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A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection.
Yu, Jiahao; Gao, Xin; Li, Baofeng; Zhai, Feng; Lu, Jiansheng; Xue, Bing; Fu, Shiyuan; Xiao, Chun.
Afiliação
  • Yu J; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: yujiahao@bupt.edu.cn.
  • Gao X; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: xlhhh74@bupt.edu.cn.
  • Li B; China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: libaofeng@epri.sgcc.com.cn.
  • Zhai F; China Electric Power Research Institute Company Limited, Beijing, 100192, China; School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China. Electronic address: zhaifeng@epri.sgcc.com.cn.
  • Lu J; State Grid Shanxi Marketing Service Center, Taiyuan, 030032, China. Electronic address: lujiansheng@sx.sgcc.com.cn.
  • Xue B; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: xuebing@bupt.edu.cn.
  • Fu S; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: ShiyuanFu@bupt.edu.cn.
  • Xiao C; State Grid Shanxi Marketing Service Center, Taiyuan, 030032, China. Electronic address: tyutxiaochun@163.com.
Neural Netw ; 170: 478-493, 2024 Feb.
Article em En | MEDLINE | ID: mdl-38039685
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizagem Idioma: En Revista: Neural Netw Assunto da revista: NEUROLOGIA Ano de publicação: 2024 Tipo de documento: Article País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizagem Idioma: En Revista: Neural Netw Assunto da revista: NEUROLOGIA Ano de publicação: 2024 Tipo de documento: Article País de publicação: Estados Unidos