A Feature Optimized Attention Transformer with Kinetic Information Capture and Weighted Robust Z-score for Industrial NOx Emission Forecasting DOI
Jian Long, Siyu Jiang, Luyao Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136276 - 136276

Published: April 1, 2025

Language: Английский

Localized global models using autoencoder-based clustering to forecast related time series DOI
Hossein Abbasimehr, Ali Noshad

International Journal of Data Science and Analytics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Language: Английский

Citations

1
A novel data-driven model for explainable hog price forecasting DOI
Binrong Wu,

Huanze Zeng,

Huanling Hu

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(6)

Published: Feb. 12, 2025

Citations

1
Prediction of Waste Sludge Production in Municipal Wastewater Treatment Plants by Deep-Learning Algorithms with Antioverfitting Strategies DOI
Juanjuan Chen, Weixiang Chao, Yixuan Wang

et al.

ACS ES&T Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Language: Английский

Citations

0
Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies DOI Creative Commons
Mohiuddin Ahmed Bhuiyan, Md. Oliullah Rafi,

Gourab Nicholas Rodrigues

et al.

Array, Journal Year: 2025, Volume and Issue: unknown, P. 100390 - 100390

Published: April 1, 2025

Language: Английский

Citations

0
Active learning regression quality prediction model and grinding mechanism for ceramic bearing grinding processing DOI Creative Commons

Longfei Gao,

Yuhou Wu, Jian Sun

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0320494 - e0320494

Published: April 7, 2025

The study aims to explore quality prediction in ceramic bearing grinding processing, with particular focus on the effect of parameters surface roughness. uses active learning regression model for construction and optimization, empirical analysis under different conditions. At same time, various deep models are utilized conduct experiments processing. experimental setup covers a variety parameters, including wheel linear speed, depth feed rate, ensure accuracy reliability According results, when increases 21 μm, average training loss further decreases 0.03622, roughness Ra value significantly 0.1624 μm. In addition, experiment also found that increasing velocity moderately adjusting can improve machining quality. For example, is 45 m/s 0.015 mm, drops 0.1876 results not only provide theoretical support processing bearings, but basis optimization actual production, which has an important industrial application value.

Language: Английский

Citations

0
A Feature Optimized Attention Transformer with Kinetic Information Capture and Weighted Robust Z-score for Industrial NOx Emission Forecasting DOI
Jian Long, Siyu Jiang, Luyao Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136276 - 136276

Published: April 1, 2025

Language: Английский

Citations

0