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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 1, 2025.
Abstract: This paper addresses the challenges of custom-generating multi-dimensional data SCADA (Supervisory Control And Data Acquisition) reports using web technologies. To improve efficiency, reduce maintenance costs, and enhance scalability, the paper proposes a custom generation method based on the LSO-GAN (Light Spectrum Optimizer - Generative Adversarial Network) model. The study begins by analyzing the requirements for multi-dimensional SCADA reports and proposes a web-based design scheme. The LSO algorithm is employed to optimize the GAN model, enabling efficient generation of customizable SCADA reports. The proposed LSO-GAN model was validated using relevant SCADA data, with experimental results showing that the method outperformed other models in terms of accuracy and generation efficiency. Specifically, the LSO-GAN model achieved an RMSE of 14.98 and a MAPE of 0.93, surpassing traditional models such as Conv-LSTM and FC-LSTM. The custom report generation method based on LSO-GAN significantly improves the customization and generation of multi-dimensional data SCADA reports, demonstrating superior performance in both accuracy and operational efficiency.
Fanxiu Fang, Guocheng Qi, Haijun Cao, He Huang, Lingyi Sun, Jingli Yang, Yan Sui, Yun Liu, Dongqing You and Wenyu Pei, “Optimizing Multi-Dimensional SCADA Report Generation Using LSO-GAN for Web-Based Applications” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160124
@article{Fang2025,
title = {Optimizing Multi-Dimensional SCADA Report Generation Using LSO-GAN for Web-Based Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160124},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160124},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Fanxiu Fang and Guocheng Qi and Haijun Cao and He Huang and Lingyi Sun and Jingli Yang and Yan Sui and Yun Liu and Dongqing You and Wenyu Pei}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.