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

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.

Download Full-text

Superresolution of FT-NMR Spectra by the Maximum Entropy Method and AR Model Fitting with Singular Value Decomposition

Japanese Journal of Applied Physics ◽

10.1143/jjap.29.212 ◽

1990 ◽

Vol 29 (Part 1, No. 1) ◽

pp. 212-218 ◽

Cited By ~ 3

Author(s):

Takanori Uchiyama ◽

Haruyuki Minamitani ◽

Makoto Sakata

Keyword(s):

Singular Value Decomposition ◽

Maximum Entropy ◽

Nmr Spectra ◽

Maximum Entropy Method ◽

Model Fitting ◽

Entropy Method ◽

Singular Value ◽

Ar Model ◽

Value Decomposition

Download Full-text

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

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.85.12.879 ◽

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 (HSI). 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 HSI endmember extraction, which improves the accuracy of the results based on K-singular value decomposition (K-SVD). 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 K-SVD 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.

Download Full-text

Measurement of Flow Fluctuation in the Flow Standard Facility Based on Singular Value Decomposition

Sensors ◽

10.3390/s21206850 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6850

Author(s):

Tao Meng ◽

Huanchang Wei ◽

Feng Gao ◽

Huichao Shi

Keyword(s):

Singular Value Decomposition ◽

Measuring Method ◽

Flow Stability ◽

Singular Value ◽

Single Frequency ◽

Standard Facility ◽

Flow Fluctuation ◽

Set Up ◽

Value Decomposition ◽

The Impact

In order to accurately evaluate the flow stability of the flow standard facility, the flow fluctuation in the standard facility needs to be accurately measured. However, the flow fluctuation signal is always superimposed with the fluctuation signal of the measuring flowmeter or measurement system (mainly noise), which leads to inaccurate measurement of the flow fluctuation and even an unreliable evaluation result of the flow stability. In addition, when there are multiple fluctuation sources, flow fluctuations with different frequencies are superimposed together, which is extremely unfavorable for evaluating the impact of flow fluctuation with different single frequencies. In this paper, a new measuring method was proposed to obtain the fluctuation signal and the flow fluctuation based on singular value decomposition (SVD). Simulation experiments on the fluctuation signal (single frequency and multiple frequencies) under different levels of noise were conducted, and simulation results showed that the proposed method could accurately obtain the fluctuation signal and the flow fluctuation, even under high noise. Finally, an experimental platform was set-up based on a water flow standard facility and a flow fluctuation generator, and experiments on the output signal of a venturi flowmeter were carried out. The experiment results showed that the proposed method could effectively obtain the fluctuation signal and accurately measure the flow fluctuation.

Download Full-text

Suppression Method of Partial Discharge Interferences Based on Singular Value Decomposition and Improved Empirical Mode Decomposition

Energies ◽

10.3390/en14248579 ◽

2021 ◽

Vol 14 (24) ◽

pp. 8579

Author(s):

Linao Li ◽

Xinlao Wei

Keyword(s):

Singular Value Decomposition ◽

White Noise ◽

Partial Discharge ◽

Singular Value ◽

Narrowband Interference ◽

Noise Interference ◽

Mode Decomposition ◽

Interference Signals ◽

Suppression Method ◽

Value Decomposition

Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interference suppression method based on singular value decomposition (SVD) and improved empirical mode decomposition (IEMD) is proposed in this paper. First, the partial discharge signal with periodic narrowband interference and white noise interference x(t) is decomposed by SVD. According to the distribution characteristics of single values of periodic narrowband interference signals, the singular value corresponding to periodic narrowband interference is set to zero, and the signal is reconstructed to eliminate the periodic narrowband interference in x(t). IEMD is then performed on x(t). Intrinsic mode function (IMF) is obtained by EMD, and based on the improved 3σ criterion, the obtained IMF components are statistically processed and reconstructed to suppress the influence of white noise interference. The methods proposed in this paper, SVD and SVD + EMD, are applied to process the partial discharge simulation signal and partial discharge measurement signal, respectively. We calculated the signal-to-noise ratio, normalized correlation coefficient, and mean square error of the three methods, respectively, and the results show that the proposed method suppresses the periodic narrowband and white noise interference signals in partial discharge more effectively than the other two methods.

Download Full-text