• Steel and Composite Structures
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• v.32 no.5
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• pp.657-670
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• 2019

This paper proposes a one-dimensional fiber beam element model taking account of materially non-linear behavior, benefiting the highly efficient elastic-plastic analysis of girders with shear-lag effects. Based on the displacement-based fiber beam-column element, two additional degrees of freedom (DOFs) are added into the proposed model to consider the shear-lag warping deformations of the slabs. The new finite element (FE) formulations of the tangent stiffness matrix and resisting force vector are deduced with the variational principle of the minimum potential energy. Then the proposed element is implemented in the OpenSees computational framework as a newly developed element, and the full Newton iteration method is adopted for an iterative solution. The typical materially non-linear behaviors, including the cracking and crushing of concrete, as well as the plasticity of the reinforcement and steel girder, are all considered in the model. The proposed model is applied to several test cases under elastic or plastic loading states and compared with the solutions of theoretical models, tests, and shell/solid refined FE models. The results of these comparisons indicate the accuracy and applicability of the proposed model for the analysis of both concrete box girders and steel-concrete composite girders, under either elastic or plastic states.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.1
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• pp.17-26
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• 1990

A solution strategy for geometric non-linear analysis for the plane frame structures subjected to conservative and non-conservative forces is presented. By making efficient combination of the load incremental method and the displacement incremental method, this strategy can find postbuckling configuration such as snap-through and turning-back phenomena which cannot be easily found by the conventional load and displacement incrementation scheme. In the case of the analysis of the framed structure subjected to circulatory non-conservative forces, the total tangent stiffness matrix becomes unsymmetric and when calculating the incremental load and unbalanced load vector components, the direction change of the non-conservative forces is considered. Several example problems to demonstrate the feasibility of the present strategy, over ranges of deformation that are well beyond those likly to occur in practical framed structures, are given and discussed.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.288-296
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• 2004

This paper proposes a new mesh simplification algorithm using differential error metric. Many simplification algorithms make use of a distance error metric, but it is hard to measure an accurate geometric error for the high-curvature region even though it has a small distance error measured in distance error metric. This paper proposes a new differential error metric that results in unifying a distance metric and its first and second order differentials, which become tangent vector and curvature metric. Since discrete surfaces may be considered as piecewise linear approximation of unknown smooth surfaces, theses differentials can be estimated and we can construct new concept of differential error metric for discrete surfaces with them. For our simplification algorithm based on iterative edge collapses, this differential error metric can assign the new vertex position maintaining the geometry of an original appearance. In this paper, we clearly show that our simplified results have better quality and smaller geometry error than others.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of the Korean Society of Radiology
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• v.3 no.1
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• pp.29-39
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• 2009

In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.