• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.145-148
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• 2019

Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.280-295
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• 2022

This paper contains an introduction to industrial problems, solutions, and results conducted with the Korea Association of Machinery Industry. The client company commissioned the problem of upgrading the method of identifying global supply risky items. Accordingly, the factors affecting the supply and demand of imported items in the global supply chain were identified and the method of selecting risky items was studied and delivered. Through research and discussions with the client companies, it is confirmed that the most suitable factors for identifying global supply risky items are 'import size', 'import dependence', and 'trend abnormality'. The meaning of each indicator is introduced, and risky items are selected using export/import data until October 2022. Through this paper, it is expected that countries and companies will be able to identify global supply risky items in advance and prepare for risks in the new normal situation: the economic situation caused by infectious diseases such as the COVID-19 pandemic; and the export/import regulation due to geopolitical problems. The client company will include in his report, the method presented in this paper and the risky items selected by the method.