• Transactions of Materials Processing
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• v.9 no.5
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• pp.504-511
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• 2000

Hydroforming is a forming process enabling circular metal tubes to be produced in complex cross sections along curved axial paths. This forming process is widely used to manufacture parts in automotive industry. This paper presents bending and forming results to following angle of weld line positions. These compare to good bending, bad bending and without weld line model case. And then this result of after forming compare to each forming cases. The purpose of this paper is found that adaptive weld line position for bended final shape.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.285-288
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• 2007

The use of commercial finite element analysis software to perform the entire process analysis and springback analysis has increased fast for last decade. Pamstamp2G is one of commercial software to be used widely in the world but it has still not been perfected in the springback prediction accuracy. We must select the combination of input parameters for the highest springback prediction accuracy in Pamstamp2G because springback prediction accuracy is sensitive to input parameters. Then we study the affect of input parameters to use member part for acquiring high springback prediction accuracy in Pamstamp2G. First, we choose important four parameters which are adaptive mesh level at drawing stage and cam flange stage, Gauss integration point number through the thickness and cam offset on basis of experiment. Second, we make a orthogonal array table L82[(7)] which is consist of 8 cases to be combined 4 input parameters, compare to tryout result and select main factors after analyzing affect factors of input parameters by Taguchi's method in 6 sigma. Third, we simulate after changing more detail the conditions of parameters to have big affect. At last, we find the best combination of input parameters for the highest springback prediction accuracy in Pamstamp2G. The results of the study provide the selection of input parameters to Pamstamp2G users who want to Increase the springback prediction accuracy.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.10a
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• pp.75-78
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• 2004

A mesh generation algorithm adapted to the mesh density map using the Delaunay mesh generation technique is developed. In the finite element analyses of the forging processes, the numerical error increases as the process goes on because of discrete property of the finite elements or severe distortion of elements. Especially, in the region where stresses and strains are concentrated, the numerical discretization error will be highly increased. However, it is too time consuming to use a uniformly fine mesh in the whole domain to reduce the expected numerical error. Therefore, it is necessary to construct locally refined mesh at the region where the error is concentrated such as at the die corner. In this study, the point insertion algorithm is used and the mesh size is controlled by moving nodes to optimized positions according to a mesh density map constructed with a posteriori error estimation. An optimization technique is adopted to obtain a good position of nodes. And optimized smoothing techniques are also adopted to have smooth distribution of the mesh and improve the mesh element quality.

• PNF and Movement
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• v.6 no.3
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• pp.37-51
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• 2008

Purpose : This paper focuses on the emerging concept that adult central nervous system neurogenesis can be regulated by various physical activity, enriched environment, and pathological conditions. Neurogenesis-the production of new neuron-is an ongoing process that persists in the adult brain of mammalian, including humans. Result : The adult brain was thought be limited in its regenerative function. However, this concepts changed, recent evidence of neurogenesis in certain adult brain areas such as SVZ(subventricular zone) and SGZ(subgranular zone) in hippocampus, raised possibility for improved treatment for patient with stroke. Neural plasticity has an adaptive purpose, because an ability of the brain to change in response to peripheral stimulation, physical activity, experience, and injury. Conclusions : The major function of the neurogenesis in adult brain seems to be replacing the neuron that die regularly in discrete adult brain regions. These cells are capable of functionally integrating into neighboring neural cells, and reconnecting to the correct neural networks. This review suggest that various intervention, including physical activity, voluntary movement training, skilled forelimb reaching training, and enriched environment, induced neural cell production in certain adult brain, and associated with functional recovery after stroke.

• Journal of Ecology and Environment
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• v.45 no.2
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• pp.78-87
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• 2021

Background: To understand shade and wind effects on seedling traits of common reed (Phragmites australis), we conducted a mesocosm experiment manipulating day length (10 h daytime a day as open canopy conditions or 6 h daytime a day as partially closed canopy conditions) and wind speed (0 m/s as windless conditions or 4 m/s as windy conditions). Results: Most values of functional traits of leaf blades, culms, and biomass production of P. australis were higher under long day length. In particular, we found sole positive effects of long day length in several functional traits such as internode and leaf blade lengths and the values of above-ground dry weight (DW), rhizome DW, and total DW. Wind-induced effects on functional traits were different depending on functional traits. Wind contributed to relatively low values of chlorophyll contents, angles between leaf blades, mean culm height, and maximum culm height. In contrast, wind contributed to relatively high values of culm density and below-ground DW. Conclusions: Although wind appeared to inhibit the vertical growth of P. australis through physiological and morphological changes in leaf blades, it seemed that P. australis might compensate the inhibited vertical growth with increased horizontal growth such as more numerous culms, indicating a highly adaptive characteristic of P. australis in terms of phenotypic plasticity under windy environments.

