The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.
With the advent of 3D scanner, accurate segmentation of 3D fruit shape from unorganized point clouds has been turned out to be the most challenging task in scientists and engineers in reverse engineering. This paper herein proposes efficient and robust approach to extract pear shape from background. At first, an interactive, non-local denoising algorithm is employed to efficient denoise the pear scans; Second, geometric properties, including normal, variation and curvature, are estimated by covariance analysis; Third, a Recursive Region Increment (RRI) is proposed to add the geometric similarity points to a base set, to generate an ultimate set only including the points of pear shape; Forth, point clouds is linearized for rapidly rending in the post processing. Finally, segmentation algorithm applied on ten range scans of a pear demonstrates that our algorithm reduces the number of pear point clouds by 88.3%, proves the validity and practicability of this method in pear segmentation
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.