Guest Editorial Special Issue on Learning From Imperfect Data for Industrial Automation

Ping Zhou, Xuewu Dai, Kyriakos G. Vamvoudakis, Jan Faigl, Hong Wang

Abstract: With the rapid development of advanced sensing, communication, and the industrial Internet of Things, it has become much easier to obtain, transmit, and, store a massive amount of real-world data. However, imperfect data is inevitable in real-world systems, such as the existence of outliers, contaminated, incomplete, inaccurate, and even missing information in the data. This phenomenon is called data imperfection, which usually makes traditional datadriven modeling and automation methods either unfeasible or ending at undesired inaccuracies. This has been a wellknown challenge to data-driven methods when applied to real-world systems, such as process industry, manufacturing, energy networks, and transportation systems.