Corrosion protection of mild-steel in 1M HCl using nanocomposite from <i>Triumfetta rhomboidei</i>

Agbaffa, Bamidele E.; Olasehinde, Emmanuel F.; Adebayo, Matthew A.

doi:10.1080/02670836.2023.2210919

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…(4) Environmental protection [40,41]: building wood measurement detection can help reduce wood waste and loss, improve wood utilization efficiency and sustainability, and thus promote environmental protection. (5) Construction insurance [42,43]: measurement detection of construction wood can provide quality and structural information of wood, providing more accurate risk assessment and pricing for construction insurance companies.…”

Section: Application Fields and Future Prospectsmentioning

confidence: 99%

A fast detection method for building wood measurement based on interval type-2 fuzzy model

Wang,

Xu,

Gui

et al. 2023

Meas. Sci. Technol.

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Automatic measurement detection in the process of wood processing can effectively improve the production efficiency and yield of promote the upgrading of wood processing industry. Therefore, this article conducts research on visual detection technology for wood surface measurements and proposes a construction wood measurement detection method based on interval type II fuzzy theory. This method overcomes the problem of increasing heterogeneity and similarity of homogeneous areas on the wood surface, which increases the difficulty of measurement detection. Firstly, the Gaussian model is improved by establishing a wood measurement image feature model that considers rate neighborhood relationships as the basic model. Then, the basic model is fuzzified to construct an interval Type-2 fuzzy model (IT2FM) to characterize the uncertainty features of the modeling and achieve measurement detection of building wood. We conducted experiments using a wood measurement detection dataset to study the influence of the construction form of IT2FM on wood measurement detection, and evaluated the results using kappa values. The detection accuracy of this method is improved by about 10% compared to traditional machine learning and deep learning methods, and it only takes about 3-5 seconds to run the program. The experimental results show that the proposed method can effectively suppress the noise generated by wood measurement detection and prevent missed detection. It can achieve high-quality measurement detection of wood dead knots, loose knots, and cracked parts.

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