Model updating strategy study about sex identification of silkworm pupae using transfer learning and NIR spectroscopy.

This paper proposes a novel model updating strategy named SilkwormNet for the first time to address the sex discrimination problem of silkworm pupae with new species. SilkwormNet integrates a ResNet block, a multi-head attention mechanism, and a Schedule-Free optimization strategy. Initially, the preprocessed spectra from one species were input into SilkwormNet to establish an optimal primary model.

Low-illumination and noisy bridge crack image restoration by deep CNN denoiser and normalized flow module.

When applying deep learning and image processing techniques for bridge crack detection, the obtained images in real-world scenarios have severe image degradation problem. This study focuses on restoring low-illumination bridge crack images corrupted by noise to improve the accuracy of subsequent crack detection and semantic segmentation. The proposed algorithm consists of a deep CNN denoiser and a normalized flow-based brightness enhancement module.

Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis.

Background: Most studies only focus on the sex discrimination of silkworm pupae. However, species differentiation of silkworm pupae is also needed in sericulture. To classify the sex and species at the same time, the present study adopts near infrared (NIR) spectroscopy combined with multivariate analysis.