Abstract: The present invention relates to the technical field of new catalytic materials, and specifically relates to a phosphorus-doped nickel-aluminum oxide, its preparation method and the application thereof. Said preparation method comprises: the nickel-aluminum-based layered double hydroxide is subjected to high-temperature aerobic calcination to obtain nickel-aluminum oxide, the nickel-aluminum oxide obtained thereby is mixed with a phosphorus source and heated in an inert gas atmosphere or in vacuum conditions to dope phosphorus into the nickel-aluminum oxide, whereby the final phosphorus-doped nickel-aluminum oxide is obtained. In the present invention the Ni—Al interactions are constructed by subjecting the nickel-aluminum based layered double hydroxides to aerobic calcination at high temperature. The Ni—P interactions are constructed by doping P into nickel-aluminum oxides.

Abstract: Clean Version of the Abstract A defective picture generation method applied to industrial quality inspection and a defective picture generation apparatus applied to industrial quality inspection are provided.

Abstract: A defective picture generation method applied to industrial quality inspection and a defective picture generation apparatus applied to industrial quality inspection are provided. The method includes: acquiring a first workpiece picture set with defects and a second workpiece picture set without defects; determining a defect annotation picture corresponding to each first workpiece picture in the first workpiece picture set; determining a feature value of each second workpiece picture in the second workpiece picture set; training a pix2pixHD network based on the first workpiece pictures, the second workpiece pictures, the defect annotation pictures and the feature values; acquiring a target defect annotation picture from the defect feature database according to the desired defect type; acquiring a target feature value from the picture feature database according to the desired picture type; and inputting the target defect annotation picture and the target feature value into the trained generator.