Research Article
Research on the Performance of Text Mining and Processing in Power Grid Networks
@ARTICLE{10.4108/eetsis.v10i4.3094, author={Yuzhong Zhou and Zhengping Lin and Liang Tu and Jiahao Shi and Yuliang Yang}, title={Research on the Performance of Text Mining and Processing in Power Grid Networks}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={5}, publisher={EAI}, journal_a={SIS}, year={2023}, month={6}, keywords={Text mining, performance analysis, deep learning}, doi={10.4108/eetsis.v10i4.3094} }- Yuzhong Zhou
Zhengping Lin
Liang Tu
Jiahao Shi
Yuliang Yang
Year: 2023
Research on the Performance of Text Mining and Processing in Power Grid Networks
SIS
EAI
DOI: 10.4108/eetsis.v10i4.3094
Abstract
This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.
Copyright © 2023 Yuzhong Zhou et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
