Since the proposal of bonnet polishing technology, it has been widely used in the surface polishing of various high-precision curved components. Because of its unique polishing tools and polishing method, it has the characteristics of high polishing efficiency and high polishing accuracy. Therefore, bonnet polishing technology is particularly important in engineering applications. We mainly review the research progress of bonnet polishing technology in the past 10 years, including the introduction of bonnet polishing technology applied to important engineering projects such as the European Extremely Large Telescope, the main mirror of Japan’s next-generation space telescope, the Shenguang-III mainframe unit, and the artificial skeleton. We summarize the key technologies of bonnet polishing technology such as removal mechanism, removal function, motion control, process control, and software development according to the typical applications of bonnet polishing technology. Finally, the current limitations are summarized according to the characteristics of bonnet polishing technology, and the development trend of bonnet polishing technology is also foreseen, hoping to provide a reference for the subsequent in-depth research of bonnet polishing technology.
Hyperspectral image has high-dimensional Spectral–spatial features, those features with some noisy and redundant information. Since redundant features can have significant adverse effect on learning performance. So efficient and robust feature selection methods are make the best of labeled and unlabeled points to extract meaningful features and eliminate noisy ones. On the other hand, obtaining sufficient accurate labeled data is either impossible or expensive. In order to take advantage of both precious labeled and unlabeled data points, in this paper, we propose a new semisupervised feature selection method, Firstly, we use labeled points are to enlarge the margin between data points from different classes; Secondly, we use unlabeled points to find the local structure of the data space; Finally, we compare our proposed algorithm with Fisher score, PCA and Laplacian score on HSI classification. Experimental results on benchmark hyperspectral data sets demonstrate the efficiency and effectiveness of our proposed algorithm.
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