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Sung Ji Yeon,Seo Jong Do,Ko Dae-Hyun,박민정,Hwang Sang Mee,Oh Sohee,Chun Sail,성문우,Song Junghan,Song Sang Hoon,Park Sung Sup 대한진단검사의학회 2021 Annals of Laboratory Medicine Vol.41 No.2
Background: Reference intervals defined for adults or children of other ethnicities cannot be applied in the evaluation of Korean pediatric patients. Pediatric reference intervals are difficult to establish because children are in their growing stage and their physiology changes continuously. We aimed to establish reference intervals for routine laboratory tests for Korean pediatric patients through retrospective multicenter data analysis. Methods: Preoperative laboratory test results from 1,031 pediatric patients aged 0 month–18 years who underwent minor surgeries in four university hospitals were collected. Age- and sex-specific reference intervals for routine laboratory tests were defined based on the Clinical and Laboratory Standards Institute (CLSI) EP28-A3c guidelines. Results: The pediatric reference intervals determined in this study were different from existing adult reference intervals and pediatric reference intervals for other ethnicities. Most tests required age-specific partitioning, and some of those required sex-specific partitioning for at least one age-partitioned subgroup. Erythrocyte sedimentation rate, monocyte percentage, basophil percentage, activated partial thromboplastin time, glucose, cholesterol, albumin, bilirubin, chloride, and C-reactive protein did not show any difference between age- or sex-partitioned subgroups. Conclusions: We determined Korean pediatric reference intervals for hematology, coagulation, and chemistry tests by indirect sampling based on medical record data from multiple institutions. These reference intervals would be valuable for clinical evaluations in the Korean pediatric population.
최정철(Choi Jungchul),손은국(Son Eunkuk),이광세(Lee Gwangse),강민상(Kang Minsang),이진재(Lee Jinjae),황성목(Hwang Sungmok),박사일(Park Sail) 한국태양에너지학회 2021 한국태양에너지학회 논문집 Vol.41 No.4
Continuous fatigue information is essential for the structural health monitoring (SHM) of wind turbines. Faults, such as sensor failure, data loss, and cable disconnection, can result in a total loss of SHM. To avoid such a malfunction, machine learning algorithms and polynomial curve fitting are suggested to predict the missing fatigue data from the otherwise known measurement data. Artificial neural networks showed the best prediction performance. Decision trees and regularized linear regression are also powerful alternatives.
1.5 MW 풍력 터빈 소음 측정 및 저주파 소음 특성 분석
손은국(Eunkuk Son),이광세(Gwang-Se Lee),이진재(Jinjae Lee),강승진(Seungjin Kang),황성목(Sungmok Hwang),박사일(Sail Park),김석우(Seokwoo Kim) 한국신재생에너지학회 2018 신재생에너지 Vol.14 No.4
The noise from a 1.5 MW wind turbine was measured and the apparent sound power level, tonal audibility, and spectrum balance were analyzed. The apparent sound power level and tonal audibility were analyzed according to IEC 61400-11: ed3. (2012-11). The measured noise data at the turbine site was mainly in the north-west (NW) and north-north-west (NNW) directions, and approximately 500 and 250 samples of total noise and background noise data were obtained. Three tone components were observed in the low frequency region below 100 Hz. The tones in the low frequency critical band with a center frequency of 78.1 Hz were found to have a higher level than the hearing threshold. The possibility of wind turbine noise annoyance was also analyzed through the spectrum balance analysis.