• 한국운동역학회지
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• 제28권4호
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• pp.213-218
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• 2018

Objective: The purpose of this study was to examine the effect of the core muscle strength enhancement of the elderly on 8 weeks training using the core exercise equipment for the elderly on the ability to control the whole-body center of mass in posture stabilization. Method: 16 females (10 exercise group, 6 control group) participated in this study. Exercise group took part in the core strength training program for 8 weeks with total of 16 repetitions (2 repetitions per week) using a training device. External perturbation during standing as pulling force applied at the pelvic level in the anterior direction was provided to the subject. In a UCM model, the controller selects within the space of elemental variables a subspace (a manifold, UCM) corresponding to a value of a performance variable that needs to be stabilized. In the present study, we were interested in how movements of the individual segment center of mass (elemental variables) affect the whole-body center of mass (the performance variable) during balance control. Results: At the variance of task-irrelevant space, there was significant $test^*$ group interactions ($F_{1,16}=7.482$, p<.05). However, there were no significant main effect of the test ($F_{1,16}=.899$, p>.05) and group ($F_{1,16}=1.039$, p>.05). At the variance of task-relevant space, there was significant $test^*$ group interactions ($F_{1,16}=7.382$, p<.05). However, there were no significant main effect of the test ($F_{1,16}=.754$, p>.05) and group ($F_{1,16}=1.106$, p>.05). Conclusion: The results of this study showed that the 8 weeks training through the core training equipment for the elderly showed a significant decrease in the $Vcm_{TIR}$ and $Vcm_{TR}$. This result indicates that the core strength training affects the trunk stiffness control strategy to maintain balance in the standing position by minimizing total variability of individual segment CMs.

• 한국정보기술학회논문지
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• 제17권9호
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• pp.19-30
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• 2019

태양광 발전은 일사량 및 온도 등 외부변화에 따른 안정적이고 효율적인 최대 전력 출력 전력점을 추적하기 위한 MPPT 알고리즘이 필요하다. 본 연구는 인공 신경망을 이용하여 기존 MPPT 알고리즘보다 신속하게 MPP를 추적할 수 있는 모델을 구현하였다. 제안 모델은 인공 신경망의 학습 데이터를 위해 다양한 일사량과 온도의 조합에 대해서 기존 MPPT 알고리즘으로 MPP의 전류와 전압을 찾았다. 획득한 MPP 데이터는 입력 노드를 일사량과 온도로 출력 노드를 전류와 전압으로 하여 학습하였다. 실험결과 일사량과 온도 변화가 있는 0~0.3t 구간에서 추적시간은 기존 알고리즘인 P&O와 InC 그리고 Fuzzy는 각각 잘못된 계산식t, 0.49t 그리고 0.4076t이였으며, 제안 모델은 0.32511t로서 기존 알고리즘 보다 0.1t 이상 신속하게 MPP를 추적하였다.