Denoising of gamma-ray spectrum by optimized wavelet thresholding based on modified genetic algorithm in carbon/oxygen logging

Yang, Guofeng; Dai, Junhu; Liu, Xiangjun; Wu, Xiaolong; Chen, Meng; Qin, Hao

doi:10.1007/s10967-019-06477-x

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…The performance of Refs. [18,29,34] tends to stabilize as the input SNR increases, but the final denoising effect is not as significant as MGPSO-ITF. In contrast, the proposed MGPSO-ITF algorithm outperforms the existing approaches in terms of output SNR, RMSE, and NSR.…”

Section: Simulated Results Of the Mgpso-itfmentioning

confidence: 94%

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Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

Zhu,

Fu,

et al. 2023

Electronics

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The wavelet threshold functions are widely used in oil chromatography denoising because high-quality signals are the basis for Dissolved Gas Analysis (DGA), which determines the accuracy of transformer fault monitoring. However, there are certain limitations of the wavelet threshold functions, such as the Pseudo-Gibbs phenomenon and improper threshold selection. To this purpose, a modified genetic particle swarm optimization-based improved threshold function denoising method (MGPSO-ITF) is proposed. Specifically, the method constructs a new parametric threshold function that possesses high-order derivability and a small constant deviation. To obtain optimal values for the tunable parameters, MGPSO is employed, which outperforms other methods in identifying the optimum and achieving fast convergence. The simulation results demonstrate that the enhanced thresholding function yields a higher Signal-to-Noise Ratio (SNR), higher Noise Suppression Ratio (NSR), and smaller Root Mean Square Error (RMSE) compared to prior methods. Specifically, for the originally relatively smooth signal, MGPSO-ITF does not over-correct it to cause distortion. Furthermore, experiments on measured signals illustrate that the MGPSO-ITF is highly effective at denoising and preserving the original signal properties. Particularly in cases where peak deformation is prominent, the algorithm outperforms both hard and soft thresholding methods, achieving a reduction of 2.934% and 1.029% in peak area error, respectively.

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Section: Simulated Results Of the Mgpso-itfmentioning

confidence: 94%

“…Denoising performance of different algorithms for different SNR inputs. (The methods and references in the figure are as follows: IWTF[18], IWT[19], PSO-PWT[28], PSO-RWE[29], GDES-ABC[30] and GA-WT[34].)5. The Experiment on Measured Signals5.1.…”

mentioning

confidence: 99%

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

Zhu,

Fu,

et al. 2023

Electronics

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“…Mutation operation continuously increases the diversity of the population in evolution and helps to jump out of the local optimum [10] . Mutation probability M is an important parameter, representing the number of variants of the mutated individuals, which usually takes a value not greater than 0.1 and is constant, M is small and helps to jump out of the local optimum, but too large or too small a value of M has an impact on the iterative process of the algorithm.…”

Section: Improved Mutation Operationsmentioning

confidence: 99%

Research on energy storage charging piles based on improved genetic algorithm

Sun

2024

J. Phys.: Conf. Ser.

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Aiming at the charging demand of electric vehicles, an improved genetic algorithm is proposed to optimize the energy storage charging piles optimization scheme. Firstly, the characteristics of electric load are analyzed, the model of energy storage charging piles is established, the charging volume, power and charging/discharging timing constraints in the charging process are considered, and the optimization is carried out by the improved genetic algorithm, and the profit of charging piles and user charging electricity charges are calculated, so as to obtain the possible optimal parameters, as well as the impact of the energy storage system on the stability of the power grid. Secondly, the analysis of the results shows that the energy storage charging piles can not only improve the profit to reduce the user’s electricity cost, but also reduce the impact of electric vehicle charging on the power grid load.

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“…Conventional denoising methods tend to remove all of the high-frequency components of a signal that contain noise, thereby also eliminating the desirable components of a signal in the high-frequency range. Wavelet thresholding can solve this problem [11].…”

Section: Introductionmentioning

confidence: 99%

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

Shen

Cao

et al. 2019

Shock and Vibration

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In recent years, High-G MEMS accelerometers have been widely used in aviation, medicine, and other fields. So it is extremely important to improve the accuracy and performance of High-G MEMS accelerometers. For this purpose, we propose a fusion algorithm that combines EMD, wavelet thresholding, and temperature compensation to process measurement data from a High-G MEMS accelerometer. In the fusion algorithm, the original accelerometer signal is first decomposed by EMD to obtain the intrinsic mode function (IMF). Then, sample entropy (SE) is used to divide the IMF components into three segments. The noise segment is directly omitted, wavelet thresholding is performed on the mixing segment, and a GA-BP performs temperature compensation on the drift segment. Finally, signal reconstruction is implemented. Later, a comparative analysis is carried out on the results from four models: EMD, wavelet thresholding, EMD + wavelet thresholding, and EMD + wavelet thresholding + temperature compensation. The experimental data show that the acceleration random walk change from 1712.66 g/h/Hz0.5 to 79.15 g/h/Hz0.5 and the zero-deviation stability change from 49275 g/h to 774.7 g/h. This indicates that the fusion algorithm (EMD + wavelet thresholding + temperature compensation) not only effectively suppresses the noise of high-frequency components but also compensates for temperature drift in the accelerometer.

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Denoising of gamma-ray spectrum by optimized wavelet thresholding based on modified genetic algorithm in carbon/oxygen logging

Cited by 11 publications

References 18 publications

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

Research on energy storage charging piles based on improved genetic algorithm

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

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