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

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

doi:10.1109/tase.2024.3374132

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

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

^*Corresponding author for this work

Research output: Contribution to journal › Editorial › peer-review

Original language	English
Pages (from-to)	1088-1091
Number of pages	4
Journal	IEEE Transactions on Automation Science and Engineering
Volume	21
Issue number	2
DOIs	https://doi.org/10.1109/tase.2024.3374132
Publication status	Published - 1 Apr 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1109/tase.2024.3374132

Cite this

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title = "Guest Editorial Special Issue on Learning from Imperfect Data for Industrial Automation",

author = "Ping Zhou and Xuewu Dai and Vamvoudakis, \{Kyriakos G.\} and Jan Faigl and Hong Wang",

year = "2024",

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doi = "10.1109/tase.2024.3374132",

language = "English",

volume = "21",

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T1 - Guest Editorial Special Issue on Learning from Imperfect Data for Industrial Automation

AU - Zhou, Ping

AU - Dai, Xuewu

AU - Vamvoudakis, Kyriakos G.

AU - Faigl, Jan

AU - Wang, Hong

PY - 2024/4/1

Y1 - 2024/4/1

UR - https://www.scopus.com/pages/publications/85190854399

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DO - 10.1109/tase.2024.3374132

M3 - Editorial

AN - SCOPUS:85190854399

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VL - 21

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JO - IEEE Transactions on Automation Science and Engineering

JF - IEEE Transactions on Automation Science and Engineering

IS - 2

ER -

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

UN SDGs

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