• Korean Journal of Applied Biomechanics
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• v.28 no.4
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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.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.19-30
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• 2019

Photovoltaic power generation requires MPPT algorithm to track stable and efficient maximum power output power point according to external changes such as solar radiation and temperature. This study implemented a model that could track MPP more quickly than original MPPT algorithm using artificial neural network. The proposed model finds the current and voltage of MPP using the original MPPT algorithm for various combinations of insolation and temperature for training data of artificial neural networks. The acquired MPP data was learned using the input node as insolation and temperature and the output node as the current and voltage. The Experiment results show tracking time of the original algorithms P&O, InC and Fuzzy were respectively 0.428t, 0.49t and 0.4076t for the 0t~0.3t range, and MPP tracking time of the proposed model was 0.32511t and it is 0.1t faster than the original algorithms.