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Erschienen in: Engineering with Computers 5/2023

Open Access 30.08.2022 | Correction

Correction to: A dynamic soft sensor based on hybrid neural networks to improve early off-spec detection

verfasst von: Seokyoung Hong, Nahyeon An, Hyungtae Cho, Jongkoo Lim, In-Su Han, Il Moon, Junghwan Kim

Erschienen in: Engineering with Computers | Ausgabe 5/2023

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The original article can be found online at https://​doi.​org/​10.​1007/​s00366-022-01694-7.

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1 Correction to: Engineering with Computers https://​doi.​org/​10.​1007/​s00366-022-01694-7

In the original publication of the article, the title of the article was incorrectly. The correct title is “A dynamic soft sensor based on hybrid neural networks to improve early off-spec detection”.
The original article has been corrected.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Titel
Correction to: A dynamic soft sensor based on hybrid neural networks to improve early off-spec detection
verfasst von
Seokyoung Hong
Nahyeon An
Hyungtae Cho
Jongkoo Lim
In-Su Han
Il Moon
Junghwan Kim
Publikationsdatum
30.08.2022
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 5/2023
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-022-01731-5

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