Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM
To enable quick and accurate detection of formaldehyde emissions from wood-based panels and to address the complexities of the full-scale chamber method and the inaccuracy of sensor-based methods, a modified formaldehyde emission model based on AdaBoost-MSBKA-SVMD-HKELM (AMSHKELM) is proposed. This...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10663759/ |
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| author | Yinuo Wang Huanqi Zheng Hua Wang Yucheng Zhou |
| author_facet | Yinuo Wang Huanqi Zheng Hua Wang Yucheng Zhou |
| author_sort | Yinuo Wang |
| collection | DOAJ |
| description | To enable quick and accurate detection of formaldehyde emissions from wood-based panels and to address the complexities of the full-scale chamber method and the inaccuracy of sensor-based methods, a modified formaldehyde emission model based on AdaBoost-MSBKA-SVMD-HKELM (AMSHKELM) is proposed. This model utilizes sensor data, which provides ease of use and rapid detection, as input and formaldehyde emission data measured by the full-scale chamber method as output. Initially, multiple strategies are employed to address the inherent limitations of the black-winged kite algorithm, which struggles to effectively find optimal solutions and is susceptible to getting trapped in local optima. The multi-strategy improved black-winged kite algorithm then optimizes key parameters of the successive variational mode decomposition (SVMD) and hybrid kernel extreme learning machine (HKELM). In addition, adaptive boosting is introduced to further improve the accuracy and robustness of the model. Then, the AMSHKELM formaldehyde emission modified deviation model is constructed to fit the decomposed subsequence. Moreover, an adaptive bandwidth kernel density estimation combined with the AMSHKELM is developed to construct an interval prediction model. Experimental results indicate that the AMSHKELM model achieves the coefficient of determination of up to 0.9767 and the root mean square error of 2.7141e-03, demonstrating higher fitting accuracy and stronger robustness compared to various other models. Additionally, the interval prediction model performs superiorly. This model can combine interval prediction information to effectively and comprehensively assess the pass rate of test samples, providing a fast and reliable solution for formaldehyde emission testing of wood-based panels. |
| format | Article |
| id | doaj-art-5665ac1e62114363a321800b5b45cd52 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-5665ac1e62114363a321800b5b45cd522025-01-10T00:00:48ZengIEEEIEEE Access2169-35362025-01-01135112512810.1109/ACCESS.2024.345397010663759Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELMYinuo Wang0Huanqi Zheng1https://orcid.org/0000-0002-5418-3987Hua Wang2https://orcid.org/0009-0008-7277-8790Yucheng Zhou3https://orcid.org/0009-0000-9905-6335School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, Shandong, ChinaSchool of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, Shandong, ChinaNational Center of Quality Inspection and Test for Decoration Materials, Jinan, ChinaSchool of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, Shandong, ChinaTo enable quick and accurate detection of formaldehyde emissions from wood-based panels and to address the complexities of the full-scale chamber method and the inaccuracy of sensor-based methods, a modified formaldehyde emission model based on AdaBoost-MSBKA-SVMD-HKELM (AMSHKELM) is proposed. This model utilizes sensor data, which provides ease of use and rapid detection, as input and formaldehyde emission data measured by the full-scale chamber method as output. Initially, multiple strategies are employed to address the inherent limitations of the black-winged kite algorithm, which struggles to effectively find optimal solutions and is susceptible to getting trapped in local optima. The multi-strategy improved black-winged kite algorithm then optimizes key parameters of the successive variational mode decomposition (SVMD) and hybrid kernel extreme learning machine (HKELM). In addition, adaptive boosting is introduced to further improve the accuracy and robustness of the model. Then, the AMSHKELM formaldehyde emission modified deviation model is constructed to fit the decomposed subsequence. Moreover, an adaptive bandwidth kernel density estimation combined with the AMSHKELM is developed to construct an interval prediction model. Experimental results indicate that the AMSHKELM model achieves the coefficient of determination of up to 0.9767 and the root mean square error of 2.7141e-03, demonstrating higher fitting accuracy and stronger robustness compared to various other models. Additionally, the interval prediction model performs superiorly. This model can combine interval prediction information to effectively and comprehensively assess the pass rate of test samples, providing a fast and reliable solution for formaldehyde emission testing of wood-based panels.https://ieeexplore.ieee.org/document/10663759/Formaldehyde emission detectionfull-scale chambermulti-strategy black-winged kite algorithmsuccessive variational mode decompositionadaptive boostinginterval prediction |
| spellingShingle | Yinuo Wang Huanqi Zheng Hua Wang Yucheng Zhou Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM IEEE Access Formaldehyde emission detection full-scale chamber multi-strategy black-winged kite algorithm successive variational mode decomposition adaptive boosting interval prediction |
| title | Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM |
| title_full | Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM |
| title_fullStr | Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM |
| title_full_unstemmed | Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM |
| title_short | Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM |
| title_sort | research on the rapid detection of formaldehyde emission from wood based panels based on the amshkelm |
| topic | Formaldehyde emission detection full-scale chamber multi-strategy black-winged kite algorithm successive variational mode decomposition adaptive boosting interval prediction |
| url | https://ieeexplore.ieee.org/document/10663759/ |
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