Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Correction

Correction: Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree

  • Yanning Lu,
  • Yanzheng Xiang,
  • Bo Chen,
  • Haiyang Zhu,
  • Junfeng Yue,
  • Yawei Jin,
  • Pengfei He,
  • Yibo Zhao,
  • Yingjie Zhu,
  • Jiasheng Si,
  • Deyu Zhou

In Fig 1, there is an error in one of the operator symbols on the Long Short-Term Memory (LSTM) neural network. It should have been a multiplication instead of addition. Please see the correct Fig 1 here.

thumbnail
Download:
Fig 1. The architecture of LSTM.

https://doi.org/10.1371/journal.pone.0305366.g001

Moreover, in the LSTM subsection of Preliminary, the Eq 3 is incorrect. The right bracket is missing. Please view the complete, correct equation here.

(3)

Reference

  1. 1. Lu Y, Xiang Y, Chen B, Zhu H, Yue J, Jin Y, et al. (2022) Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree. PLoS ONE 17(10): e0275998. pmid:36301794

Citation: Lu Y, Xiang Y, Chen B, Zhu H, Yue J, Jin Y, et al. (2024) Correction: Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree. PLoS ONE 19(6): e0305366. https://doi.org/10.1371/journal.pone.0305366

Published: June 6, 2024

Copyright: © 2024 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.