• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.40-46
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• 1990

This paper introduces a geometric modelling system adopted in a newly developed preprocessor for finite element analysis of three dimensional structures. The formulation is characterized by hierarchical construction of structural model which consists of control points, curves, surfaces and solids. Various surface and solid modeling schemes based on blending functions and boundary representation are systematized for finite element mesh generation. The modeling system is integrated with model synthesis and operations which facilitate modelling of complex structures.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.200-201
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• 2005

• International conference on construction engineering and project management
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• 2007.03a
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• pp.140-149
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• 2007

Indian construction companies have only 0.05% market share in the 3-4 trillion dollar global construction business and only two Indian construction companies figure in the ENR "Top 225 Global Contractors 2006" list. Hence, while enormous scope for growth exists, international construction experience is limited. This study explores the risks as perceived by Indian companies venturing abroad since risks in international construction differ from home market risks. Literature survey identified a number of risk factors that were evaluated by the experts, highlighting fourteen important risk factors. Interpretive Structural Modelling (ISM) was used to develop a hierarchical model showing the relationships between the different risk factors, thus helping to focus on the key risks for effective risk management. The study shows that poor project management is a key risk forming the hub of the system, while political instability has maximum influence. The results of the study can be used by managers to visualise the risks in perspective and prioritise the mitigation effort.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.193-200
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• 2000

Modelling the dependence via random effects in censored multivariate survival data has recently received considerable attention in the biomedical literature. The random effects models model not only the conditional survival times but also the conditional hazard rate. Systematic likelihood inference for the models with random effects is possible using Lee and Nelder's (1996) hierarchical-likelihood (h-likelihood). The purpose of this presentation is to introduce Ha et al.'s (2000a,b) inferential methods for the random effects models via the h-likelihood, which provide a conceptually simple, numerically efficient and reliable inferential procedures.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1083-1090
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• 2016

In clinical studies, different types of outcomes (e.g. repeated measures data and time-to-event data) for the same subject tend to be observed, and these data can be correlated. For example, a response variable of interest can be measured repeatedly over time on the same subject and at the same time, an event time representing a terminating event is also obtained. Joint modelling using a shared random effect is useful for analyzing these data. Inferences based on marginal likelihood may involve the evaluation of analytically intractable integrations over the random-effect distributions. In this paper we propose a joint HGLM approach for analyzing such outcomes using the HGLM (hierarchical generalized linear model) method based on h-likelihood (i.e. hierarchical likelihood), which avoids these integration itself. The proposed method has been demonstrated using various numerical studies.

• Advances in aircraft and spacecraft science
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• v.1 no.3
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• pp.253-271
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• 2014

A linearised buckling analysis of thin-walled beams is addressed in this paper. Beam theories formulated according to a unified approach are presented. The displacement unknown variables on the cross-section of the beam are approximated via Mac Laurin's polynomials. The governing differential equations and the boundary conditions are derived in terms of a fundamental nucleo that does not depend upon the expansion order. Classical beam theories such as Euler-Bernoulli's and Timoshenko's can be retrieved as particular cases. Slender and deep beams are investigated. Flexural, torsional and mixed buckling modes are considered. Results are assessed toward three-dimensional finite element solutions. The numerical investigations show that classical and lower-order theories are accurate for flexural buckling modes of slender beams only. When deep beams or torsional buckling modes are considered, higher-order theories are required.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.605-612
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• 2011

In multi-center randomized clinical trial the treatment eect may be changed over centers. It is thus important to investigate the heterogeneity in treatment eect between centers. For this, uncorrelated random-eect models assuming independence between random-eect terms have been often used, which may be a strong assumption. In this paper we propose a correlated frailty modelling approach of investigating such heterogeneity using the hierarchical-likelihood method when the outcome is time-to-event. In particular, we show how to construct a proper prediction interval for frailty, which explores graphically the potential heterogeneity for a treatment-by-center interaction term. The proposed method is illustrated via numerical studies based on data from the design of a multi-center clinical trial.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.423-433
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• 1998

In agricultural field experiments, the completely randomized block design is often used for the analysis of variety trials. An important assumption is that every experimental unit in each block has the some fertility. But, in most agricultural field experiments there often exists a systematic heterogeneity in fertility among the experimental units. To account for the heterogeneity, we propose to use the hierarchical generalized linear models. We compare our analysis of the data from Scottish Agricultural colleges list with that using Markov chain Monte Carlo method.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.935-938
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• 1991

We are developing an automatic adjusting system for ITC. ITC(Integrated Tube Components) has a large set-by-set variability in its characteristics. And it also has nonlinearities. It requires not only a fast vision process but also an efficient control algorithm to meet the need for high productivity. In this paper, the description of an adjusting system and the modelling of ITC will be presented. And also the concept of a new rule based hierarchical algorithmic approaches will be suggested.