대학 축구선수들의 여가활동 유형과 여가만족이 운동스트레스에 미치는 영향

나규민(Na, Gyu-Min),김대훈(Kim, Dae-Hoon),오세이(Oh, Sei-Yi) 한국여가레크리에이션학회 2022 한국여가레크리에이션학회지 Vol.46 No.2

The purpose of this study was to help university soccer players relieve sports stress through leisure satisfaction by efficiently utilizing their leisure time by identifying differences in leisure satisfaction and sports stress according to the types of leisure activities. In order to clarify this, the convenience sampling method was used for university soccer players registered in the Korea Football Association in 2021. The survey was conducted from September 4th to September 19th of 2021, and a total of 311 copies were used for data analysis. The following results were derived. First, significant differences were found in psychological leisure satisfaction, educational leisure satisfaction, physiological leisure satisfaction, and environment leisure satisfaction. Second, there were no significant differences in all leisure satisfaction sub-factors. Third, it was found that relaxational leisure satisfaction and physical leisure satisfaction had a significant effect on dissatisfaction with soccer skills, and environment leisure satisfaction was found to have a significant effect on restrict private life. As a result of this study, it is necessary to develop leisure education and leisure activity programs for university soccer players.

고장 신호 강화 이미지를 통한 기어박스 다중 클래스 고장 진단

나규민(Kyumin Na),김건(Keon Kim),윤헌준(Heonjun Yoon),윤병동(Byeng D. Youn) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.5

음향신호 에너지 감소 메커니즘 기반 확률적 보일러 튜브 누설 위치 추정

나규민(Kyumin Na),김형민(Hyeongmin Kim),이현찬(Hyeonchan Lee),윤병동(Byeng D. Youn) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12

Estimation of leak location is important considering the labor cost of maintenance procedure and downtime cost in thermal power plant. The well-known approach, TDOA(Time difference of arrival) has lots of problem on practical situation such as limitation of leak signal extraction and numerically not solving issue. To solve these kinds of difficulties, we use probabilistic approach considering energy decaying effect of sound such as attenuation and geometric spreading. In addition, we use bayesian updating method to prevent bias error caused by unpredictable outsource energy variation such as soot-blowing. Finally, We validate our method with simulation data and acoustic emission sensor data of real power plant from installed BTLD(Boiler tube leak detection) system.

굴삭기 스윙 기어박스의 고장 진단을 위한 주파수 에너지 불확실성 기반 진동 신호 데이터 증폭기법

나규민(Kyumin Na),하종문(Jong Moon Ha),김건(Keon Kim),윤병동(Byeng D. Youn) 대한기계학회 2018 대한기계학회 춘추학술대회 Vol.2018 No.12

This study was proposed for detecting the fault of swing gearbox in the excavator, and especially fault exists in sun gear component. To prevent the accident, it is necessary to monitor the health condition of a gearbox in the excavator. Generally, the excavator used in the industrial field have lots of noise related with not only sensor system but other component consisting the excavator. Additionally, it is hard to obtain the vibration signal due to the difficulty of installing the sensor system in the excavator in the industrial field. Therefore, the general machine learning algorithm for classifying the state of the gearbox have difficulty in learning the pattern induced by the fault because of the shortage of data. To get over this difficulty, we introduce the data augmentation techniques on frequency domain by considering the uncertainty of physical quantity. The proposed method is validated with the experiment, which shows the accuracy of several machine learning algorithm is higher with using the augmented data.

