scholarly journals Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhiwei Zhang ◽  
Hongyuan Gao ◽  
Jingya Ma ◽  
Shihao Wang ◽  
Helin Sun
Keyword(s):  
Objective Function ◽  
Blind Source Separation ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Source Separation ◽  
Hybrid Optimization ◽  
Slime Mould ◽  
Stable Performance

In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.

scholarly journals Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)

2020 ◽  
Vol 10 (11) ◽  
pp. 3735 ◽  
Author(s):  
Feng Miao ◽  
Rongzhen Zhao ◽  
Leilei Jia ◽  
Xianli Wang
Keyword(s):  
Blind Source Separation ◽  
Impulse Noise ◽  
Median Filter ◽  
Source Separation ◽  
Rotating Machinery ◽  
Second Order ◽  
Time Signal ◽  
Blind Identification ◽  
Sensor Signals ◽  
Strong Impulse

Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.

Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method

2020 ◽  
Vol 28 (6) ◽  
pp. 1037-1054
Author(s):  
Temitope E. Komolafe ◽  
Qiang Du ◽  
Yin Zhang ◽  
Zhongyi Wu ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Breast Tissue ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Optimization Method ◽  
Dual Energy ◽  
Hybrid Optimization ◽  
Signal To Noise ◽  
Breast Ct ◽  
Different Levels

BACKGROUND: Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. OBJECTIVE: To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. METHODS: The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. RESULTS: The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. CONCLUSIONS: This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

scholarly journals An Extraction Method of Weak Low-Frequency Magnetic Communication Signals Based on Multisensor

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Chao Huang ◽  
Xin Xu ◽  
Dunge Liu ◽  
Wanhua Zhu ◽  
Xiaojuan Zhang ◽  
...  
Keyword(s):  
Blind Source Separation ◽  
Extraction Method ◽  
Field Experiments ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Low Frequency ◽  
Signal Extraction ◽  
Magnetic Noise ◽  
Communication Signals ◽  
Low Snr

It is a technical challenge to effectively remove the influence of magnetic noise from the vicinity of the receiving sensors on low-frequency magnetic communication. The traditional denoising methods are difficult to extract high-quality original signals under the condition of low SNR (the signal-to-noise ratio). In this paper, we analyze the numerical characteristics of the low-frequency magnetic field and propose the algorithms of the fast optimization of blind source separation (FOBSS) and the frequency-domain correlation extraction (FDCE). FOBSS is based on blind source separation (BSS). Signal extraction of low SNR can be implemented through FOBSS and FDCE. This signal extraction method is verified in multiple field experiments which can remove the magnetic noise by about 25 dB or more.

Modal Identification Using Blind Source Separation Based on Moving Average Method

2013 ◽  
Vol 378 ◽  
pp. 375-381
Author(s):  
Jian Hua Du ◽  
Hong Wu Huang ◽  
Dian Dian Lan
Keyword(s):  
Blind Source Separation ◽  
Average Method ◽  
Moving Average ◽  
Source Separation ◽  
Modal Identification ◽  
Separation Performance ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
The Stability

The paper discusses the basic principles of blind source separation and modal identification of structures, analyses the feasibility that using blind source separation techniques for modal parameter identification. According to the noisy features of the measured data in experiments, a second-order blind identification algorithm based on moving average method is proposed. By moving average method the noises are efficiently eliminated. It greatly improves the separation performance of this algorithm. The cantilever experiments verify the stability and the applicability of the algorithm.

scholarly journals Mixing Matrix Estimation in Blind Source Separation Based on Generalized Gaussian Mixture Modal

2012 ◽  
Vol 4 ◽  
pp. 217-221
Author(s):  
Yong Qiang Chen ◽  
Jun Liu
Keyword(s):  
Objective Function ◽  
Blind Source Separation ◽  
Source Separation ◽  
Gaussian Mixture ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Traditional Methods ◽  
Underdetermined Blind Source Separation ◽  
Matrix Estimation ◽  
Mixing Matrix

The accurate estimation of mixing matrix is critical for blind separation, for solving the problems of traditional methods such as bad robustness and low accuracy, a method based on statistical modal is proposed. The generalized Gaussian mixture modal is used to fit the distribution of single-source-points, a new objective function for clustering is obtained from the view of maximum likelihood estimation. Constrained particle swarm optimization is used to optimize the objective function, by which the mixing matrix is estimated. This method is applicable to determined and underdetermined blind source separation. The simulation shows that the proposed method has higher estimation accuracy and is more robust than traditional methods.

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