• Journal of applied mathematics & informatics
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• 제6권2호
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• pp.599-609
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• 1999

Neural networks are explored as an alternative to a regres-sion model for prediction of the number of daily household vehicular trips. This study focuses on contrasting a neural network model with a regression model in term of variable selection as well as the appli-cation of these models for prediction of extreme observations, The differences in the models regarding data transformation variable selec-tion and multicollinearity are considered. The results indicate that the neural network model is a viable alternative to the regression model for addressing both messy data problems and limitation in variable structure specification.

• 품질경영학회지
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• 제29권1호
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• 대한안전경영과학회지
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• 제1권1호
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• pp.91-100
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• 1999

We propose a decision model to incorporates the values assigned by a group of experts on different factors in selecting robots. Using this model, SN ratio of taguchi method for each of subjective factors as well as values of weights are used in this comprehensive method for robot selection. A numerical example is presented to illustrate the model and to show a rank reversal when compared to a model that does not eliminate extreme values and eliminates the highest and lowest experts' values allocating the weights and the subjective factors.

• 산업공학
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• 제20권3호
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• pp.376-386
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• 2007

The analytic hierarchy process (AHP), a well-known and useful decision making method, has been applied to R&D project evaluation and selection. The objective of this study is to propose a new model for evaluating and selecting R&D projects of Samsung Electro-Mechanics, the top manufacturer of electronic components in Korea, using the AHP. To show the validity of the new model, we strived to successively compare the final priorities for R&D projects with the priorities obtained by the existing model and the new model respectively.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.1059-1066
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• 2016

In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

• 한국정보처리학회논문지
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• 제7권3호
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• pp.852-861
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• 2000

Software reliability growth models are used in testing stages of software development to model the error content and time intervals betwewn software failures. In this paper, using priors for the number of fault with the negative binomial distribution nd the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carolo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2019년도 추계 학술논문 발표대회
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• pp.92-93
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• 2019

Estimates based on BIM makes it possible to perform from quantity take-off to construction cost estimates by using model, which is made in the phase of design and construction. As the BIM models are made up of the units of element, there an advantage of the automative quantity take-off, if the correction or change of element occurs. Work items, not included in the elements of the BIM model, are excepted from bill of quantity. Level of detail(LoD) of the BIM model can be improved for detailed estimates, but an excessive modeling for estimates is inefficient. This study presents the measure for selection and quantity take-off of work items, those are not expressed in the BIM model. The proposed method avoids the creation of excessive BIM Models and enables quantity take-off in conjunction with the element.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• 산업공학
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• 제25권1호
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• pp.96-105
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• 2012

Recently, the interoperability has become more important to enhance the net-centric capabilities of the warfighter. DITA (Defense Information Technical stAndard) is the set of IT standards for improving interoperability, scalability, effectiveness, and efficiency. In this paper, we analyzed the standardizing process to derive the selection criteria and structurized the derived selection criteria using the KJ (Kawakita Jiro) method. Finally, we developed an evaluation model for selecting a standard for DITA using AHP (Analytic Hierarchy Process). As a result, we present eight selection criteria (maintainability, trend, stability, portability, effect of other standard, constraint of the network, and applicability to the systems). We also applied some examples that several IT standards to our selection model for validating the model. We expect our model to help to decide objectively whether the new standard can be listed in DITA.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.876-881
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• 2004

Finite element model updating is an inverse problem to identify and correct uncertain modeling parameters that leads to better predictions of the dynamic behavior of a target structure. Unlike other inverse problems, the restrictions on selecting parameters all: very high since the updated model should maintains its physical meaning. That is, only the regions with modeling errors should be parameterized. And the variations of the parameters should be kept small while the updated results give acceptable correlations with experimental data. To avoid an ill-conditioned numerical problem, the number of parameters should be kept as small as possible. Thus it is very difficult to select an adequate set of updating parameters which meet all these requirements. In this paper, the importance of updating parameter selection is illustrated through a case study, and an automated procedure to guide the parameter selection is suggested based on simple observations. The effectiveness of the suggested procedure is tested with two example problems, ones is a simulated case study and the other is a real engineering structure.