Partial discharge(PD)is an important reason for the insulation failure of the switchgear.In the process of PD detection,PD signal is often annihilated in strong noise.In order to improve the accuracy of PD detection i...Partial discharge(PD)is an important reason for the insulation failure of the switchgear.In the process of PD detection,PD signal is often annihilated in strong noise.In order to improve the accuracy of PD detection in power plant switchgear,a method based on continuous adaptive wavelet threshold switchgear PD signals denoising is proposed in this paper.By constructing a continuous adaptive threshold function and introducing adjustment parameters,the problems of over⁃processing of traditional hard threshold functions and incomplete denoising of soft threshold functions can be improved.The analysis results of simulated signals and measured signals show that the continuous adaptive wavelet threshold denoising method is significantly better than the traditional denoising method for the PD signal.The proposed method in this paper retains the characteristics of the original signal.Compared with the traditional denoising methods,after denoising the simulated signals,the signal⁃to⁃noise ratio(SNR)is increased by more than 30%,and the root⁃mean⁃square error(RMSE)is reduced by more than 30%.After denoising the real signal,the noise suppression ratio(NRR)is increased by more than 40%.The recognition accuracy rate of PD signal has also been improved to a certain extent,which proves that the method has a certain practicability.展开更多
In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detec...In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detection algorithm is proposed. The local maximum modulus is extracted by linear interpolation in wavelet domain. With the analysis on histogram, the image is filtered with an adaptive dual threshold method, which effectively detects the contours of small structures as well as the boundaries of large objects. A wavelet domain's propagation function is used to further select weak edges. Experimental results have shown the self adaptivity of the threshold to images having the same kind of histogram, and the efficiency even in noise tampered images.展开更多
This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on th...This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.展开更多
针对复杂海洋环境中的船舶辐射噪声信号去噪问题,该文提出了一种基于阿基米德优化算法优化变分模态分解联合小波阈值的非平稳水声信号去噪方法。首先,采用阿基米德优化算法对变分模态分解进行最优参数寻优,确定惩罚因子α和最佳模态分解...针对复杂海洋环境中的船舶辐射噪声信号去噪问题,该文提出了一种基于阿基米德优化算法优化变分模态分解联合小波阈值的非平稳水声信号去噪方法。首先,采用阿基米德优化算法对变分模态分解进行最优参数寻优,确定惩罚因子α和最佳模态分解数k。对原始水声信号进行变分模态分解,通过相关系数及其中心频率选择信号主导模态分量。结合小波阈值去噪对信号主导模态分量进行去噪后完成信号重构。仿真及实验结果表明:相比传统水声信号去噪方法,该文方法在复杂噪声环境下可有效提升信噪比12 d B,降低均方根误差80%,并在去噪的同时保持信号关键特征,具有更优的去噪性能。展开更多
基金Sponsored by the Liaoning Provincial Department of Education Scientific Research Funding Project(Youth)(Grant No.JDL2020020)the Changzhou City Applied Basic Research Program(Grant No.CJ2020007).
文摘Partial discharge(PD)is an important reason for the insulation failure of the switchgear.In the process of PD detection,PD signal is often annihilated in strong noise.In order to improve the accuracy of PD detection in power plant switchgear,a method based on continuous adaptive wavelet threshold switchgear PD signals denoising is proposed in this paper.By constructing a continuous adaptive threshold function and introducing adjustment parameters,the problems of over⁃processing of traditional hard threshold functions and incomplete denoising of soft threshold functions can be improved.The analysis results of simulated signals and measured signals show that the continuous adaptive wavelet threshold denoising method is significantly better than the traditional denoising method for the PD signal.The proposed method in this paper retains the characteristics of the original signal.Compared with the traditional denoising methods,after denoising the simulated signals,the signal⁃to⁃noise ratio(SNR)is increased by more than 30%,and the root⁃mean⁃square error(RMSE)is reduced by more than 30%.After denoising the real signal,the noise suppression ratio(NRR)is increased by more than 40%.The recognition accuracy rate of PD signal has also been improved to a certain extent,which proves that the method has a certain practicability.
文摘In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detection algorithm is proposed. The local maximum modulus is extracted by linear interpolation in wavelet domain. With the analysis on histogram, the image is filtered with an adaptive dual threshold method, which effectively detects the contours of small structures as well as the boundaries of large objects. A wavelet domain's propagation function is used to further select weak edges. Experimental results have shown the self adaptivity of the threshold to images having the same kind of histogram, and the efficiency even in noise tampered images.
基金the Fundamental Research Funds for the Central Universities of China(No.YS1404)the Beijing University of Chemical Technology Interdisciplinary Funds for "Visual Media Computing"
文摘This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.
文摘针对复杂海洋环境中的船舶辐射噪声信号去噪问题,该文提出了一种基于阿基米德优化算法优化变分模态分解联合小波阈值的非平稳水声信号去噪方法。首先,采用阿基米德优化算法对变分模态分解进行最优参数寻优,确定惩罚因子α和最佳模态分解数k。对原始水声信号进行变分模态分解,通过相关系数及其中心频率选择信号主导模态分量。结合小波阈值去噪对信号主导模态分量进行去噪后完成信号重构。仿真及实验结果表明:相比传统水声信号去噪方法,该文方法在复杂噪声环境下可有效提升信噪比12 d B,降低均方根误差80%,并在去噪的同时保持信号关键特征,具有更优的去噪性能。
