A High-Sensitivity and Low-Cost Turbidimeter Based on Metal Capillary Cavity

Li, Xuejing; Zhang, Jianwei; Huang, Hui; Cai, Weicheng; Zhao, Jian; Liu, Pengbo; Wu, Yajun; Liu, Mingchang; Chen, Jing

doi:10.1109/jsen.2023.3327998

IEEE Sensors J.

2023

DOI: 10.1109/jsen.2023.3327998

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A High-Sensitivity and Low-Cost Turbidimeter Based on Metal Capillary Cavity

Xuejing Li,

Jianwei Zhang,

Hui Huang

et al.

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2024

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A new classification algorithm for low concentration slurry based on machine vision

Wang,

Khumalo

et al. 2024

Sci Rep

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Machine vision was utilized in this study to accurately classify the low concentration slurry. Orthogonal experiment L 9 (3 4 ) indicated that the optimal coal slurry collection images were achieved with exposure value of 10, slurry layer thickness of 7 cm, and light intensity of 5 × 10 4 lux. Subsequently, a new low concentration classification model was systematically developed, encompassing aspects such as original image acquisition, data augmentation, dataset partitioning, classification algorithm design, and model evaluation. DCGAN was employed for image generation, achieving favorable outcomes with generator learning rate set at 5 × 10 − 5 , discriminator at 1 × 10 − 6 , and iteration number at 2000. At the point, the maximum SSIM similarity reached 0.9381, and the pHash similarity was 0.9375. Results from subsequent CNN model training, with 200 iterations, the accuracy on training and validation sets was demonstrated over 95% for coal slurry concentration prediction. Further evaluation using recall, precision, and F1-score revealed CNN network model metrics: maximum recall 1.000, minimum 0.800; maximum precision 1.000, minimum 0.700; and highest F1 score 1.000, lowest 0.778. Additionally, the accuracy of this model on the test set reached as high as 94%. The findings indicated the excellent performance in low concentration detection of coal slurry throughout this study.

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A new classification algorithm for low concentration slurry based on machine vision

Wang,

Khumalo

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

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A High-Sensitivity and Low-Cost Turbidimeter Based on Metal Capillary Cavity

Cited by 1 publication

References 43 publications

A new classification algorithm for low concentration slurry based on machine vision

A new classification algorithm for low concentration slurry based on machine vision

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