Gate Recurrent Unit for Efficient Industrial Gas Identification
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917802317709312 |
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| author | Wang, Ding |
| author_facet | Wang, Ding |
| contents | In recent years, gas recognition technology has received considerable attention. Nevertheless, the gas recognition area has faced obstacles in implementing deep learning-based recognition solutions due to the absence of standardized protocols. To tackle this problem, we suggest a new GRU. Compared to other models, GRU obtains a higher identification accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16997 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gate Recurrent Unit for Efficient Industrial Gas Identification Wang, Ding Machine Learning Artificial Intelligence In recent years, gas recognition technology has received considerable attention. Nevertheless, the gas recognition area has faced obstacles in implementing deep learning-based recognition solutions due to the absence of standardized protocols. To tackle this problem, we suggest a new GRU. Compared to other models, GRU obtains a higher identification accuracy. |
| title | Gate Recurrent Unit for Efficient Industrial Gas Identification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.16997 |