• Earthquakes and Structures
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• v.22 no.6
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• pp.609-623
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• 2022

To study the empirical seismic fragility of a reinforced concrete girder bridge, based on the theory of numerical analysis and probability modelling, a regression fragility method of a rapid fragility prediction model (Gaussian first-order regression probability model) considering empirical seismic damage is proposed. A total of 1,069 reinforced concrete girder bridges of 22 highways were used to verify the model, and the vulnerability function, plane, surface and curve model of reinforced concrete girder bridges (simple supported girder bridges and continuous girder bridges) considering the number of samples in multiple intensity regions were established. The new empirical seismic damage probability matrix and curve models of observation frequency and damage exceeding probability are developed in multiple intensity regions. A comparative vulnerability analysis between simple supported girder bridges and continuous girder bridges is provided. Depending on the theory of the regional mean seismic damage index matrix model, the empirical seismic damage prediction probability matrix is embedded in the multidimensional mean seismic damage index matrix model, and the regional rapid prediction matrix and curve of reinforced concrete girder bridges, simple supported girder bridges and continuous girder bridges in multiple intensity regions based on mean seismic damage index parameters are developed. The established multidimensional group bridge vulnerability model can be used to quantify and predict the fragility of bridges in multiple intensity regions and the fragility assessment of regional group reinforced concrete girder bridges in the future.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.343-351
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• 2008

By supervised learning with p predictors, we frequently obtain a prediction function of the form $y\;=\;f(x_1,...,x_p)$. When $p\;{\geq}\;3$, it is not easy to understand the inner structure of f, except for the case the function is formulated as additive. In this study, we propose to use p simple graphs for visual understanding of complex prediction functions produced by several supervised learning engines such as LOESS, neural networks, support vector machines and random forests.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.2
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• pp.49-56
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• 2003

The welding deformation of a hull structure in the shipbuilding industry is Inevitable at each assembly stage. The geometric inaccuracy caused by the welding deformation tends to preclude the introduction of automation and mechanization and needs the additional man-hours for the adjusting work at the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurateprediction method which can explicitly account for the influence of various factors on the welding deformation. The validity of the prediction method must be also clarified through experiments. This paper is aimed at deriving the simple prediction model for fillet welding deformations. For this purpose, the thermal elasto-plastic analysis varying the welding conditions and plate thickness has been performed. On the basis of numerical results, the formulae for angular distortion and transverse shrinkage have been derived through the regression analysis. Experimental work has been also carried out to clarify the validity of numerical results. It has been found that the numerical results show a good agreement with those of experiments

• Journal of the Society of Naval Architects of Korea
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• v.41 no.2
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• pp.106-113
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• 2004

The block assembly of ship consists of a certain type of heat processes such as cutting, bending, welding, residual stress relaxation and fairing. The residual deformation due to welding is inevitable at each assembly stage. The geometric inaccuracy caused by the welding deformation tends to preclude the introduction of automation and mechanization and needs the additional man-hours for the adjusting work at the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurate prediction method which can explicitly account for the influence of various factors on the welding deformation. Systematic and quantitative theoretical works to clarify the effects of various factors on the welding deformation have rarely been found. Therefore, in this paper, the effects of various factors, such as welding process and gravity on the butt welding deformation have been investigated through a number of numerical analyses. In addition, this paper proposes a simplified analysis method to predict the butt welding deformation in actual plate structure. For this purpose, a simple prediction model for butt welding deformations has been derived based on numerical and experimental results through the regression analysis. Based on these results, the simplified analysis method has been applied to some examples to show its validity.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1261-1271
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• 2019

Droplet size and distribution are important parameters determining venturi scrubber performance. In this paper, we proposed physical models for a maximum stable droplet size prediction and upper limit log-normal (ULLN) distribution parameters. For the proposed maximum stable droplet size prediction model, a Eulerian-Lagrangian framework and a Reitz-Diwakar breakup model are solved simultaneously using CFD calculations to reflect the effect of multistage breakup and droplet acceleration. Then, two ULLN distribution parameters are suggested through best fitting the previously published experimental data. Results show that the proposed approach provides better predictions of maximum stable droplet diameter and Sauter mean diameter compared to existing simple empirical correlations including Boll, Nukiyama and Tanasawa. For more practical purpose, we developed the simple, one dimensional (1-D) calculation of Sauter mean diameter.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.1-24
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• 1996

In selecting a final tree, Breiman, Friedman, Olshen, and Stone(1984) compare the prediction risks of a pair of tree, where one contains the other, using the standard error of the prediction risk of the larger one. This paper proposes an approach to selection of a final tree by using the standard error of the difference of the prediction risks between a pair of trees rather than the standard error of the larger one. This approach is compared with CART's for simulated data from a simple regression model. Asymptotic results of the approaches are also derived and compared to each other. Both the asymptotic and the simulation results indicate that final trees by CART tend to be smaller than desired.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1069-1077
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• 2009

For external noise prediction of railway vehicles, sophisticated individual source modelling as well as appropriate noise propagation model from the sources is necessary to ensure the accuracy of the predicted results and contributions of each equipment to the overall noise levels. Accurate and reasonable identification procedures of sound sources of equipment including source strength, directivity and positions installed in the train play an important role in a prediction model, since it is not easy to establish a simple model for the sources with a single rule due to the complexity of source characteristics of equipment in size and directivity pattern. This paper guidelines practical considerations for identification of noise sources in railway vehicles including typical source characteristics of several sub-systems that emits noise to the environment, particularly for electric multiple unit(EMU), and verify effectiveness of assumptions used in the modelling of equipment by measurement with a simple part. The predicted external noise level of a complete train using Exnoise, which was developed by Hyundai-Rotem and has been verified in the a lot of field-tests, incorporating source modelling considered in this paper shows close correlation with the measured ones.

• Journal of the Korean Solar Energy Society
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• v.39 no.5
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• pp.41-52
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• 2019

The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.6
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• pp.216-222
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• 1996

This paper presents a computational technique to predict roof crush resistance in early design stage of passenger car development. This technique use a simple F.E. model with nonlinear spring elements which represent plastic hinge behavior at weak areas. By assuming actual sections as equivalent simple sections, maximum bending moments which weak areas in major members can stand are theoretically calculated. Results from prediction of roof crush resistance are correlated well with test results.