굴삭기 기어박스 고장 진단을 위한 고장 정보 강화 전처리 이미지 기반 트랜스퍼 러닝

나규민(Kyumin Na),박정호(Jungho Park),김윤한(Yunhan Kim),윤병동(Byeng D. Youn) 대한기계학회 2019 대한기계학회 춘추학술대회 Vol.2019 No.11

Excavator swing reduction gearbox is major component to rotate whole body. Therefore, they are vulnerable to breakdown by teeth in gearbox because they transmit huge power with high efficiency. For many years ago, there are lots of approach to detect these kinds of fault diagnosis of gearbox using feature extraction method based on fault information. However, most of them shows the result under the lab-level environment, so they have robustness problem when they apply to real industrial field. Also, deep learning based approach also have problems that they do not produce the result under different environmental situation, but they just truncate the signal in same situation. Therefore, they did not considering reassembling problem, so it has high possibility for over-fitting. Therefore, in our research, we conjoin preprocessing approach to eliminate the noise effect due to different environment with transfer learning to solve non-linear hyperplane classification problem. We show that deep learning approach have weakness to over-fitting by comparing how much accuracy under the preprocessing could increase. We also use gradCAM and t-SNE result to briefly explain the result of our algorithm. Further research to make algorithm robust under different operating condition are also discussed.

굴삭기 스윙 기어박스의 강건한 고장 감지를 위한 ARMED 필터의 오더 최적화

나규민(Kyumin Na),김건(Keon Kim) 대한기계학회 2017 대한기계학회 춘추학술대회 Vol.2017 No.11

This paper proposes the order parameter optimization technique of autoregressive minimum entropy deconvolution(ARMED). Traditionally, the best order is selected by Akaike information criterion(AIC) which is same as expected squared prediction error (ESPE). However, in practically, there are many cases that AIC value does not have minimum value and decreases monotonically. This is because residual signal could be disturbed by the other vibration source or noise even appropriate preprocessing. Therefore, AIC’s basic assumption could be violated and it does not work well. This paper proposes new order selection techniques based on health feature related with envelop frequency using Hilbert transform. The simulation and experimental result show that the selected order using these techniques make better performance in signal filtered by ARMED for differentiating normal and fault state than the order obtained by AIC.

호흡기 알레르기 환아에서 집먼지진드기 감작과 출생 월 분포의 연관성

강은경,나규민,강희,유영,고영률,Kang, Eun Kyeong,Na, Kyu Min,Kang, Hee,Yoo, Young,Koh, Young Yull 대한소아청소년과학회 2003 Clinical and Experimental Pediatrics (CEP) Vol.46 No.4

