• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.22-25
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• 1999

A dynamic explicit finite element code for simulating sheet forming processes has been developed The code utilises the discrete Kirchhoff shell element and contact force is treated by a conventional penalty method. In order to reduce the computational cost a new and robust contact searching algorithm has been developed and implemented into the code. in the method a hierarchical structure of tool segments called a tree structure is built for each tool at the initial stage of the analysis Tree is built in a way to divide a trunk to 8 sub-trunk 2 in each direction until the lowest level of the tree(leaf) contains exactly one segment of the tool. In order to have a well-balanced tree each box on each sub level contains one eighth of the segments. Then at each time step contact line from a node comes out of the surface of the tool. Simulation of various sheet forming processes were performed to verify the validity of the developed code with main focus on he usefulness of the developed contact searching algorithm.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.705-720
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• 2012

In this paper, we introduce a hierarchical Bayesian model to simultaneously estimate the thresholds of each 6 cities. It was noted in the literature there was a dramatic increases in the number of deaths if the mean temperature passes a certain value (that we call a threshold). We estimate the difference of mortality before and after the threshold. For the hierarchical Bayesian analysis, some proper prior distribution of parameters and hyper-parameters are assumed. By combining the Gibbs and Metropolis-Hastings algorithm, we constructed a Markov chain Monte Carlo algorithm and the posterior inference was based on the posterior sample. The analysis shows that the estimates of the threshold are located at $25^{\circ}C{\sim}29^{\circ}C$ and the mortality around the threshold changes from -1% to 2~13%.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• International Journal of Quality Innovation
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• v.7 no.2
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• pp.83-96
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• 2006

In this paper, fuzzy hierarchical analysis (FHA) is used to explore the process by which the criteria weights of the Taiwan National Quality Award (TNQA) are assigned by TNQA committee members. Each member is allowed to employ fuzzy scales in place of exact scales. Each pairwise comparison of criteria is made through a questionnaire from each TNQA committee member. The membership function of trapezoidal fuzzy numbers is introduced to specify TNQA committee members' intentions. After FHA, the reasonable range of each criterion weight of TNQA is determined. The current criteria weights of TNQA are properly verified.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.435-451
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• 2001

We describe a hierarchical bayesian model to analyze multinomial nonignorable nonresponse data. Using a Dirichlet and beta prior to model the cell probabilities, We develop a complete hierarchical bayesian analysis for multinomial proportions without making any algebraic approximation. Inference is sampling based and Markove chain Monte Carlo methods are used to perform the computations. We apply our method to the dta on body mass index(BMI) and show the model works reasonably well.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.87-93
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• 2009

In this paper, we propose and evaluate the availability of single sensor node using a hierarchial modeling approach. We divides a sensor node into a software and hardware and analyze failures of each component. We construct Markov chains to represent the components of a sensor node, and then we construct a hierarchical model which use fault tree in upper level and Markov chains in lower level. We evaluate the availability and down of single sensor node.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.95-99
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• 2011

The research analyzes structure models of Repeated Measurement Design (RMD) and Hierarchical Design (HD). The experimental unit of RMD model is living organisms, such as human. In contrast, HD is used when all the factors are random. The HD models are derived from R:B:A, R:C:B:A and R:C:($A{\times}B$).

• Proceedings of the Korean Institute of Interior Design Conference
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• 2001.05a
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• pp.121-127
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• 2001

An unified measure of user interface efficiency and aesthetics for cyber museum is proposed. First, general structure of cyber museum is discussed and hierarchical analyses are done for sample sites. Usability tests based on the hierarchical analyses yield statistics of user access frequency and persistency for each page, on which access probability is deduced. Second, visual occupancy, a measure of efficiency of user interface element based on access probability is defined. The hierarchical statistics of visual occupancy can be an index for characterization and classification of cyber museums. Examples are provided.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.199-205
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• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.