• The Journal of Korean society of community based occupational therapy
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• v.8 no.2
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• pp.29-37
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• 2018

Objective : Sleep disorder caused by stress or disease to elderly currently. We tried to make clear constant exercise according to exercise intensity would have effect on sleep disorder in elderly using EEG and melatonin. Methods : Thirty subjects were over 65 years old who lived a senior facility in some parts. They were able to communicate and understand the purpose of the study. They also expressed their intension to participate actively in experiment. They should have alert consciousness and orientation about time, place, and people. Sleep disorder was assessed via below 6 hr total sleep time and Pittsburge Sleep Quality Index, which was satisfied with both of criterion at the same time. Exercises composed of low intensity walking, moderate aerobic exercise, and high intensity resistance strength. We used QEEG 8-System (LAXTHA Inc. KOREA) to check wave type and Polysomnograpy (Compumedics, Australia) to test quality of sleep. and Histologic features were observed by TTC (triphenyltetrazolium chloride) staining and H & E (Hematoxylin & Eosin) staining. Results : There was a significant sleep index and change of melatonin after aerobic exercise. There was a significant SOL, S1, S2, and SWS in aerobic exercise, but there was a significant SWS in walking and resistance strength. There was a significant change of delta wave especially in frontal and temporal region between exercises. Conclusion : They had different effects according to type of exercise, when elderly who have chemical, behavioral change of circadian rhythms did exercise consistently. Aerobic exercise had more effect on sleep disorder than other exercise. Therefore, we may supply proper exercise to elderly and high quality of life.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.321-332
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• 2024

Understanding the status of surface cover in riparian zones is essential for river management and flood disaster prevention. Traditional survey methods rely on expert interpretation of vegetation through vegetation mapping or indices. However, these methods are limited by their ability to accurately reflect dynamically changing river environments. Against this backdrop, this study utilized satellite imagery to apply the Random Forest method to assess the distribution of vegetation in rivers over multiple years, focusing on the Naeseong Stream as a case study. Remote sensing data from Sentinel-2 imagery were combined with ground truth data from the Naeseong Stream surface cover in 2016. The Random Forest machine learning algorithm was used to extract and train 1,000 samples per surface cover from ten predetermined sampling areas, followed by validation. A sensitivity analysis, annual surface cover analysis, and accuracy assessment were conducted to evaluate their applicability. The results showed an accuracy of 85.1% based on the validation data. Sensitivity analysis indicated the highest efficiency in 30 trees, 800 samples, and the downstream river section. Surface cover analysis accurately reflects the actual river environment. The accuracy analysis identified 14.9% boundary and internal errors, with high accuracy observed in six categories, excluding scattered and herbaceous vegetation. Although this study focused on a single river, applying the surface cover classification method to multiple rivers is necessary to obtain more accurate and comprehensive data.