목 적 : 출생 후 영아기에 알레르겐에 대한 노출 여부가 추후 감작과 알레르기질환의 발생에 중요한 것으로 알려져 왔다. 우리나라에서는 집먼지진드기가 알레르기질환의 주요 흡입 알레르겐으로, 집먼지진드기 항원량은 계절적으로 가을에 가장 높은 것으로 보고되었다. 이에 저자들은 소아 호흡기 알레르기 환자에서 집먼지진드기에 대한 감작 여부에 따라서 출생 월 분포의 차이가 있는지 알아보기 위해 본 연구를 시행하였다. 방 법 : 1995년 1월부터 2002년 5월까지 만성 호흡기 증상으로 서울대학교병원 어린이병원을 방문하여 알레르기 피부단자시험과 메타콜린 유발시험을 시행 받은 환아 1,327명을 대상으로 집먼지진드기에 대한 감작 여부와 출생 월을 조사하여, 집먼지진드기 감작 유무에 따라 출생 월 분포를 비교하였다. 결 과 : 한 가지 이상의 알레르겐에 피부시험 양성을 보인 아토피군은 864명(65.1%)이고 비아토피군은 463명(34.9%)이었다. 아토피군에서 집먼지진드기에 양성을 보인 환아의 수는 787명(91.1%)이고 집먼지진드기를 제외한 나머지 피부시약에 양성반응을 보인 수는 77명(8.9%)이었다. 집먼지진드기 아토피군과 비아토피군의 출생 월별 분포를 기대 환자수와 비교했을 때 집먼지진드기 알레르기를 가진 환아들은 계절적으로 8월에서 11월까지 기대 환자수 보다 유의하게 많이 출생하였고(P=0.03) 비아토피군에서는 상기와 같은 출생 월 분포를 보이지 않았다. 천식으로 진단된 환아는 총 543명(40.9%)이었고 이중 아토피성 천식 환아는 421명(77.5%)이었고 비아토피성 천식 환아가 122명(22.5 %)이었다. 아토피성 천식 환아 중 집먼지진드기 아토피성인 환아는 387명(91.9%)이었다. 집먼지진드기 아토피성 천식과 비아토피성 천식 환아에서 출생 월의 계절별 비교에서 8월에서 11월까지 집먼지진드기 아토피성 천식 환아가 비아토피성 천식 환아보다 유의하게 많이 출생하였다(P=0.002). 결 론 : 출생 월은 알레르겐에 대한 감작 여부에 연관이 있는 것으로 보이며, 우리나라에서는 8월에서 11월까지의 출생이 집먼지진드기에 대한 감작의 위험이 큰 시기로 보인다. Purpose : It has been suggested that the exposure to aeroallergens during early infancy after birth is important in the subsequent development of sensitization and allergic diseases. In Korea, the level of house dust mites as one of the important aeroallergens is known to be the highest in autumn. The aim of this study was to test whether the distribution of month of birth bears a relationship to the presence of mite sensitization in children with respiratory allergy. Methods : Skin prick tests and methacholine provocation tests were performed on 1,327 patients with chronic respiratory symptoms who visited Seoul National University Children's Hospital from January 1995 to May 2002. An analysis of patients' month of birth distribution according to the presence of mite sensitization was performed. Results : Atopic subjects who had at least one positive skin test numbered 864(65.1%); and non-atopic subjects numbered 463(34.9%). Among atopic subjects, 787(59.3%) had positive skin tests to mites and 77(5.8%) had positive skin test only to minor allergens. A significantly greater than expected number of mite atopic subjects were born in the months between August and November(P=0.03), however, the birth month of non-atopic subjects didn't show a consistent seasonal preference. Asthma patients numbered 543(40.9%). Among these, atopic asthmatics numbered 421(77.5%) and non-atopic asthmatics, 122(22.5%). Dust-mite atopic asthmatics numbered 387(91.9%) out of 421 atopic asthmatics. Dust-mite atopic asthmatics were born significantly higher in the season lasting from August to November in comparison to non-atopic asthmatics(P=0.002). Conclusion : Month of birth seems to be related with sensitization to allergens. Our results show that August to November is the risk period for the development of mite sensitization in Korea.

증기 터빈 이상 감지를 위한 동적 기준치 개발

고진욱(Jin Uk Ko),나규민(Kyumin Na),김형민(Hyeongmin Kim),윤병동(Byeng D. Youn) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12

건설장비 기어박스 진단을 위한 회전 속도 추정 방법 개발

김건(Keon Kim),나규민(Kyumin Na),이진욱(Jinwook Lee),유진오(Jinoh Yoo),윤병동(Byeng D. Youn) 한국자동차공학회 2023 한국자동차공학회 부문종합 학술대회 Vol.2023 No.5

윈도우 특성 이미지 기반 합성곱 신경망을 활용한 보일러 상태 진단

이현찬(Hyeonchan Lee),김형민(Hyeongmin Kim),나규민(Kyumin Na),윤병동(Byeng D. Youn) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12

Thermal power plant boiler is one of the main facility that produce steam. Long and thin tubes are inside boiler to generate steam efficiently. Because boiler operate under harsh condition, boiler tube is prone to leakage causing unexpected shutdown of the power plant. To detect leakage, various kind of signals are collected from boiler tube and acoustic signals are the most sensitive signal to tube leakage. However, some event unrelated to leakage cause increase in acoustic signal trend, making it harder to determine the status of boiler. Soot Blowing, cleaning procedure of soot deposited on the internal furnace tubes, is a representative event. In this paper, we propose a novel leakage detection method using Sliding Window Correlation(SWC) Matrix and Sliding Window Energy(SWE) Matrix. Two feature images are trained with Convolutional Neural Network(CNN). Test result from domestic power plant data shows that the proposed method can successfully classify normal, soot blowing and leakage.