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Effect of Regular Plyometric Training on Growthrelated Factors in Obesity Female Teenager
최준원(Junwon Choi),홍지나(Jeana Hong),최영화(Youngha Choi),박규민(Kyumin Park),이진석(Jinseok Lee),강성훈(Sunghwun Kang) 대한운동학회 2024 아시아 운동학 학술지 Vol.26 No.1
OBJECTIVES This study aimed to investigate the effect of regular plyometric training on growth-related factors in obese female teenager. METHODS The subjects of the study consisted of elementary school students group (EG, n=5) and middle school students group(MG, n=6), and overweight or obese experimenters were selected based on the ‘2017 Child and Adolescent Growth Chart Age Body Mass’ index. Exercise was conducted for 12 weeks. All measurements were carried out before and after exercise. The data processing was verified using the SPSS 26.0 statistical program to verify the correlation between paired t-test and Pearson in the 12-week pretraining and post-training groups. RESULTS After 12 weeks of plyometric training, there were significant differences in height(p=.002), ASIS(p=.003), body fat percentage(p=.018), and muscle mass(p=.014) among body composition of EG. There was a significant difference in height(p=.015) in body composition of MG. In the evaluation of muscle function, in muscle strength(60°/sec), (R)-FLE PT/bw(p=.011), (L)-FLE PT/bw(p=.017) in EG and muscle power(180°/sec), (R)-FLE PT/bw(p=.024), (L)-EXT PT/bw(p=.001), (R)-FLE TW/bw(p=.004) and (L)-EXT TW/ bw(p=.012) showed a statistically significant difference. In terms of correlation, significant relationships were found between EG body fat mass and IGF-1(p<.05), and between body fat mass and IGF-1/IGF-BP3(p<.05). CONCLUSIONS Regular plyometric training had a positive effect on growth-related factors in obese female teenager.
새로운 고차 큐뮬런트를 이용한 위상 매핑 식별 및 변조 분류 알고리즘
이재윤(Jaeyoon Lee),안성진(Seongjin Ahn),최준원(Junwon Choi),윤동원(Dongweon Yoon) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.2
In this paper, we propose a new algorithm for automatic modulation classification(AMC) based on higher-order cumulants of BPSK, QPSK, 8PSK, 16QAM, 64QAM schemes including the signal constellation with different phase mapping. For this purpose, we extract new higher-order cumulant features appropriate for recognizing the phase mapping and design the algorithm, so that it has superior performance in terms of the probability of correct classification(PCC), combining the conventional cumulant features with the new cumulant features. The proposed algorithm includes the cumulant calculation using the only real part of received signal to minimize the increased computational complexity due to additional features. Also, we verify the validity of the proposed algorithm by comparing with conventional algorithm in terms of PCC for each and average PCC for all of the modulation schemes.
차량 CAN 데이터를 이용한 딥러닝 기반 실시간 운전자 성향 분류 기법
신윤식(YunSik Shin),송희진(HeeJin Song),박재용(JaeYong Park),최준원(JunWon Choi) 한국자동차공학회 2020 한국자동차공학회 부문종합 학술대회 Vol.2020 No.7
In this paper, we propose a deep learning-based driver’s style classification method using the CAN data. We collected the 7 hours of CAN data for 100 drivers with different driving style in suburb area in Kwangju province. We categorized the driving style into “sport” versus “comfort” based on the surveys from both driver and passenger. We use the 1D Convolutional Neural Network(1D-CNN) model to extract the feature from 25.6 seconds of the CAN data and train the model to classify between the sport and comfort styles from the feature. Our evaluation shows that the proposed method achieves the accuracy of 80.94% for the test dataset and exhibits reasonable rea-time classification performance when deployed in a test vehicle.
이재한 ( Jaehan Lee ),이재현 ( Jaehyeon Lee ),유정민 ( Jeongmin Yu ),최준원 ( Junwon Choi ),김영종 ( Youngjong Kim ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.1
시각 장애인이 겪는 의복 선택 및 조합 문제를 해결하고자, 접근성 기반의 사용자 인터페이스 및 사용자 경험 설계, 이미지 처리, 기계 학습 등의 기술을 활용한 <시각 장애인을 위한 의상 조합 추천 애플리케이션>를 개발을 제안한다.
페이딩 환경에서의 딥러닝 기반 고성능 자동 변조분류 기법
이정환(Junghwan Lee),김재겸(Jaekyum Kim),김병도(Byeoungdo Kim),윤동원(Dongweon Yoon),최준원(Junwon Choi) 한국정보기술학회 2018 한국정보기술학회논문지 Vol.16 No.1
In this paper, we propose a deep learning-based method for automatically classifying modulation formats in wireless communication systems. While existing automatic modulation schemes are mostly designed for Gaussian channels, these techniques tend not to work well in fading environments. The proposed method extracts various kinds of statistical feature values from the data and classifies the modulation class using the deep neural network consisting of fully connected layers. In order to apply the proposed automatic modulation classification scheme in the fading channel, the training data is generated considering the fading environment and the deep neural network is trained by using it. As a result of applying the proposed method to the five kinds of modulation classifications of BPSK, QPSK, 8-PSK, 16-QAM and 64-QAM, we obtained better results in terms of classification accuracy than the existing methods in the fading environment.