Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.

Refrigerators used in homes occupy a large portion of the power consumption. In addition, most of the power consumption of the refrigerator is used by the compressor. Therefore, it is necessary to study on the improvement of compressor efficiency to save energy. In this study, the suction system was changed to improve the efficiency of the linear compressor. For this, numerical analysis was performed by linking the 1 dimensional lumped model and the 2 dimensional axisymmetric CFD model. The numerical analysis results were also compared with the experimental results. As a result of the numerical analysis, the model with modified suction system increased EER by about 0.25% compared to the basic model, which was also confirmed by experiment.