• Computational Structural Engineering
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• v.10 no.4
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• pp.217-228
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• 1997

The high precision analysis by the p-version of the finite element method are fairly well established as highly efficient method for linear elastic problems, especially in the presence of stress singularity. It has been noted that the merits of the p-version are accuracy, modeling simplicity, robustness, and savings in user's and CPU time. However, little has been done to exploit their benefits in elasto-plastic analysis. In this paper, the p-version finite element model is proposed for the materially nonlinear analysis that is based on the incremental theory of plasticity using the constitutive equation for work-hardening materials, and the associated flow rule. To obtain the solution of nonlinear equation, the Newton-Raphson method and initial stiffness method, etc are used. Several numerical examples are tested with the help of the square plates with cutout, the thick-walled cylinder under internal pressure, and the circular plate with uniformly distributed load. Those results are compared with the theoretical solutions and the numerical solutions of ADINA

• Korean Journal of Child Studies
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• v.26 no.5
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• pp.185-204
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• 2005

This paper demonstrated how application of evolutionary knowledge to developmental perspectives enhances understanding of human ontogeny. Evolutionary Developmental Psychology (EDP) explains human behavior through evolutionary principles and focuses on ontogeny rather than phylogeny. In this paper, the authors review concepts of evolution, adaptations, and the processes of evolution from EDP perspectives. The definition and basic assumptions of EDP are introduced, followed by explanations of how evolution happens in ontogeny by looking at developmental systems approaches, concepts of ontogenetic and deferred adaptations, evolution of childhood, and brain plasticity. Possible pathways of evolution in ontogeny are also discussed. Finally, some research methodology for applying EDP to child development is suggested with specific hypotheses and studies.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.457-458
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• 2007

Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer poor performance of learning. To archive good clustering performance, we develop a hybrid neural network model. It is the combination of Multi-Layer Perceptron (MLP) and Adaptive Resonance Theory 2 (ART2). It inherits two distinct advantages of stability and plasticity from ART2. Meanwhile, by combining the merits of MLP, it improves the performance for clustering. Experiment results show that our model can be used for clustering with promising performance.

• Transactions of Materials Processing
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• v.13 no.4
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• pp.334-341
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• 2004

In this study, a remeshing algorithm adapted to the mesh density map using the Delaunay mesh generation method is developed. In the finite element simulation of forging process, the numerical error increases as the process goes on because of discrete property of the finite elements and distortion of elements. Especially, in the region where stresses and strains are concentrated, the numerical error will be highly increased. However, it is not desirable to use a uniformly fine mesh in the whole domain. Therefore, it is necessary to reduce the analysis error by constructing locally refined mesh at the region where the error is concentrated such as at the die corner. In this paper, the point insertion algorithm is used and the mesh size is controlled by using a mesh density map constructed with a posteriori error estimation. An optimized smoothing technique is adopted to have smooth distribution of the mesh and improve the mesh element quality.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2525-2527
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• 2004

This paper classify using Adaptive Resonance Theory 1(ART1) as a vigilance parameter of pattern clustering algorithm. Inherent characteristics of the model are analyzed. In particular the vigilance parameter $\rho$ and its role in classification of patterns is examined. Our estimates show that the vigilance parameter as designed originally does not necessarily increase the number of categories with its value but can decrease also. This is against the claim of solving the stability-plasticity dilemma. However, we have proposed a modified vigilance parameter setting criterion which takes into account the problem of subset and superset patterns and stably categorizes arbitrarily many input patterns in one list presentation when the vigilance parameter is closer to one. And this paper goal is the input pattern cognition and classification using neural network.