&lt;inline-formula&gt;&lt;tex-math&gt;${\pi}$&lt;/tex-math&gt;&lt;/inline-formula&gt;-Phase-Shifted FBG for High-Resolution Static-Strain Measurement Based on Wavelet Threshold Denoising Algorithm

Huang, Wenzhu; Zhang, Wentao; Zhen, Tengkun; Zhang, Fusheng; Li, Fang

doi:10.1109/jlt.2014.2354413

Cited by 18 publications

(3 citation statements)

References 16 publications

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“…In this paper, the Daubechies wavelet function, which is similar to the variation curve of the measured concentration of the gas [37], is selected for data processing. Daubechies wavelets are orthogonal wavelets [38],With asymmetric properties, generally abbreviated as db N. N represents the order of the wavelet, the larger the order, the better the smoothness of the wavelet and the stronger the ability to localize the frequency domain [39,40]. The usual range of N is from 2 to 10, with N=4 chosen in this paper.…”

Section: Optimized Least Squares-wavelet Transform Soft Thresholding ...mentioning

confidence: 99%

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment

Su,

Zhang,

Wang

et al. 2024

Preprint

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In order to improve the analytical accuracy and applicability of infrared gas sensors, this paper examines the need to integrate a pressure compensation algorithm into the infrared gas analyzer, identifying appropriate compensation methods and optimizing test data accuracy. First, a test platform was constructed to evaluate the accuracy of three types of infrared sensors for trace gases in industrial environments. The sensors were tested in the following order: CO2 at concentrations of 0.5%-4.5%; CH4 at concentrations of 2%-18%; and CO at 100-900ppm standard gas concentrations, found that the maximum value of the test error were 48%, 44.5%, 45%, respectively, do not meet the error range specified in the standard. Secondly, the constant-pressure accuracy test was carried out by selecting the same concentration of CO2, CH4 and CO standard gases and substituting them into the optimized least squares compensation model, and it was found that the maximum errors of the three gas tests were 16%, 9% and 24%, which were superior to the standard error ranges. Thirdly, the pressure compensation was verified for the three infrared gas analyzers after algorithmic compensation within the pressure compensation device. Based on this, the optimized least squares-wavelet transform soft threshold coupling model were established to integrate the ambient pressure, the test concentration and the standard gas concentration with the optimization model, and it was found that the maximum measurement errors after optimization with the optimized least squares-wavelet transform soft thresholding coupling method were 0.2%, 0.05% and 1%. In summary, this paper develops an error optimization model for various environmental pressure conditions, and provides a theoretical framework for the research and development of infrared gas analyzers and other spectroscopic gas detecting instruments. Additionally, it establishes a technical foundation for constructing in-situ on-line monitoring systems for trace gases in industrial environments.

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Section: Optimized Least Squares-wavelet Transform Soft Thresholding ...mentioning

confidence: 99%

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment

Su,

Zhang,

Wang

et al. 2024

Preprint

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“…The wavelet threshold denoising (WTD) proposed by Donoho and Johnstone in 1992 is of great significance in signal denoising [30,31]. The WTD is the most widely used denoising method because it is easy to calculate and can remove noise to a large extent while retaining the characteristics of singular information of the original signal [32,33]. Yangfeng Zhang et al proposed a WTD method with an Artificial Neural Network (ANN)optimized threshold for vibration sensor data in 2019, which has an ideal filtering effect on vibration sensor signals [34].…”

Section: Introductionmentioning

confidence: 99%

Elastic Wave Denoising in the Case of Bender Elements Type Piezoelectric Transducers

Xie

Liu

2022

Sustainability

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The accuracy of the wave signal is key to studying physical information inside the soil using bender-element-type piezoelectric transducers. There is too much noise during the elastic wave signal collected by bender elements, which is caused by factors such as fluid current and infiltration. At present, the mainstream method is the superposition method, which superposes multiple tested waveform data to obtain a clear waveform. However, the superposition method is limited by the number of signals during the collection, and the denoised waveform still contains high-frequency noise. A combination method combining superposition and the wavelet threshold is proposed in this work to improve the accuracy of the elastic waveform signal. Three different signal denoising simulation tests and one model box test are conducted to verify the method’s feasibility from two aspects. The results show that the combined method can effectively remove high-frequency noise and display clear waveforms based on overcoming the number of signals. This work provides a new means of signal denoising in the case of studying soil properties by bender-element-type piezoelectric transducers.

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“…Rasti et al 12 used three-dimensional wavelets to remove noise from hyperspectral images, enhancing the spectral features and increasing the accuracy of image classification. Huang et al 19 used a wavelet threshold denoising algorithm to eliminate the noise of Pound-Drever-Hall signals, further improving the signal-to-noise ratio (SNR) of signal and static-strain measurement resolution. However, selecting the wavelet functions and the decomposition scale of the signal was complicated and difficult.…”

Section: Introductionmentioning

confidence: 99%

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

Zhang

et al. 2019

J. Appl. Rem. Sens.

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Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters,"Abstract. Spectral noise causes distorted spectra, shifting the central wavelength and thus reducing the accuracy of surface parameter retrieval. A hybrid method combining mathematical-morphology and wavelet-transform (WB)-based filters was used to remove spectral noise. First, a generalized mathematical-morphology (GM) filter was used to remove large-amplitude noise, and then the processed spectra were smoothed using the WT-based filter to remove smallamplitude noise. The simulated noise spectrum and 76 measured canopy spectra for winter wheat were denoised with three filters: the combination filter (CF), GM, and WT. In the simulated experiments, five evaluation indices were calculated to evaluate the denoising effects. For measured spectra, qualitative analyses were performed based on spectral characteristics. Quantitative evaluations were conducted by deriving various vegetation indices from denoised spectra to retrieve wheat's biophysical and biochemical parameters. The results indicated that the CF removed both large-and small-amplitude noise efficiently, improving signal-to-noise ratio and peak signal-to-noise ratio of simulated noise spectrum and retrieval accuracy of leaf water content (LWC) significantly. Meanwhile, it better maintained the waveform and smoothness of spectrum, improving the retrieval accuracies of leaf area index and chlorophyll data slightly. The coefficient of determination (R 2 ) of developed model between the modified normalized difference water index and LWC was improved from 0.428 to 0.622 using the CF, 0.555 using the GM, and 0.549 using the WT. The R 2 and root mean square error between the measured and retrieval LWC were improved from 0.364 and 0.027 to 0.611 and 0.018 using the CF, whereas the corresponding values were 0.504 and 0.022 for the GM, and 0.478 and 0.023 for the WT. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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<inline-formula><tex-math>${\pi}$</tex-math></inline-formula>-Phase-Shifted FBG for High-Resolution Static-Strain Measurement Based on Wavelet Threshold Denoising Algorithm

Cited by 18 publications

References 16 publications

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment

Elastic Wave Denoising in the Case of Bender Elements Type Piezoelectric Transducers

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

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