TY - CHAP
T1 - Machine Learning-Based Abnormal Event Detection and Classification
AU - Smidts, Carol
AU - Ray, Indrajit
AU - Zhu, Quanyan
AU - Vaddi, Pavan Kumar
AU - Zhao, Yunfei
AU - Huang, Linan
AU - Diao, Xiaoxu
AU - Talukdar, Rakibul
AU - Pietrykowski, Michael C.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The utilization of digital networks has improved the ease of monitoring and control and significantly optimized the operations in industrial control systems (ICSs). With the rapid growth of artificial intelligence techniques and hardware in recent years, industries are moving toward nearly or fully autonomous operation, i.e., industry 4.0, where smart machines with improved communication, control, and monitoring capabilities are introduced into ICSs. Recently, nuclear industry, where safety is of utmost importance, is migrating toward digitalization as well.
AB - The utilization of digital networks has improved the ease of monitoring and control and significantly optimized the operations in industrial control systems (ICSs). With the rapid growth of artificial intelligence techniques and hardware in recent years, industries are moving toward nearly or fully autonomous operation, i.e., industry 4.0, where smart machines with improved communication, control, and monitoring capabilities are introduced into ICSs. Recently, nuclear industry, where safety is of utmost importance, is migrating toward digitalization as well.
UR - http://www.scopus.com/inward/record.url?scp=85140207505&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140207505&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-12711-3_3
DO - 10.1007/978-3-031-12711-3_3
M3 - Chapter
AN - SCOPUS:85140207505
T3 - SpringerBriefs in Computer Science
SP - 29
EP - 54
BT - SpringerBriefs in Computer Science
PB - Springer
ER -