Hybrid pixel based method for multimodal image fusion based on Integration of Pulse Coupled Neural Network (PCNN) and Genetic Algorithm (GA) using Empirical Mode Decomposition (EMD)

Indhumathi, R.; Narmadha, T. V.; kurunathan, Harrison

doi:10.1016/j.micpro.2022.104665

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…where R vv denotes the autocorrelation function of the sound source signal, α denotes the attenuation coefficient, and D denotes the number of delay points. Empirical mode decomposition (EMD) [37] converts the original signal from a time scale into a small number of intrinsic mode function (IMF) components. These components satisfy the following two criteria: (1) the number of extreme values and number of zero crossings must be equal or differ by at most one.…”

Section: Frequency-domain Parametersmentioning

confidence: 99%

Experimental study on acoustic signal characteristic analysis and time delay estimation of pipeline leakage in boilers

Luo,

Yang,

Sun

et al. 2023

Meas. Sci. Technol.

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Acoustic signal detection technology has significant advantages in detecting the leakage and bursting of heat exchange pipes in boilers. To address the current lack of sound signal data for steam medium leakages and the problem of large errors in the complex sound field environment of power plants, we performed an innovative experimental comparative analysis of leakage acoustic signals under a dual medium of compressed air and steam to provide a reference for detecting leakage acoustic signals. During the experiment, the time and frequency domains were analyzed by changing the pressure of the leakage pipeline and aperture of the leakage hole, and the spectrogram and power spectrum of the leakage acoustic signal were obtained using fast Fourier transform and autocorrelation analysis. The results showed that the signal value of the leakage medium increased with increasing pipeline pressure and leakage aperture and that the energy of the steam leakage acoustic signal was greater than that of compressed air under the same pressure and aperture because of its larger specific heat capacity. In addition, the complete empirical mode decomposition of adaptive noise (CEEMDAN) algorithm was introduced into the denoising decomposition of the leakage sound signal in the furnace, and the average error of the time delay value of the leakage acoustic signal calculated using the CEEMDAN algorithm was observed to be within 5%.

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Section: Frequency-domain Parametersmentioning

confidence: 99%

Experimental study on acoustic signal characteristic analysis and time delay estimation of pipeline leakage in boilers

Luo,

Yang,

Sun

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…It is precisely this characteristic that makes EMD method can be effectively used to process nonstationary signals with large amount of data. The steps of EMD decomposition are as follows [4] : 1) All extreme points of the signal to be processed are screened;…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

The research on noise reduction of ground target vibration signal based on wavelet decomposition and EMD

Zou

Zhang

2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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In order to solve the problems of identifying the remote ground vibration signals with complex signal components and low signal-to-noise ratio, this paper proposes a denoising method combining wavelet transform and empirical mode decomposition: Wavelet EMD Denoising. First, wavelet transform is applied to the noisy signal for initial noise reduction, then empirical mode decomposition is applied to the signal after initial noise reduction, then wavelet decomposition is applied to the sub signal for noise reduction again, and then the sub signal is filtered and synthesized to obtain a signal with high signal-to-noise ratio for subsequent processing and analysis. In this paper, EMD synthesis and wavelet EMD noise reduction processing are respectively carried out for the collected long-distance personnel vibration signal and vehicle vibration signal, and the time-frequency domain of the results of both are analyzed. It is proved that the wavelet EMD noise reduction proposed in this paper can effectively reduce the noise of this type of signal compared with EMD synthesis, and its frequency domain energy is more concentrated: the energy proportion of the frequency band where the effective signal of vehicle signal is located is increased by 29%, The proportion of energy in the frequency band where the effective signal of personnel signal is located is increased by 51%. The noise reduction effect is better than the traditional EMD synthesis algorithm.

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“…For the fusion of highfrequency subband, a fusion strategy using PCNN with global coupling and pulse synchronization properties is used. PCNN is a third-generation bio-artificial neural network (PCNN) method used in various fields such as image processing, target detection, and image fusion [35][36][37]. The main advantage of the PCNN method is that no training process is required to apply image fusion.…”

Section: High Frequency Fusionmentioning

confidence: 99%

An image fusion-based method for recovering the 3D shape of roll surface defects

Xu,

Lou

et al. 2023

Meas. Sci. Technol.

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Most of the existing studies on roll surface defects focus on qualitative detection and lack of quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are stylus method, laser scanning method and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform (NSST) is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called Mm-PCNN to obtain a fully focused image. The method achieves 3D shape recovery of defects by modelling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. The experimental results show that the proposed method effectively integrates detailed information and achieves a reconstruction error control of less than 4%, thus ensuring higher accuracy and improved noise immunity.

show abstract

Hybrid pixel based method for multimodal image fusion based on Integration of Pulse Coupled Neural Network (PCNN) and Genetic Algorithm (GA) using Empirical Mode Decomposition (EMD)

Cited by 10 publications

References 23 publications

Experimental study on acoustic signal characteristic analysis and time delay estimation of pipeline leakage in boilers

Experimental study on acoustic signal characteristic analysis and time delay estimation of pipeline leakage in boilers

The research on noise reduction of ground target vibration signal based on wavelet decomposition and EMD

An image fusion-based method for recovering the 3D shape of roll surface defects

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