• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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• pp.344-354
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• 2008

The ITA meta-model descriptions being promoted by Korean government was developed to build information technology architecture for applications in public institutions. However, the application of this ITA meta-model is not easy because of the complexity and overlapping between classes and attributes which reside in the ITA meta-models. Additionally, the National Defense Architecture is planned for development using the MND-AF. Since the National Defense Architecture must align with the Government ITA for interoperability and consistency, it is crucial the differences in the meta-models between MND-AF and Government ITA must be resolved. This study presents the trade-off results between the meta-models of MND-AF and Government ITA. It also proposes a set of tailored meta-models for use with the National Defense architecture development. The tailored meta-models use an ERA (Element, Relationship, Attribute) data structure that decreases complexity and eliminates the overlapping between classes and attributes.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.59-74
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• 2007

Computer simulation models are in general quite complex and time-consuming to run, and therefore, a simpler meta-model is usually constructed for further analysis. In this paper, JANUS, a war-game simulator, is used to describe a certain tank combat situation. Then, second-order response surface and artificial neural network meta-models are developed using the data from eight different experimental designs. Relative performances of the developed meta-models are compared in terms of the mean squared error of prediction. Computational results indicate that, for the given problem, the second-order response surface meta-model generally performs better than the neural network, and the orthogonal array-based Latin hypercube design(LHD) or LHD using maximin distance criterion may be recommended.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.339-344
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• 2001

In recent engineering, the designer has become more and more dependent on the computer simulations such as FEM (Finite Element Method) and BEM (Boundary Element Method). In order to optimize such implicit models more efficiently and reliably, the meta-modeling technique has been developed for solving such a complex problems combined with the DACE (Design and Analysis of Computer Experiments). It is widely used for exploring the engineer's design space and for building meta-models in order to facilitate an effective solution of multi-objective and multi-disciplinary optimization problems. Optimization of a train suspension is performed according to the minimization of forty-six responses that represent ten ride comforts, twelve derailment quotients, twelve unloading ratios, and twelve stabilities by using the Kriging meta-model of a train suspension. After each Kriging meta-model is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called SQP (Sequential Quadratic Programming).

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.323-335
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• 2016

Breast cancer risk assessment has developed during years and evaluation of genetic factor affecting risk of breast cancer is an important component of this risk assessment. The aim of this meta-analysis was to investigate the role of XRCC1 polymorphisms (Arg194Trp, Arg280His and Arg399Gln) in risk of breast cancer among different population and categories of menopausal status.PubMed, Medline, Web of Science, and PubMed Central were systematically searched to identify studies evaluating association between breast cancer and XRCC1 gene polymorphisms (Arg194Trp, Arg280His and Arg399Gln). Two authors independently extracted required information. Odds Ratios were pooled for four genetic inheritance models using both fixed and the DerSimonian and Laird random-effect models. Egger's test and contour-enhanced funnel plot was used to evaluate publication bias and small study effect. Additional subgroup analysis was performed for menopausal status, ethnicity, and source of controls. After evaluation and applying inclusion criteria on extracted studies, fifty three studies were included in this meta-analysis. For polymorphisms of Arg194Trp and Arg280His, no significant association was observed in all genetic models. Arg194Trp had a protective effect in post-menopausal status only in homozygote model (OR=0.57 [0.37-0.88]). Arg399Gln showed significant association with breast cancer in homozygote (OR=1.21 [1.10-1.34]), dominant (OR=1.09 [1.03-1.15]) and recessive (OR=1.21 [1.09- 1.35]) genetic models. Arg399Gln was associated with higher risk in post-menopausal status for homozygote and heterozygote models. Our findings suggest that XRCC1 gene polymorphisms modify breast cancer risk in different populations and different categories of menopausal status.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.313-318
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• 2000

This article addresses aspects of combining information, with special attention to meta-analysis. In specific, we consider hierarchical Bayesian models for meta-analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. Numerical methods of finding Bayes estimators under these heavy tailed prior are given, and are illustrated with an actual example.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.161-175
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• 2001

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. Hierarchical models including selection models are introduced and shown to be useful in such Bayesian meta-analysis. Semiparametric hierarchical models are proposed using the Dirichlet process prior. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierachical selection model with including unknown weight function and use Markov chain Monte Carlo methods to develop inference for the parameters of interest. Using Bayesian method, this model is used on a meta-analysis of twelve studies comparing the effectiveness of two different types of flouride, in preventing cavities. Clinical informative prior is assumed. Summaries and plots of model parameters are analyzed to address questions of interest.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.19-34
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• 2000

Impact of business process re-engineering requires the fundamental rethinking of how information systems are analyzed and designed. It is no longer sufficient to establish a monolithic system for fixed business environments. Information systems must be adaptive in nature. This demand is also applied in production domain. Enabling concept for the adaptive information system is reusability. This paper presents a new object-oriented analysis process for creating such reusable software components in production domain, especially for production planning and scheduling. Our process called MeCOMA is based on three meta-models: physical object meta-model, data object meta-model, and activity object meta-model. After the three meta-models are extended independently for a given production system, they are collaboratively integrated on the basis of integration pattern. The main advantages of MeCOMA are (1) to reduce software development time and (2) to consistently build reusable production software components.

• Journal of the Korean Society of Systems Engineering
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• v.7 no.1
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• pp.31-41
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• 2011

The purpose of this paper is to provide an abstracted meta-model for executing Capabilities Portfolio Management (CPM) effectively based on DoDAF2.0. The purpose of developing an architecture is for beneficial use of it. A good set of architectural artifacts facilitates the manipulation and use of them in meeting its usage objectives well. Systems engineering methodologies evolve to accommodate or to deal with enterprise or SoS/FoS level problems. And DoD's Capabilities Portfolio Management (CPM) is a good example which demonstrates enterprise or SoS level problems. However, the complexity of the architecture framework makes it difficult to develop and use the architecture models and their associated artifacts. DoDAF states that it was established to guide the development of architectures and to satisfy the demands of a structured, repeatable method for evaluating alternatives which add value to decisions and management practices. One of the objectives of DoDAF2.0 is to define concepts and models usable in CPM which is one of DoD's six core processes. However, DoDAF and various guidelines state requirements for CPM rather than how to. This paper provides methodology for CPM which includes process and tailored meta-models based on DoDAF Meta Model (DM2).

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.35-50
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• 2000

Diverse requirements of users on web-based model management force a system agent to develop user-adaptive building a model in reality and providing an adequate solution method of the model. The relationship between models is important knowledge for the agent to effectively build a new model to adaptively adjust an existing model under a problem and to efficiently connect the new model into an adequate solution method. Since the generating process of the inter-model relationship is more difficult than the building a new model however the process mostly depends on the knowledge of operation research experts. Without the adequate scheme of the inter-model relationship the burden of the management for the agent increases rapidly and the quality of the services may worsen. This study shows that meta-knowledge generated from relationship between models is important for the user to build a model in reality and to acquire the solver appropriate to the model. The relationship that consists of common and exclusive objects between models can be represented by frames. The system under development to implement the idea includes user-adaptive ability which identifies a model through forward chaining method and searches the solver appropriate to the model by using the meta knowledge. We illustrate the meta knowledge with an applied delivery system in supply chain management.