scholarly journals An Optimization Method for Hyperspectral Endmember Extraction Based on K-SVD

2019 ◽  
Vol 85 (12) ◽  
pp. 879-887
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Ya Zhang ◽  
Yun Tang
Keyword(s):  
Singular Value Decomposition ◽  
Background Noise ◽  
Optimization Method ◽  
Extraction Methods ◽  
Singular Value ◽  
Endmember Extraction ◽  
Partially Observed ◽  
Mixed Pixels ◽  
Ground Object ◽  
Value Decomposition

Mixed pixels are common in hyperspectral imagery (<small>HSI</small>). Due to the complexity of the ground object distribution, some end-member extraction methods cannot obtain good results and the processes are complex. Therefore, this paper proposes an optimization method for <small>HSI</small> endmember extraction, which improves the accuracy of the results based on K-singular value decomposition (<small>K-SVD</small>). The proposed method comprises three core steps. (1) Based on the contribution value of initial endmembers, partially observed data selected according to the appropriate confidence participate in the calculation. (2) Construction of the error model to eliminate the background noise. (3) Using the <small>K-SVD</small> to perform column-by-column iteration on the endmembers to achieve the overall optimality. Experiments with three real images are applied, demonstrating the proposed method can improve the overall endmember accuracy by 15.1%–55.7% compared with the original methods.

Modeling Research of Background Noise in Low-Voltage Power Line Communication Channel

2012 ◽  
Vol 516-517 ◽  
pp. 1386-1390 ◽  
Author(s):  
Hao Kun Guo ◽  
Jun Ji Wu ◽  
Zhan Feng Ying
Keyword(s):  
Singular Value Decomposition ◽  
Background Noise ◽  
Low Voltage ◽  
Power Line ◽  
Singular Value ◽  
Ar Model ◽  
Recursive Method ◽  
Noise Interference ◽  
Set Up ◽  
Value Decomposition

Background noise interference is one of the most important factors for low-voltage power line communication’s reliability. By analyzing the background noise of low-voltage power line communication’s channel, the background noise’s measuring circuit is set up and the AR model of the measured background noise is established. Both of them are respectively using singular value decomposition and Levinson-Durbin (LD) recursive method to calculate the AR model’s parameters and a comparative analysis of the simulation is made. The results induct: parameters acquired from the methods of singular value decomposition and LD recursive method are feasible, the parameter model from singular value decomposition is relatively complex, but extremely accurate, which is suitable for the off-line calculation and analysis of the low-voltage power line’s background noise; the parameter model from LD recursive method is very simple, but has a greater loss of accuracy, fitting for online quickly generation of the low-voltage power line’s background noise.

Forearm EMG Signal Classification Based on Singular Value Decomposition and Wavelet Packet Transform Features

2012 ◽  
Vol 433-440 ◽  
pp. 912-916 ◽  
Author(s):  
Mohammad Karimi
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Wrist Flexion ◽  
Emg Signal ◽  
Value Decomposition ◽  
Svd Method

The myoelectric signal (MES) with broad applications in various areas especially in prosthetics and myoelectric control, is one of the biosignals utilized in helping humans to control equipments. In this paper, a technique for feature extraction of forearm electromyographic (EMG) signals using wavelet packet transform (WPT) and singular value decomposition (SVD) is proposed. In the first step, the WPT is employed to generate a wavelet decomposition tree from which features are extracted. In the second step, an algorithm based on singular value decomposition (SVD) method is introduced to compute the feature vectors for every hand motion. This technique can successfully identify eight hand motions including forearm pronation, forearm supination, wrist flexion, wrist abduction, wrist adduction, chuck grip, spread fingers and rest state. These motions can be obtained by measuring the surface EMG signal through sixteen electrodes mounted on the pronator and supinator teres, flexor digitorum, sublimas, extensor digitorum communis, and flexor and extensor carpi ulnaris. Moreover, through quantitative comparison with other feature extraction methods like entropy concept in this paper, SVD method has a better performance. The results showed that proposed technique can achieve a classification recognition accuracy of over 96% for the eight hand motions.

scholarly journals THE SUPPORT VECTOR MACHINE PARAMETER OPTIMIZATION METHOD BASED ON ADAPTIVE ELITIST DIFFERENTIAL EVOLUTION ALGORITHM AND ITS APPLICATION TO ROLLER BEARING FAULT DIAGNOSIS

2022 ◽  
Vol 36 (06) ◽  
Author(s):  
HUNGLINH AO ◽  
THANHHANG NGUYEN ◽  
V.HO HUU ◽  
TRANGTHAO NGUYEN
Keyword(s):  
Singular Value Decomposition ◽  
Differential Evolution ◽  
Parameter Optimization ◽  
Roller Bearing ◽  
Optimization Method ◽  
Singular Value ◽  
Superior Performance ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Value Decomposition

SVM parameters have serious effects on the accuracy rate of classification result. Tuning SVM parameters is always a challenge for scientists. In this paper, a SVM parameter optimization method based on Adaptive Elitist Differential Evolution (AeDE-SVM) is proposed. Furthermore, AeDE-SVM is applied to diagnose roller bearing fault by using complementary ensemble empirical mode decomposition (CEEMD) and singular value decomposition (SVD) techniques. First, original acceleration vibration signals are decomposed into Intrinsic Mode Function (IMFs) by using CEEMD method. Second, initial feature matrices are extracted from (IMFs) by singular value decomposition (SVD) techniques to obtain single values. Third, these values serve as input vector for AeDE-SVM classifier. The results show that the combination of AeDE-SVM classifiers and the CEEMD-SVD method obtains higher classification accuracy and lower cost time compared to other methods. In this paper, the roller bearing vibration signals were used to evaluate the proposed method. The experimental results showed that the superior performance compared to other SVM parameter optimization techniques and successfully recognized different fault types of roller bearing during its operation.

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