Grad-MobileNet: A Gradient-Based Unsupervised Learning Method for Laser Welding Surface Defect Classification.

Deep learning technology has advanced rapidly and has started to be applied for the detection of welding defects. In the manufacturing process of power batteries for new energy vehicles, welding defects may occur due to the high directivity, convergence, and penetration of the laser beam. The accuracy of deep learning prediction relies heavily on big data, but balanced big data of welding defects is hard to acquire at the battery production site. In this paper, the authors construct a dataset named RIAM, which consists of images captured from an industrial environment for laser welding of power battery modules. RIAM contains four types of images: Normality, Lack of fusion, Surface porosity, and Scaled surface. The characteristics of RIAM are carefully considered in the application scenarios. Moreover, this paper proposes a gradient-based unsupervised model named Grad-MobileNet, which can be trained with only a few normal images and can extract the feature gradients of the input images. Welding defects can then be classified by the gradient distribution. This model is based on MobileNetV3, which is a lightweight convolutional neural network (CNN), and achieves 99% accuracy, which is higher than the accuracy expected from supervised learning.

GermlncRNA: a unique catalogue of long non-coding RNAs and associated regulations in male germ cell development.

Spermatogenic failure is a major cause of male infertility, which affects millions of couples worldwide. Recent discovery of long non-coding RNAs (lncRNAs) as critical regulators in normal and disease development provides new clues for delineating the molecular regulation in male germ cell development. However, few functional lncRNAs have been characterized to date. A major limitation in studying lncRNA in male germ cell development is the absence of germ cell-specific lncRNA annotation. Current lncRNA annotations are assembled by transcriptome data from heterogeneous tissue sources; specific germ cell transcript information of various developmental stages is therefore under-represented, which may lead to biased prediction or fail to identity important germ cell-specific lncRNAs. GermlncRNA provides the first comprehensive web-based and open-access lncRNA catalogue for three key male germ cell stages, including type A spermatogonia, pachytene spermatocytes and round spermatids. This information has been developed by integrating male germ transcriptome resources derived from RNA-Seq, tiling microarray and GermSAGE. Characterizations on lncRNA-associated regulatory features, potential coding gene and microRNA targets are also provided. Search results from GermlncRNA can be exported to Galaxy for downstream analysis or downloaded locally. Taken together, GermlncRNA offers a new avenue to better understand the role of lncRNAs and associated targets during spermatogenesis. Database URL: http://germlncrna.cbiit.cuhk.edu.hk/