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RISS 인기검색어
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- 저자 : 오윤주 ( Yun-ju Oh ) (검색결과 2 건)
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슬링을 이용한 플랭크 운동 시 로프 타입에 따른 배곧은근, 배바깥빗근, 척주세움근의 근활성도 비교
오윤주 ( Yun-ju Oh ),김령현 ( Ryeong-hyeon Kim ),이재하 ( Jae-ha Lee ),임한슬 ( Han-seul Lim ),정준 ( Jun Jeong ),황규미 ( Kyu-mi Hwang ),차용준 ( Yong-jun Cha ) 대전대학교 기초과학연구소 2023 혜화보건의료학회지 Vol.1 No.1
Exercise under unstable conditions is recommended as a method for enhancing the effectiveness of plank exercise. Among the preceding studies, there is a relatively insufficient study comparing muscle activity on the unstable support surface using various ropes of slings. The purpose of this study was to compare the muscle acitivities in rectus abdominis (lower part), external oblique, erector spinae in healthy 20’s during general plank exercise using sling with two different types of rope (elastic, or non-elastic). Thirty-five healthy 20’s without musculoskeletal, nerve, and cardiopulmonary diseases within the last 6 months participated. Muscle activities were measured during a general plank exercise, a plank exercise with sling using an elastic rope, or a plank exercise with sling using an non-elastic rope by maintaining 5 seconds. The order of plank exercise was randomized. There was no significant difference in the muscle activity of lower part of rectus abdominis, external oblique, and erector spinae according to the three different plank exercises. However, in all three plank exercises, the muscle activity of lower part of rectus abdominis was higher than that of external oblique and erector spinae. There was no meaningful difference of muscle activities in the three plank exercise methods, but plank exercise using a sling is considered to be the exercise method that can increase the muscle activity of the lower part of rectus abdominis the most.
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머신러닝 스태킹 앙상블을 이용한 자율주행 자동차 RADAR 성능 향상
장시연 ( Si-yeon Jang ),최혜림 ( Hye-lim Choi ),오윤주 ( Yun-ju Oh ) 한국인터넷정보학회 2024 인터넷정보학회논문지 Vol.25 No.2
레이다는 자율주행 차에 있어 필수적인 센서 부품으로, 레이다가 활용되는 시장은 점차 커지고 있으며 제품 종류도 다양해지고 있다. 본 연구에서는 평가 공정에서부터 레이다의 불량 여부를 예측해 자율주행의 안정성과 효율성을 높일 수 있도록 성능 예측 모델을 구축하고 평가하였다. 레이더 공정 과정의 39607개 입력 데이터로 모델을 학습하였으며, 결과적으로 17개 모델을 스태킹 앙상블했을 때 Meta Ridge 모델이 가장 높은 학습률을 나타내는 것을 확인하였다. 이러한 연구 결과가 제품의 불량을 공정 단계에서 우선 예측해 수율을 극대화하고 불량으로 인한 제품 폐기 비용을 감축하는 데 도움이 될 것으로 기대한다. Radar is an essential sensor component in autonomous vehicles, and the market for radar applications in this context is steadily expanding with a growing variety of products. In this study, we aimed to enhance the stability and performance of radar systems by developing and evaluating a radar performance prediction model that can predict radar defects. We selected seven machine learning and deep learning algorithms and trained the model with a total of 49 input data types. Ultimately, when we employed an ensemble of 17 models, it exhibited the highest performance. We anticipate that these research findings will assist in predicting product defects at the production stage, thereby maximizing production yield and minimizing the costs associated with defective products.
ScienceON
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