During algorithm design, we propose two publishing algorithms corresponding to the user models and prove that both algorithms satisfy the privacy requirement. Then, we perform a comparative analysis on utility, efficiency and speedup techniques. Finally, we evaluate our algorithms through experiments on several datasets. The experiment results verify that our proposed algorithms preserve users' privay. We also test utility and discuss the privacy-utility tradeoff that real-world data publishers may face.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.4
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• pp.694-711
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• 2016

This study analyzes the spatial characteristics of knowledge industry which has shown relatively rapid growth in the low-growth economy situation in recent years. In particular, we catch hold of the locational characteristics of the knowledge service industry which occupies the highest ratio by professional-expert jobs favoured by young generations, as well as estimate their occupational employment opportunities. By applying Location Quotient(LQ) and LISA, we reveal the spatial distribution patterns of publishing business, information service business and education service business in the Seoul Metropolitan area, and examine the changes in the spatial patterns during the last ten years. In order to understand the socio-economic factors which explain their locations, we apply the stepwise multiple regression analysis. Furthermore, we predict the changes distribution of Knowledge service industrial employment by applying Markov Chain Model. As the result, we found their clusters at the specific locations, while there is the significant variations in the socio-economic variables related their locations respectively. The related job opportunities of the knowledge service businesses in the Seoul Metropolitan area are predicted steady growth trend for the next four years, even though dull or stagnant trend is expected for other industries. This study provides basic resources to the planning for young generation employment problem.

• Journal of the military operations research society of Korea
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• v.34 no.2
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• pp.163-174
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• 2008

The military is an organization where reliability and availability take much more importance than in any other organization. And, in line with a recent trend of putting emphasis on 'system readiness', not only functions but also availability of a weapon system has become one of achievement targets. In this regard, the military keeps spares for important facility and equipment, which is called as Maintenance Float (M/F), in order to enhance reliability and availability in case of an unforeseen event. The military has calculated yearly M/F requirements based on the number of equipment and utilization rate. However, this method of calculation has failed to meet the intended targets of reliability and availability due to lack of consideration on the characteristics of equipment malfunctions and maintenance unit's capability. In this research, we present an analysis model that can be used to determine an optimal M/F inventory level based on queuing and absorbed Markov chain theories. And, we applied the new analysis model to come out with an optimal volume of K-1 tank M/F for the OO division, which serves as counterattack military unit. In our view, this research is valuable because, while using more tractable methodology compared to previous research, we present a new analysis model that can describe decision making process on M/F level more satisfactorily.