• Journal of Information Processing Systems
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• v.16 no.2
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• pp.318-328
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• 2020

Letter of Credit (L/C) is currently a very popular international settlement method frequently used in international trade processes amongst countries around the globe. Compared with other international settlement methods, however, L/C has some obvious shortcomings. Firstly, it is not easy to use due to the sophisticated processes its usage involves. Secondly, it is sometimes accompanied by a few risks and some uncertainty. Thus, highly efficient methods need to be used to assess and control these risks. To begin with, FAHP and KMV methods are used to resolve the problem of incomplete information associated with L/C and then, on this basis, Bayesian game theory is used in order to make more scientific and reasonable decisions with respect to international trade.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.411-423
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• 2003

In this paper we develop a method for calculating a probability that a particular generalized variance is the smallest of all the K multivariate normal generalized variances. The method gives a way of comparing K multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approach for the probability is intractable and thus a Bayesian method is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach. Necessary theory involved in the method and computation is provided.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.229-236
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• 2004

This article presents a multiple comparison ranking procedure for several products of the Poisson rates. A preference probability matrix that warrants the optimal comparison ranking is introduced. Using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. Necessary theory and two illustrative examples are provided.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.73-78
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• 2002

In this paper we develop a method for constructing a Bayesian HPD (highest probability density) interval of a ratio of two multivariate normal generalized variances. The method gives a way of comparing two multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approaches for the interval is intractable and thus a Bayesian HPD(highest probability densith) interval is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach introduced by Chen and Shao(1999). Necessary theory involved in the method and computation is provided.

• Earthquakes and Structures
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• v.8 no.4
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• pp.935-956
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• 2015

Structural Health Monitoring (SHM) of steel towers has become a hot research topic. From the literature, it is impractical and impossible to develop a "general" method that can detect all kinds of damages for all types of structures. A practical method should make use of the characteristics of the type of structures and the kind of damages. This paper reports a feasibility study on the use of measured modal parameters for the detection of damaged braces of tower structures following the Bayesian probabilistic approach. A substructure-based structural model-updating scheme, which groups different parts of the target structure systematically and is specially designed for tower structures, is developed to identify the stiffness distributions of the target structure under the undamaged and possibly damaged conditions. By comparing the identified stiffness distributions, the damage locations and the corresponding damage extents can be detected. By following the Bayesian theory, the probability model of the uncertain parameters is derived. The most probable model of the steel tower can be obtained by maximizing the probability density function (PDF) of the model parameters. Experimental case studies were employed to verify the proposed method. The contributions of this paper are not only on the proposal of the substructure-based Bayesian model updating method but also on the verification of the proposed methodology through measured data from a scale model of transmission tower under laboratory conditions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.589-594
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• 2007

This paper is shown new method for durability assesment and design have been noticed to be very valuable has been successfully applied to predict concrete structures. This paper provides that a new approach for predicting the corrosion durability of reinforced concrete structures exposed to chloride attack. In this method, the prediction can be updated successive1y by the Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures under chloride attack environments.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.647-654
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• 2003

Bayesian probability models for finite populations are considered assuming so-called the super-population. We find the posterior distribution of population mean by a new approach, using the concept of pivotal quantity for the small sample case. A large sample theory is also treated throught the concept of asymptotically pivotal quantity.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.2
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• pp.151-161
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• 1998

In this study, an attempt is made to apply the concept of fuzzy-bayesian theory to the integrity assessment of RC highway bridge, and uncertainty states are represented in terms of fuzzy sets which define several linguistic variables such as "very good", "good", "average", "poor", "very poor", etc. Especially, the concept of fuzzy conditional probability aids to derive a new reliability analysis which includes the subjective assessment of engineers without introducing any additional correction factors. The fuzzy concept are also used as reliability indexes for the condition assessment based on the proposed models, the proposed fuzzy theory-based approach with the results of visual inspection and extensive field load tests are applied to the integrity assessment of a new RC highway bridge, namely, Jichok bridge.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.467-482
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• 2021

Purpose: The main theme of this study is to determine the optimal control limit of conditional variance investigation by mathematical approach. According to the determination approach of control limit presented in this study, it is possible with only one parameter to calculate the control limit necessary for budgeting control system or standard costing system, in which the limit could not be set in advance, that's why it has the advantage of high practical application. Methods: This study followed the analytical methodology in terms of the decision model of information economics, Bayesian probability theory and Taguchi's quality loss function concept. Results: The function suggested by this study is as follows; ${\delta}{\leq}\frac{3}{2}(k+1)+\frac{2}{\frac{3}{2}(k+1)+\sqrt{\{\frac{3}{2}(k+1)\}^2}+4$ Conclusion: The results of this study will be able to contribute not only in practice of variance investigation requiring in the standard costing and budgeting system, but also in all fields dealing with variance investigation differences, for example, intangible services quality control that are difficult to specify tolerances (control limit) unlike tangible product, and internal information system audits where materiality standards cannot be specified unlike external accounting audits.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.2
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• pp.81-91
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• 1991

This thesis aims to improve the analysis accuracy of remotely sensed digital imagery, and the improvement is achieved by considering the weight factors(a priori probabilities) of Bayesian MLC in the classification stage. To be concrete, Bayesian decision theory is studied from remote sensing field of view, and the equations in the n-dimensional form are derived from normal probability density functions. The amount of the misclassified pixels is extracted from probability function data using the thres-holding, and this is a basis of evaluating the classification accuracy. The results indicate that 5.21% of accuracy improvement was carried out. The data used in this study is LANDSAT TM(1985.10.21 ; 116-34), and the study area is within the administrative boundary of Seoul.