传统稀疏表示方法因其在冲击类信号特征提取中的独特优势而在故障诊断领域被广泛研究。然而,传统稀疏表示理论基于对干扰噪声的高斯分布假设,导致其难以适用于多种噪声分布混合的实际现场。针对上述问题,提出一种混合高斯噪声条件下的...传统稀疏表示方法因其在冲击类信号特征提取中的独特优势而在故障诊断领域被广泛研究。然而,传统稀疏表示理论基于对干扰噪声的高斯分布假设,导致其难以适用于多种噪声分布混合的实际现场。针对上述问题,提出一种混合高斯噪声条件下的冲击类故障特征稀疏表示方法。基于传统稀疏表示理论的贝叶斯框架,借助混合高斯分布的万有逼近性质,建立了基于db4小波字典的混合高斯噪声稀疏分解模型,并推导了基于EM(Expectation-maximum,EM)和ADMM(Alternating direction method of multipliers,ADMM)的优化求解算法用于模型求解。仿真和实验结果表明,所提出的方法能够有效提取混合噪声干扰下的冲击类微弱故障特征信号。展开更多
相关熵被广泛地应用于非高斯信号处理中,但是也不可避免步长与失调量之间的矛盾。为了解决这个矛盾,基于最大相关熵的凸组合(Convex combination of Maximum Correntropy Criterion,CMCC)滤波器被提出,以同时获得大步长滤波器的快收敛...相关熵被广泛地应用于非高斯信号处理中,但是也不可避免步长与失调量之间的矛盾。为了解决这个矛盾,基于最大相关熵的凸组合(Convex combination of Maximum Correntropy Criterion,CMCC)滤波器被提出,以同时获得大步长滤波器的快收敛速度和小步长滤波器的低失调量。但是,当两个步长不同的滤波器进行凸组合时,由于步长选取的差异,会导致组合滤波器的收敛速度和追踪最优值的能力下降。因此,提出了基于最大相关熵的多凸组合滤波器(Multi-convex Combination of Maximum Correntropy Criterion, MCMCC)。MCMCC算法能够提供数量更多不同步长的自适应滤波器,使得组合滤波器可以灵活地调节多个步长的权重占比,从而具有更好的追踪能力。仿真结果表明,在针对混合高斯噪声和突变的系统辨识中,MCMCC算法相比CMCC算法具有更快的收敛速度、再收敛能力和追踪能力。展开更多
In order to effectively restore color noisy images with the mixture of Gaussian noise and impulse noise,a new algorithm is proposed using the quaternion-based holistic processing idea for color images.First,a color im...In order to effectively restore color noisy images with the mixture of Gaussian noise and impulse noise,a new algorithm is proposed using the quaternion-based holistic processing idea for color images.First,a color image is represented by a pure quaternion matrix.Secondly,according to the different characteristics of the Gaussian noise and the impulse noise,an algorithm based on quaternion directional vector order statistics is used to detect the impulse noise. Finally,the quaternion optimal weights non-local means filter （QOWNLMF）for Gaussian noise removal is improved for the mixed noise removal.The detected impulse noise pixels are not considered in the calculation of weights.Experimental results on five standard images demonstrate that the proposed algorithm performs better than the commonly used robust outlyingness ratio-nonlocal means （ROR-NLM）algorithm and the optimal weights mixed filter （OWMF）.展开更多
To discuss further the dependence of stochastic resonance on signals, nonlinear systems and noise, especially on noise, the binary input signal buried in Gaussian mixture noise through a nonlinear threshold array is s...To discuss further the dependence of stochastic resonance on signals, nonlinear systems and noise, especially on noise, the binary input signal buried in Gaussian mixture noise through a nonlinear threshold array is studied based on mutual information. It is obtained that Gaussian mixture noise can improve the information transmission through the array. Both stochastic resonance （SR） and suprathreshold stochastic resonance （SSR） can be observed in the single threshold system and in the threshold array. The parameters in noise distribution affect the occurrence of SR and SSR. The efficacy of information transmission can be significantly enhanced as the number of threshold devices in the array increases. These results show further the dependence of SR and SSR on the noise distribution, and also extend the applicability of SR and SSR in information transmission.展开更多
文摘传统稀疏表示方法因其在冲击类信号特征提取中的独特优势而在故障诊断领域被广泛研究。然而,传统稀疏表示理论基于对干扰噪声的高斯分布假设,导致其难以适用于多种噪声分布混合的实际现场。针对上述问题,提出一种混合高斯噪声条件下的冲击类故障特征稀疏表示方法。基于传统稀疏表示理论的贝叶斯框架,借助混合高斯分布的万有逼近性质,建立了基于db4小波字典的混合高斯噪声稀疏分解模型,并推导了基于EM(Expectation-maximum,EM)和ADMM(Alternating direction method of multipliers,ADMM)的优化求解算法用于模型求解。仿真和实验结果表明,所提出的方法能够有效提取混合噪声干扰下的冲击类微弱故障特征信号。
文摘相关熵被广泛地应用于非高斯信号处理中,但是也不可避免步长与失调量之间的矛盾。为了解决这个矛盾,基于最大相关熵的凸组合(Convex combination of Maximum Correntropy Criterion,CMCC)滤波器被提出,以同时获得大步长滤波器的快收敛速度和小步长滤波器的低失调量。但是,当两个步长不同的滤波器进行凸组合时,由于步长选取的差异,会导致组合滤波器的收敛速度和追踪最优值的能力下降。因此,提出了基于最大相关熵的多凸组合滤波器(Multi-convex Combination of Maximum Correntropy Criterion, MCMCC)。MCMCC算法能够提供数量更多不同步长的自适应滤波器,使得组合滤波器可以灵活地调节多个步长的权重占比,从而具有更好的追踪能力。仿真结果表明,在针对混合高斯噪声和突变的系统辨识中,MCMCC算法相比CMCC算法具有更快的收敛速度、再收敛能力和追踪能力。
基金The National Natural Science Foundation of China(No.61572258,61173141,61271312,61232016,61272421)the Natural Science Foundation of Jiangsu Province(No.BK2012858,BK20151530)+1 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.13KJB520015)Open Fund of Jiangsu Engineering Center of Network Monitoring(No.KJR1404)
文摘In order to effectively restore color noisy images with the mixture of Gaussian noise and impulse noise,a new algorithm is proposed using the quaternion-based holistic processing idea for color images.First,a color image is represented by a pure quaternion matrix.Secondly,according to the different characteristics of the Gaussian noise and the impulse noise,an algorithm based on quaternion directional vector order statistics is used to detect the impulse noise. Finally,the quaternion optimal weights non-local means filter （QOWNLMF）for Gaussian noise removal is improved for the mixed noise removal.The detected impulse noise pixels are not considered in the calculation of weights.Experimental results on five standard images demonstrate that the proposed algorithm performs better than the commonly used robust outlyingness ratio-nonlocal means （ROR-NLM）algorithm and the optimal weights mixed filter （OWMF）.
文摘To discuss further the dependence of stochastic resonance on signals, nonlinear systems and noise, especially on noise, the binary input signal buried in Gaussian mixture noise through a nonlinear threshold array is studied based on mutual information. It is obtained that Gaussian mixture noise can improve the information transmission through the array. Both stochastic resonance （SR） and suprathreshold stochastic resonance （SSR） can be observed in the single threshold system and in the threshold array. The parameters in noise distribution affect the occurrence of SR and SSR. The efficacy of information transmission can be significantly enhanced as the number of threshold devices in the array increases. These results show further the dependence of SR and SSR on the noise distribution, and also extend the applicability of SR and SSR in information transmission.
