Zhihao Qu
Dezhi Tang3
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- 1Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China
- 2Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China
- 3PetroChina Planning and Engineering Institute, Beijing, China
A Corrigendum on
Pitting judgment model based on machine learning and feature optimization methods
by Qu Z, Tang D, Wang Z, Li X, Chen H and Lv Y (2021). Front. Mater. 8:733813. doi: 10.3389/fmats.2021.733813
In the original article, the Funding Statement is missing from page 8.
The correct statement is as follows:
“Funding
This work was supported by National Key R&D Program of China (No. 2020YFB0704501).”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: machine learning, feature engineering, pitting, random forest, pipeline steel
Citation: Qu Z, Tang D, Wang Z, Li X, Chen H and Lv Y (2022) Corrigendum: Pitting judgment model based on machine learning and feature optimization methods. Front. Mater. 9:1029548. doi: 10.3389/fmats.2022.1029548
Received: 27 August 2022; Accepted: 30 August 2022;
Published: 29 September 2022.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2022 Qu, Tang, Wang, Li, Chen and Lv. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhu Wang, d2FuZ3podUB1c3RiLmVkdS5jbg==