Complex Singular Value Decomposition Based Noise Reduction of Dynamic PET Images

2011 ◽  
Vol 7 (2) ◽  
pp. 113-117 ◽  
Author(s):  
David S. Wack ◽  
Rajendra D. Badgaiyan
Keyword(s):  
Singular Value Decomposition ◽  
Noise Reduction ◽  
Singular Value ◽  
Dynamic Pet ◽  
Value Decomposition

scholarly journals Intelligent alarm data noise reduction for power systems based on singular value decomposition

2021 ◽  
Vol 2108 (1) ◽  
pp. 012074
Author(s):  
Chen Chen ◽  
Hongren Man ◽  
Xiu Liu
Keyword(s):  
Data Structure ◽  
Singular Value Decomposition ◽  
Power System ◽  
Noise Reduction ◽  
Reduction Method ◽  
Singular Value ◽  
Data Noise ◽  
Noise Data ◽  
Value Decomposition ◽  
Noise Statistics

Abstract The noise types of power system intelligent alarm data are complex. When reducing the intelligent alarm data, the profile noise statistics of the noise data are large, resulting in the actual noise reduction value is too small. To solve this problem, a power system intelligent alarm data noise reduction method based on singular value decomposition is designed. The selected normalized decomposition matrix iteratively processes the original matrix, the singular value decomposes the power system alarm data, sets an estimation quantity within the paradigm of the alarm data, controls the noise profile noise statistics, characterizes the noise alarm data structure, uses the SC algorithm to process the cluster basis vectors in the noise data structure, and constructs a repeated iterative convergence process to realize intelligent data noise reduction processing. The original alarm data within a known power system is used as test data, the power system alarm window is set, and the power system alarm data singular values are circled. The data mining-based alarm data noise reduction method, the regularized filter-based alarm data noise reduction method and the designed data noise reduction method are applied to the noise reduction process, and the results show that the designed data noise reduction method has the largest noise value and the best noise reduction effect.

scholarly journals Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition

2020 ◽  
Vol 10 (4) ◽  
pp. 1409
Author(s):  
Gang Zhang ◽  
Benben Xu ◽  
Kaoshe Zhang ◽  
Jinwang Hou ◽  
Tuo Xie ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Noise Pollution ◽  
Singular Value ◽  
Reduction Model ◽  
Mode Decomposition ◽  
Starting Point ◽  
Reduction Effect ◽  
Value Decomposition

Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

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