In order to organize disfrihuted educafionol re.soiirces effiientlv. to provide acfive 1earner.s an integrated, e-rfendible and cohesive inferface to share the dv17amicallv growing multimedia learning materials on the Internef. tl7is paper proposes a generic resource org.anizafion model with semantic .sfructiire.s fo imprave e.rpressivene.s.s, scalahilih and cohesiveness. We developed an active learning s.vstem wit17 semantic sripporf,for leaf-ners to access and novigate throiigli learning resoiircei in an efjiient and /le.rihle manner. We provide ,facilitie.s for instnrcturs fo manipulate the strncfired educational resoiirces via a convenient visual inferface. We a1.w developed a resource discovering and gathering engine hased on comp1e.r semantic associations fi?r several specific topics.
Having summarized the previous research on ceramic identification and the anti-counterfeiting, the authors propose a ceramic identification system that combines computer vision algorithms with blockchain technology. The system uses irregular pores on microscopic images of ceramic surfaces as image features, and it applies the SIFT(Scale-invariant feature transform) algorithm to extract feature. The images and feature vector sets are then stored by IPFS(Inter-planetary File System). When a consumer needs to authenticate a ceramic product, it is only necessary to take a microscopic image of the specified location, and then the SIFT algorithm will compare the picture with the data stored in the IPFS network, and was previously obtained through the records on a blockchain network, the matching result then determines whether the photographed ceramic is one of those already recorded. Experimental show that the matching results can be used as a strong basis for identifying the origin of ceramic products.
Recent years, the significant advantage has been taken by ceramic industrial from the strong evolution in automation. All production phases have been addressed through various technical innovations, with the exception of the final stage of the manufacturing process. When applying computer vision on ceramic product quality inspection, the different kind of product of ceramic makes it difficult, especially on ceramic bowls, which have a curved surface. It is difficult to analyze rapidness and catch the production rate. This paper proposed an approach for automatic surface imperfection inspection of ceramic products. The bowl's curved surface unfolding model was discussed derived from helicoid unfolding method. Experiment shows that this method can be nicety.
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