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Akihiro Yamaguchi, Ken Ueno, Kazunori Uchida, Eiji Matsumoto, and Toshiyuki Saida. 2022c. Development of advanced AI technologies for condition diagnosis of high voltage switchgear in substations. CIGRE Science and Engineering, Vol. CSE 026 (2022), 1--11."},{"key":"e_1_3_2_2_72_1","unstructured":"Lexiang Ye and Eamonn Keogh. 2009. Time Series Shapelets: A New Primitive for Data Mining. In KDD. ACM 947--956.  Lexiang Ye and Eamonn Keogh. 2009. Time Series Shapelets: A New Primitive for Data Mining. In KDD. ACM 947--956."},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"crossref","unstructured":"Jidong Yuan Qianhong Lin Wei Zhang and Zhihai Wang. 2019. Locally Slope-Based Dynamic Time Warping for Time Series Classification. In CIKM. ACM 1713--1722.  Jidong Yuan Qianhong Lin Wei Zhang and Zhihai Wang. 2019. Locally Slope-Based Dynamic Time Warping for Time Series Classification. In CIKM. 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