• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.74-80
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• 2013

This study describes structural reliability analysis of actively-controlled structure for which random vibration analysis is incorporated into the first-order reliability method (FORM) framework. The existing approaches perform the reliability analysis based on the RMS response, whereas the proposed study uses the peak response for the reliability analysis. Therefore, the proposed approach provides us a meaningful performance measure of the active control system, i.e., realistic failure probability. In addition, it can deal with the uncertainties in the system parameters as well as the excitations in single-loop reliability analysis, whereas the conventional random vibration analysis requires double-loop reliability analysis; one is for the system parameters and the other is for stochastic excitations. The effectiveness of the proposed approach is demonstrated through a numerical example where the proposed approach shows fast and accurate reliability (or inversely failure probability) assessment results of the dynamical active control system against random seismic excitations in the presence of parametric uncertainties of the dynamical structural system.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.101-110
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• 1999

Products are often evaluated on rating scales to measure and quantify their attributes of interest. In case that one wishes to compare multiple rating datasets simultaneously, there must be a standardized scale with which one can discriminate relative differences among corresponding scale means. In this regard, the concept of Thurstonian modeling applied to various discrimination tests including the triangular method has been recently being reconsidered. In this paper we extend previous researches on the triangular method and evaluate the effect of unequal variances and correlated variables upon the probability of correct response using Monte-Carlo simulation. We observed that the probability of correct response depends on dimensionality, variances, and correlation structure of stimulus sets. But it does not depend on the relative orientation in a multidimensional space.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.12 no.1
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• pp.33-39
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• 2008

We consider a queueing system fed by a superposition of multiple discrete autoregressive processes of order 1, and propose an approximation method to estimate the overflow probability of the system. Numerical examples are provided to validate the proposed method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.5
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• pp.623-628
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• 2007

The accurate peak response estimation of a seismically excited structure with frictional damping system(FDS) is very difficult since the structure with FDS shows nonlinear behavior dependent on the structural period, loading characteristics, and relative magnitude between the frictional force and the excitation load. Previous studies have estimated that by replacing a nonlinear system with an equivalent linear one or by employing the response spectrum obtained based on nonlinear time history and statistical analysis. In the case that an earthquake load is defined with probabilistic characteristics, the corresponding response of the structure with FDS has probabilistic distribution. In this study, nonlinear time history analyses were performed for the structure with FDS subjected to artificial earthquake loads generated using Kanai-Tajimi filter. An equation for the probability density function (PDF) of the displacement response is proposed by adapting the PDF of the normal distribution. Finally, coefficients of the proposed PDF are obtained by regression analysis of the statistical distribution of the time history responses. Finally the correlation between PDFs and statistical response distribution is presented.

• Journal of the Earthquake Engineering Society of Korea
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• v.13 no.6
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• pp.39-46
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• 2009

The selection of an evaluation response spectrum can have a significant effect on the seismic fragility evaluation of a structure. A method for modifying the seismic fragility parameters that are calculated based on the design spectrum is described in this study. The modification factor is used to modify the original fragility parameters. The HCLPF (High Confidence of Low Probability of Failure) acceleration levels of the electric system using previous design spectrum and uniform hazard spectrum (UHS) were compared. Finally, seismic risk analyses were performed according to a uniform hazard spectrum. From the results, it was concluded that based on the design spectrum, seismic risk for the electric system might be underestimated.

• Steel and Composite Structures
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• v.19 no.1
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• pp.237-262
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• 2015

In this study, we try to compare different intensity measures for evaluating nonlinear response of bridge structure. This paper presents seismic analytic fragility of a three-span concrete girder highway bridge. A complete detail of bridge modeling parameters and also its verification has been presented. Fragility function considers the relationship of intensities of the ground motion and probability of exceeding certain state of damage. Incremental dynamic analysis (IDA) has been subjected to the bridge from medium to strong ground motions. A suite of 20 earthquake ground motions with different range of PGAs are used in nonlinear dynamic analysis of the bridge. Complete sensitive analyses have been done on the response of bridge and also efficiency and practically of them are studied to obtain a proficient intensity measure for these types of structure by considering its sensitivity to the period of the bridge. Three dimensional finite element (FE) model of the bridge is developed and analyzed. The numerical results show that the bridge response is very sensitive to the earthquake ground motions when PGA and Sa (Ti, 5%) are used as intensity measure (IM) and also indicated that the failure probability of the bridge system is dominated by the bridge piers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.131-140
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• 2018

A broadcast scheduling scheme is an essential technique to improve the performance of data broadcast systems. This paper explores the problem of data broadcast over multiple channels to reduce query response time, and proposes a new hybrid data broadcast scheduling scheme named HDAMA. The proposed scheme employs the strategy that combines the advantages of push-based broadcast and pull-based broadcast. HDAMA could enhance the performance of query response time since it is capable of controlling the influence of access probability properly reflecting the characteristics of multi-data queries and broadcasting pull data items relatively on time. Simulation is performed to evaluate the performance of the proposed scheme. The simulation results show that the performance of HDAMA is superior to other schemes in terms of the average response time.

• Korean Journal of Computational Design and Engineering
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• v.18 no.5
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• pp.359-366
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• 2013

In the lower arm design process, a tolerance optimization of the variance of design variables should be preceded before manufacturing process, since it is very cost-effective compared to a strict management of tolerance of products. In this study, a design of experiment (DOE) based on response surface model (RSM) was carried out to find optimized design variables of the lower arm, which can meet a given requirement of probability constraint for the process capability index (Cpk) of the weight and maximum stress. Then, the design space was explored by using the central composite design method, in which the 2nd order Taylor expansion was applied to predict a standard deviation of the responses. The optimal solutions satisfying the probability constraint of the Cpk were found by considering both of the mean value and the standard deviation of the design variables.

• 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.