• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1059-1068
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• 2022

Recently, research using image-based deep learning is being conducted to determine similar fonts or recommend fonts. In order to increase the accuracy in judging the similarity of Hangul fonts, a previous study was conducted to calculate the similarity according to the combination of stroke elements. In this study, we tried to solve this problem by designing an integrated model that reflects the weights for each stroke element. By comparing the results of the user's font similarity calculation conducted in the previous study and the weighted model, it was confirmed that there was no difference in the ranking of the influence of the stroke elements. However, as a result of comparison by letter sizes, it was confirmed that there was a difference in the ranking of the influence of stroke elements. Accordingly, we proposed a weighted model set separately for each font size.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1552-1564
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• 2020

As various styles of fonts were used, there were problems such as output errors due to uninstalled fonts and difficulty in font recognition. To solve these problems, research on font recognition and recommendation were actively conducted. However, Hangul font research remains at the basic level. Therefore, in order to automate the comparison on Hangul font similarity in the future, we analyze the influence of each stroke element property. First, we select seven representative properties based on Hangul stroke shape elements. Second, we design a calculation model to compare similarity between fonts. Third, we analyze the effect of each stroke element through the cosine similarity between the user's evaluation and the results of the model. As a result, there was no significant difference in the individual effect of each representative property. Also, the more accurate similarity comparison was possible when many representative properties were used.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.05a
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• pp.101-105
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• 2005

Hemming process, composed of flanging, pre-hemming and main hemming, is the last one of a series of forming processes conducted on the automotive panels, having a great influence on the outward appearance of them. The hem quality can be quantitatively defined by the hemming defects including turn-down, warp and roll-in. However, it is difficult to evaluate and predict the hem quality through the experimental measurement or the numerical calculation since the size of defects is very small. This study is focused on how to simulate in the finite element analysis (FEA) the same conditions as in the experiment. The FEA result on turn-down, that was obtained from a finite element model including the spring element linked to the flanging pad, had a good correlation with the experimental data. It was found that the radius of curvature of the flange deeply affects the final hem quality and therefore high rigidity of forming tools and tight assembling tolerance are highly recommended. An over-stroke of the main hemming punch is also proposed to reduce the turn-down.