Forecasting and analyzing technology development trends with self-attention and frequency enhanced LSTM DOI

Zhi-Xing Chang,

Wei Guo, Lei Wang

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 64, P. 103093 - 103093

Published: Dec. 31, 2024

Language: Английский

The Role of Utilizing Artificial Intelligence and Renewable Energy in Reaching Sustainable Development Goals DOI
Fatma M. Talaat, A.E. Kabeel,

Warda M. Shaban

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: 235, P. 121311 - 121311

Published: Sept. 7, 2024

Language: Английский

Citations

11
The Abnormal Diagnosis Method for Process Parameter Fluctuation Based on Power Spectral Density and Statistical Characteristics DOI Creative Commons
Zhu Wang, Jiale Zhan, Qinghe Zheng

et al.

IET Signal Processing, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

In processes of refining and chemical productions, alarm systems are generally centralized management for process parameters. However, in order to address the challenges advanced manipulation maintenance during emergencies, there has been limited research on timely alarming individual critical This paper proposes a method based combination power spectral density statistical characteristics, which can quickly accurately diagnose large‐scale trend changes short‐term nonstationary abnormal trends First, employs incremental data from historical records parameters volatility analysis. Second, segmented into multiple appropriately sized datasets. We employ combined analysis characteristics extract features multitude data. Meanwhile, we have designed tuning scheme frequencies their threshold parameters, be used testing online diagnostics. Experimental validation is performed using actual Chinese refineries. The experimental results indicate that detect demonstrating good diagnostic performance.

Language: Английский

Citations

0
Enhanced accuracy and interpretability of nitrous oxide emission prediction of wastewater treatment plants through machine learning of univariate time series: A novel approach of learning feature reconstruction DOI
Zixuan Wang, Anlei Wei, K.S. Tang

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 71, P. 107263 - 107263

Published: Feb. 15, 2025

Language: Английский

Citations

0
Forecasting and analyzing technology development trends with self-attention and frequency enhanced LSTM DOI

Zhi-Xing Chang,

Wei Guo, Lei Wang

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 64, P. 103093 - 103093

Published: Dec. 31, 2024

Language: Английский

Citations

0