• Steel and Composite Structures
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• v.29 no.3
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• pp.309-318
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• 2018

Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.456-461
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• 1995

The concept of hyperbolicity breaking is applied to predict the flow regime transition from inverted annular flow (IAF) to agitated inverted annular flow (AIAF). The resultant correlation has the similar form to Takenaka's empirical one. To validate the proposed model, it is applied to predict Takenaka's experimental results using R-113 refrigerant with four different tube diameters of 3, 5, 7 and 10 mm. The proposed model gives accurate predictions for the tube diameters of 7 and 10 min. As the tube diameter decreases, the differences between the predictions and the experimental results increase slightly. The flow regime transition from AIAF to dispersed flow (DF) is described by the drift flux model.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.380-386
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• 2001

In considering the issue of safely during emergency building evacuations, it is important to be able to make accurate predictions about evacuation conditions and to be able to assess safety levels. Simulation techniques are often used to make predictions regarding evacuation conditions. The two main types of prediction models are crowd flow models and discrete models. We have developed an evacuation simulation system based on the discrete model which attempts to address the implementation problems of existing evacuation models. Our model incorporates characteristics such as evacuee profiles and spatial considerations, and is capable of dynamically predicting the behavior of individual evacuees. The simulation system is primarily designed for buildings in which many people are incapacitated and require helpers in order to evacuate, such as hospitals and facilities fur the elderly. We show the results that the evacuation simulation system was used to perform two trial simulations.

• Journal of Mechanical Science and Technology
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• v.15 no.7
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• pp.889-894
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• 2001

In this study, the performances of various turbulence closure models are evaluated in the calculation of a transonic flow over axisymmetric bump. k-$\varepsilon$, explicit algebraic stress, and two Reynolds stress models, i.e., GL model proposed by Gibson & Launder and SSG model proposed by Speziale, Sarkar and Gatski, are chosen as turbulence closure models. SSG Reynolds stress model gives best predictions for pressure coefficients and the location of shock. The results with GL model also show quite accurate prediction of pressure coefficients down-stream of shock wave. However, in the predictions of mean velocities and turbulent stresses, the results are not so satisfactory as in the prediction of pressure coefficients.

• Journal of Ship and Ocean Technology
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• v.1 no.2
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• pp.1-10
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• 1997

The prediction of the total resistance of a ship is generally based on considering it to be a simple sum of the viscous resistance and the wave resistance. An experimental approach for predicting full-size ship resistance on this basis is practical but obviously has the deficiency that a model has to be built for each prototype of interest and the resulting tank tests are time consuming. On the other hand, purely theoretical calculations of the wave resistance, using, for example, the Michell theory, require relatively little computer time and give an excellent portrayal of the overall variation of the vessel resistance as a function of forward speed. Unfortunately, there are sufficient differences between this theory and the measured results to make this method impractical for design purposes. The proposal examined here is to use a data bank of experimental resistance results to modify the theoretical predictions. It is demonstrated that the technique will produce remarkably accurate resistance predictions and can take into account the effects of the water depth, any restriction of canal or river width, as well as the prismatic coefficient, and other geometric parameters.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.143-146
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• 2003

A finite element based on the efficient higher order zig-zag theory with multiple delaminations Is developed to refine the predictions of frequency and mode shapes. Displacement field through the thickness are constructed by superimposing linear zig-zag field to the smooth globally cubic varying field. The layer-dependent degrees of freedom of displacement fields are expressed in terms of reference primary degrees of freedom by applying interface continuity conditions including delaminated interfaces as well as free hounding surface conditions of transverse shear stresses. Thus the proposed theory is not only accurate but also efficient. This displacement field can systematically handle the number, shape, size, and locations of delaminations. Throught the dynamic version of variational approach, the dynamic equilibrium equations and variationally consistent boundary conditions are obtained. Through the natural frequency analysis and time response analysis of composite plate with multiple delaminations, the accuracy and efficiency of the present finite element are demonstrated. The present finite element is suitable in the predictions of the dynamic response of the thick composite plate with multiple delaminations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.253-256
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• 1996

Rainfall is one of the major and complicated elements of hydrologic system. Accurate prediction of rainfall is very important to mitigate storm damage. The neural network is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. In this dissertation, rainfall predictions by the neural network theory were presented. A multi-layer neural network was constructed. The network learned continuous-valued input and output data. The network was used to predict rainfall. The online, multivariate, short term rainfall prediction is possible by means of the developed model. A multidimensional rainfall generation model is applied to Seoul metropolitan area in order to generate the 10-minute rainfall. Application of neural network to the generated rainfall shows good prediction. Also application of neural network to 1-hour real data in Seoul metropolitan area shows slightly good predictions.

• Genomics & Informatics
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• v.19 no.4
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• pp.36.1-36.11
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• 2021

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.467-485
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• 2024

Policymakers and educational researchers have grown increasingly interested in the extent to which study results generalize across different groups of students. Current generalization research in education has largely focused on the compositional similarity among students based on a set of observable characteristics. However, generalization is defined differently across various disciplines. While the concept of compositional similarity is prominent in causal research, generalization among the statistical learning community refers to the extent to which a model produces accurate predictions across samples and populations. The purpose of this study is to assess the extent to which concepts related to contextual generalization (based on compositional similarity) are associated with the ideas related to model generalization (based on accuracy of prediction). We use observational data from the Programme for International Student Assessment (PISA) 2015 wave as a case study to examine the conditions under which contextual and model generalization are aligned. We assess the correlations between statistical measures that quantify compositional similarity and prediction accuracy and discuss the implications for generalization research.

• Journal of the Society of Disaster Information
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• v.3 no.1
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• pp.37-53
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• 2007

Various difficult problems occur due to insufficient bearing capacity or excessive settlements when constructing roads or large complexes. Accurate predictions on the final settlement and consolidation time can help in choosing the ground improvement method and thus enables to save time and expense of the whole project. Asaoka's method is probably the most frequently used for settlement prediction which are based on Terzaghi's one dimensional consolidation theory. Empirical formulae such as Hyperbolic method and Hoshino's method are also often used. However, it is known that the settlement predicted by these methods do not match with the actual settlements. Furthermore these methods cannot be used at design stage when there is no measured data. To find an elaborate method in predicting settlement in embankments using various test results and actual settlement data from domestic sites, Back-Propagation Neural Network(BPNN) and Recurrent Neural Network(RNN) were employed and the most suitable model structures were obtained. Predicted settlement values by the developed models were compared with the measured values as well as numerical analysis results. Analysis of the results showed that RNN yielded more compatible predictions with actual data than BPNN and predictions using cone penetration resistance were closer to actual data than predictions using SPT results. Also, it was found that the developed method were very competitive with the numerical analysis considering the number of input data, complexity and effort in modelling. It is believed that RNN using cone penetration test results can make a highly efficient tool in predicting settlements if enough field data can be obtained.