• Journal of the Korea Fashion and Costume Design Association
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• v.6 no.3
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• pp.111-121
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• 2004

The influence of the 'well-being' trend on the every day life of our society can be found by recent books, articles, and magazines. The purpose of this study is to analyze the well-being trend and its effect on the current knitwear fashion. The results of this study can be summarized as follows; (1) The 'well-being' trend is very different from the past life style and it will be a preceding life style in 21st century. (2) The preference for organic food and the promotion of 'colorful menu' came from the 'well-being' trend. And, the yoga, zen, meditation, aroma-therapy, spa, and massage are also under the influence of 'well-being' trend. (3) The Neo-Hippie look and Neo-Vintage look are connected with the 'well-being' trend. (4) The influences of 'well-being' trend on the current knitwear fashion are shown in the hand knitting (bulky knit & crochet), twin sets including cardigans, hand crafted details, natural colors, and natural materials.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.33-41
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• 2023

As carbon-free has been getting interest, renewable energy sources have been increasing. However, renewable energy is intermittent and variable so it is difficult to predict the produced electrical energy from a renewable energy source. In this study, digital-twin concept is applied to solve difficulties in predicting electrical energy from a renewable energy source. Considering that rotation of wind turbine has high correlation with produced electrical energy, a model which simulates rotation in the drivetrain of a wind turbine is developed. The base of a drivetrain simulation model is set with well-known state equation in mechanical engineering, which simulates the rotating system. Simulation based machine learning is conducted to get unknown parameters which are not provided by manufacturer. The simulation is repeated and parameters in simulation model are corrected after each simulation by optimization algorithm. The trained simulation model is validated with 27 real wind turbine operation data set. The simulation model shows 4.41% error in average compared to real wind turbine operation data set. Finally, it is assessed that the drivetrain simulation model represents the real wind turbine drivetrain system well. It is expected that wind-energy-prediction accuracy would be improved as wind turbine digital twin including the developed drivetrain simulation model is applied.