• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.375-376
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• 2005

• Journal of Welding and Joining
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• v.27 no.4
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• pp.6-12
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• 2009

• Proceedings of the PSK Conference
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• 2001.10a
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• pp.244.3-245
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• 2001

• The Bulletin of The Korean Astronomical Society
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• v.34 no.1
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• pp.159-159
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• 2009

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.771-774
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• 1987

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2000.11a
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• pp.70-70
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• 2000

• Information Systems Review
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• v.3 no.1
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• pp.73-82
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• 2001

• Proceedings of the Korean Magnestics Society Conference
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• 2008.12a
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• pp.270.2-270.2
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• 2008

• The Journal of Internal Korean Medicine
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• v.39 no.5
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• pp.895-903
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• 2018

Objectives: This study reports on the efficacy of using integrative Korean medical treatments for Type 1 complex regional pain syndrome (CRPS). Methods: A 48-year-old female patient with Type 1 R/O CRPS was treated with herbal medicines, acupuncture, and pharmacopuncture for 38 days. The chief complaints were severe burning pain, general weakness, sleep disorder, and aggressive and depressed mood. The treatment effect was evaluated by measuring the numerical rating scale (NRS) of pain, improvement of the quality of sleep, and change in mood status. Results: After the hospital treatment, the patient's pain was controlled and the NRS score was decreased. Sleep and mood disorder also improved. Conclusions: The integrative Korean medical treatments appeared to be effective in reducing Type 1 CRPS symptoms. Further clinical research of patients with CRPS is needed.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.4
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• pp.234-242
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• 2021

In this study, we introduce the prediction of brake power for low-speed full ships and container carriers using the linear regression and a machine learning approach. The residual resistance coefficient, wake fraction coefficient, and thrust deduction factor are predicted by regression models using the main dimensions of ship and propeller. The brake power of a ship can be calculated by these coefficients according to the 1978 ITTC performance prediction method. The mean absolute error of the predicted power was under 7%. As a result of several validation cases, it was confirmed that the machine learning model showed slightly better results than linear regression.