2022
DOI: 10.1016/j.ymssp.2022.109243
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Self-feature-based point cloud registration method with a novel convolutional Siamese point net for optical measurement of blade profile

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Cited by 46 publications
(19 citation statements)
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“…The complex feature of the hypersonic flow nearby the nose cone is important challenge for the evaluation of the thermal efficiency of these proposed techniques 3 , 4 . Besides, the production of the shock with air dissociation also intensifies the complexity of the flow physic in the vicinity of the nose cone 5 .…”
Section: Introductionmentioning
confidence: 99%
“…The complex feature of the hypersonic flow nearby the nose cone is important challenge for the evaluation of the thermal efficiency of these proposed techniques 3 , 4 . Besides, the production of the shock with air dissociation also intensifies the complexity of the flow physic in the vicinity of the nose cone 5 .…”
Section: Introductionmentioning
confidence: 99%
“…A grid-independent analysis was performed for the blade geometry, using simulation results that were adequately grid-independent [ 40 ]. The tetrahedral meshing of the blade was generated and is shown in Figure 3 .…”
Section: Methodsmentioning
confidence: 99%
“…A projection matrix computed from training data is employed to map the finger-vein images into subspace, and the resulting features are further used for recognition. The typical methods include principal component analysis (PCA) (Wu and Liu, 2011a ), two dimensional principal component analysis (2DPCA) (Qiu et al, 2016 ), two-directional and two-dimensional principal component analysis ((2D)2PCA) (Yang et al, 2012 ; Li et al, 2017 ; Zhang et al, 2021 ; Ban et al, 2022 ; She et al, 2022 ), linear discriminant analysis (LDA) (Wu and Liu, 2011b ), high-dimensional state space (Zhang et al, 2022 ), self-feature-based method (Xie et al, 2022 ), and latent factor model (Wu et al, 2022 ).…”
Section: Introductionmentioning
confidence: 99%