Journal of Applied Science and Engineering

Published by Tamkang University Press

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Hybrid Self-Attention with Lightweight Gating for ChineseEnglish Neural Machine Translation

 2026

 Volume 30

Zhibin Xu1, Chuanye He2, Hailin Chen1, and Meng Lei3

1China Certification & Inspection Group Hebei Co., Ltd., Shijiazhuang 050071, Hebei, China

2School of Software, Dalian University of Technology, Dalian 116620, Liaoning, China

3School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China

Received: November 11, 2025
Accepted: November 24, 2025
Publication Date: March 8, 2026

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Comparison of model-predicted Mad values with true values across the sample sequence (sorted by true labels)

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Near-Infrared Spectroscopy (NIRS) has become one of the most widely used non-destructive techniques for rapid coal quality assessment due to its efficiency, low cost, and suitability for on-site industrial deployment. However, NIRS data collected under different measurement conditions often exhibit significant spectral distribution shifts, which substantially weaken the cross-domain generalization ability of conventional prediction models. To address this challenge, this paper proposes a multi-source spectral learning method tailored for NIRS-based coal analysis. The method integrates a unified multi-source domain adaptation framework, a dual-regression-head architecture, and a performance-driven dynamic weighting strategy, enabling effective cross-domain learning without requiring target-domain data. Experiments conducted on real coal NIRS datasets demonstrate that the proposed approach consistently outperforms existing baselines across multiple measurement scenarios, highlighting its strong potential for accurate and robust rapid coal quality detection.

Keywords: Coal quality assessment; multi-source domain adaptation; cross-domain generalization; transfer learning

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