Utilizing SVD and VMD for Denoising Non-Stationary Signals of Roller Bearings
Sensors, 2021 In view of the fact that vibration signals of rolling bearings are much contaminated by noise in the early failure period, this paper presents a new denoising SVD-VMD method by combining singular value decomposition (SVD) and variational mode ...
Qinghua Wang +4 more
doaj +1 more source
A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD
Shock and Vibration, 2018 A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). Firstly, the collected
Chenguang Huang +3 more
doaj +1 more source
An Improved Unscented Kalman Filter Algorithm for Radar Azimuth Mutation
International Journal of Aerospace Engineering, 2020 An improved UKF (Unscented Kalman Filter) algorithm is proposed to solve the problem of radar azimuth mutation. Since the radar azimuth angle will restart to count after each revolution of the radar, and when the aircraft just passes the abrupt angle ...
Dazhang You +5 more
doaj +1 more source
Singular Value Decomposition (SVD) Berdasarkan Intensitas Pencahayaannya Untuk Pengenal Wajah [PDF]
Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer, 2021 Singular Value Decomposition (SVD) is a method that can be used for biometric systems, where the biometric system is a system for identification by using physical features or human limbs such as fingerprints, eye retina, face, and others. This writing aims to recognize faces based on the intensity of their lighting using SVD. The use of SVD is done
Samsurizal Samsurizal +2 more
openaire +1 more source
A Constructive Algorithm for Decomposing a Tensor into a Finite Sum of Orthonormal Rank-1 Terms [PDF]
, 2015 We propose a constructive algorithm that decomposes an arbitrary real tensor into a finite sum of orthonormal rank-1 outer products. The algorithm, named TTr1SVD, works by converting the tensor into a tensor-train rank-1 (TTr1) series via the singular ...
Batselier, Kim, Liu, Haotian, Wong, Ngai
core +3 more sources
Harmonic detection algorithm based on improved SVD and Prony
Dianzi Jishu Yingyong, 2019 An improved algorithm based on singular value decomposition(SVD) and Prony is proposed to solve the problem that the traditional Prony algorithm is not very accurate because it is sensitive to noise during harmonic and inter-harmonic detection.
Ying Jun, Zhu Yunpeng, He Chao
doaj +1 more source
Reducing Memory Cost of Exact Diagonalization using Singular Value Decomposition [PDF]
, 2011 We present a modified Lanczos algorithm to diagonalize lattice Hamiltonians with dramatically reduced memory requirements, {\em without restricting to variational ansatzes}. The lattice of size $N$ is partitioned into two subclusters.
/SLAC +5 more
core +2 more sources
The Hessian biased singular value decomposition method for optimization and analysis of force fields [PDF]
, 1996 We present methodology (HBFF/SVD) for optimizing the form and parameters of force fields (FF) for molecular dynamics simulations through utilizing information about properties such as the geometry, Hessian, polarizability, stress (crystals), and elastic ...
Dasgupta, Siddharth +2 more
core +1 more source
DEKOMPOSISI NILAI SINGULAR PADA SISTEM PENGENALAN WAJAH
Jurnal Matematika, 2012 Dekomposisi Nilai Singular atau Singular Value Decomposition (SVD) merupakan salah satu cara untuk menyatakan Principal Component Analysis (PCA).
Beni Utomo
doaj +1 more source
Fast multilinear Singular Values Decomposition for higher-order Hankel tensors [PDF]
, 2014 International audienceThe Higher-Order Singular Value Decomposition (HOSVD) is a possible generalization of the Singular Value Decomposition (SVD) to tensors, which have been successfully applied in various domains.
Boizard, Maxime +3 more
core +3 more sources