• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.379-384
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• 2020

The purpose of this study is to analyze the impact of individual savings banks' financial conditions on their loan portfolio after savings bank restructuring. The analysis results are as follows. First, it was estimated that the relationship between the rate of change in the NPL Ratio and the ratio of household loans has a significant positive value. Second, it was estimated that the interaction effect between the rate of change in the ratio of fixed and below loans and the spread of the deposit-to-deposit rate has a significant negative (-) value with the household loan weight. Third, the relationship between the asset size and the proportion of household loans was estimated to have a significant positive (+) value. In other words, it was analyzed that the financial situation of the savings bank affects the loan portfolio, and it should provide important implications for establishing policies for each financial situation of the savings bank. Depending on the financial situation in the future, there is a need to avoid excessive asset expansion of specific loans and preemptive soundness management.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.