A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal.

Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifacts can seriously affect the practical application effectiveness of ICT in defect detection and dimensional measurement. In this paper, a series of convolution neural network models are designed and implemented based on preparing the ICT image artifact removal datasets. Our findings indicate that the RF (receptive field) and the spatial resolution of network can significantly impact the effectiveness of artifact removal. Therefore, we propose a dilated residual network for turbine blade ICT image artifact removal (DRAR), which enhances the RF of the network while maintaining spatial resolution with only a slight increase in computational load. Extensive experiments demonstrate that the DRAR achieves exceptional performance in artifact removal.

[Determination of complanatoside A in semen Astragali complanati by HPLC].

OBJECTIVE: To establish the determination method for complanatoside A in seeds of Astragalus complanatus. METHOD: An HPLC method has been developed to separate complanatoside A on ZORBAX EXTEND-C18 (4.6 mm x 250 mm, 5 microm) column with acetonitrile-water-phosphoric acid (20:80:0.2) as mobile phase and UV detection at 267 nm. RESULT: The good linearity of complanatoside A ranged 0.086-0.430 microg, r = 0.9999. An average recovery of 99.8% (n = 5) was obtained with a RSD of 1.0%. CONCLUSION: The established method is proved to be stability, fast, accurate and can be used for quantification of Complanatoside A in Semen Astragali Complanati.