| Autor: |
GUAN Xue-mei, WU Yan, YANG Qu-san |
| Předmět: |
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| Zdroj: |
China Forest Products Industry; Jan2024, Vol. 61 Issue 1, p1-7, 7p |
| Abstrakt: |
This study focused on enhancing the accuracy and efficiency of color detection in stained wood. Specifically, the research investigated the dyeing process of Chinese pine wood veneers. The spectral reflectance of the stained veneers was extracted and used as input data. The Extreme Learning Machine (ELM) model was employed as the predictive model to estimate the L*, a*, b* color parameters of the stained veneers. The ELM model was optimized using the Particle Swarm Optimization (PSO) algorithm, which addressed the issue of local optima by introducing non-linear inertia weights and a novel position-velocity update strategy. Evaluation of the models was based on the L*, a*, b* average absolute error metric. The results indicated that the optimized model achieved an average absolute error of 0.16, outperforming the baseline ELM model (0.68) and the ELM model optimized with the sparrow algorithm (0.37). This research contributed to improving the efficiency of wood staining production and holds implications for the field of color detection in wood materials. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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