MENGLAN: Multiscale Enhanced Nonparametric Gas Analyzer with Lightweight Architecture and Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918074477707264 |
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| author | Duan, Zhenke Pan, Jiqun Tu, Jiani |
| author_facet | Duan, Zhenke Pan, Jiqun Tu, Jiani |
| contents | Accurate detection of ethylene concentrations in mixed gases is crucial in chemical production for safety and health purposes. Traditional methods are hindered by high cost and complexity, limiting their practical application. This study proposes MENGLAN, a Multiscale Enhanced Nonparametric Gas Analyzer that integrates a dual-stream structure, a Hybrid Multi-Head Attention mechanism, and a Feature Reactivation Module to enable real-time, lightweight, and high-precision ethylene concentration prediction. Results show that MENGLAN achieves superior performance, reduced computational demand, and enhanced deployability compared to existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22490 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MENGLAN: Multiscale Enhanced Nonparametric Gas Analyzer with Lightweight Architecture and Networks Duan, Zhenke Pan, Jiqun Tu, Jiani Signal Processing Machine Learning Accurate detection of ethylene concentrations in mixed gases is crucial in chemical production for safety and health purposes. Traditional methods are hindered by high cost and complexity, limiting their practical application. This study proposes MENGLAN, a Multiscale Enhanced Nonparametric Gas Analyzer that integrates a dual-stream structure, a Hybrid Multi-Head Attention mechanism, and a Feature Reactivation Module to enable real-time, lightweight, and high-precision ethylene concentration prediction. Results show that MENGLAN achieves superior performance, reduced computational demand, and enhanced deployability compared to existing methods. |
| title | MENGLAN: Multiscale Enhanced Nonparametric Gas Analyzer with Lightweight Architecture and Networks |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2506.22490 |