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HSU를 탑재한 기계-유압식 무단변속기의 동력특성에 관한 고찰
정한영(H. Y. Jung),정동수(D. S. Jung),김종철(J. C. Kim),문준혁(J. H. Mun) 유공압건설기계학회 2019 유공압건설기계학회 학술대회논문집 Vol.2019 No.6
The mechanical-hydraulic continuously variable transmission, HMT, is emerging as a core technology for medium and large tractors that are geared to the aging trend of rural areas. HMT combines the two transmission structures of mechanical power and hydraulic power to complement each other’s shortcomings, so it has excellent mobility, almost no shift shock, and transmission efficiency is improved. The HMT implementation is the HSU, which is mandatory, including a detailed description, a detailed description, and a detailed description. In this research, we analyze the internal structure and power transmission of HSU and planetary gear that are the core technologies of HMT, present the test results of prototype, and introduce the current state of HMT research conducted in Korea.
홍상표(S. P. Hong),김연욱(Y. W. Kim),조우형(W. H. Cho),좌경림(K. L. Joa),정한영(H. Y. Jung),김규성(K. S. Kim),이상민(S. M. Lee) 한국재활복지공학회 2017 재활복지공학회논문지 Vol.11 No.1
본 논문에서는 균형평가도구 중 임상에서 가장 많이 사용되는 BBS(Berg Balance Scale)를 머신러닝 기법을 이용하여 점수 분류 정확도를 제시한다. 데이터취득은 Noraxon 시스템을 이용하여, 신체 8군데(왼쪽·오른쪽 발목, 왼쪽·오른쪽 엉덩이 위, 왼쪽·오른쪽 손목, 등(Back), 이마)에 관성센서를 부착하였다. 관성센서의 3축 가속도데이터를 기반으로 특징벡터 STFT(Short Time Fourier Transform), SAM(Signal Area Magnitude)를 추출하였다. 그 다음, BBS의 항목을 동작특성에 따라 정적인 동작(static movement)과 동적인 동작(dynamic movement)으로 나누었고, BBS의 각 항목에 대하여 점수에 영향이 있는 센서부착위치에 따라 특징벡터를 선별하였다. BBS의 항목마다 선별된 특징벡터는 GMM(Gaussian Mixture Model)을 이용하여 분류하였다. 실험대상자 40명에 대한 정확도 산출결과, 1번순으로 차례대로 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%의 분류 정확도를 확인하였다. In this paper, we present the score classification accuracy of BBS(Berg Balance Scale) which is the most commonly used balance evaluation tool using machine learning. Data acquisition was performed using the Noraxon system and an inertial sensor of Noraxon system was attached to the body in 8 locations (left and right ankle, left and right upper buttocks, left and right wrists, back, forehead). Based on the 3-axis accelerometer of the inertial sensor, the feature vector STFT(Short Time Fourier Transform) and SAM(Signal Area Magnitude) were extracted. Then, the items of the BBS were divided into static movement and dynamic movement depending on the operation characteristics, and the feature vectors were selected according to the sensor attachment positions which affect the score for each item of the BBS. Feature vectors selected for each item of BBS were classified using GMM(Gaussian Mixture Model). As a result of the accuracy calculation for 40 subjects, 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%, respectively.