查看更多>>摘要：As an ultra-precise instrument to characterize nano-morphology and structure,the morphology of atomic force mi-croscopy(AFM)tip directly affects the quality of the scanned images,which in turn affects the measurement accuracy.In order to accurately characterize three-dimensional information of AFM tip,a reconstruction method of AFM tip us-ing 2 pm lattice sample is researched.Under normal circumstances,an array of micro-nano structures is used to recon-struct the morphology of AFM tip.Therefore,the 2 pm lattice sample was developed based on semiconductor tech-nology as a characterization tool for tip reconstruction.The experimental results show that the 2 pm lattice sample has good uniformity and consistency,and can be applied to the tip reconstruction method.In addition,the reconstruction method can accurately obtain the morphology of AFM tip,effectively eliminate the influence of the"probe effect"on the measurement results,and improve measurement accuracy.
查看更多>>摘要：With the advantages of simple structure and fast training speed,broad learning system(BLS)has attracted attention in hyperspectral images(HSIs).However,BLS cannot make good use of the discriminative information contained in HSI,which limits the classification performance of BLS.In this paper,we propose a robust discriminative broad learning system(RDBLS).For the HSI classification,RDBLS introduces the total scatter matrix to construct a new loss function to participate in the training of BLS,and at the same time minimizes the feature distance within a class and maximizes the feature distance between classes,so as to improve the discriminative ability of BLS features.RDBLS inherits the advantages of the BLS,and to a certain extent,it solves the problem of insufficient learning in the limited HSI samples.The classification results of RDBLS are verified on three HSI datasets and are superior to other comparison methods.
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