变电设备内部有时会存在多个缺陷的局部放电,其放电模式识别及危险度评估的难度大大增加,为更有效地诊断设备绝缘状况,该文提出了一种基于累积能量函数特征参量优化提取的多源局放分离技术。利用时频域累积能量函数表征脉冲电流脉冲或...变电设备内部有时会存在多个缺陷的局部放电,其放电模式识别及危险度评估的难度大大增加,为更有效地诊断设备绝缘状况,该文提出了一种基于累积能量函数特征参量优化提取的多源局放分离技术。利用时频域累积能量函数表征脉冲电流脉冲或特高频(UHF)信号的时频域变化,并采用数学形态学梯度运算提取了时频域累积能量的上升陡度参量。提出了以上升陡度参量的标准差作为分离性能评价指标,优化选取数学形态学梯度运算中的结构元素长度,提取此时的上升陡度参量,达到最优分离效果的目标。最后在实验室252k V GIS模型内建立了3种典型多缺陷模型,将所提出的多源放电分离技术应用于该混合缺陷放电UHF信号的分离,进而将该方法成功应用于一起现场1100k V GIS多源局放案例。结果表明,特征参量优化提取分离方法适用于内外置UHF传感器信号,在多源放电混合UHF信号分离中具有良好的应用效果。展开更多
Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of ble...Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.展开更多
Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique i...Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant. Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops,and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis(ICA).展开更多
A neural network method for independent source separation (ISS) of multichannel electroencephalogram (EEG) is proposed in this paper.Using the denoising function of wavelet multiscale decomposition,the high-frequency ...A neural network method for independent source separation (ISS) of multichannel electroencephalogram (EEG) is proposed in this paper.Using the denoising function of wavelet multiscale decomposition,the high-frequency noises are removed from the original (raw) EEGs.Then the multichannel EEGs are treated as the weighted mixtures and the expression of weight vector is obtained by seeking the local extrema of the fourth-order cumulants (i.e.kurtosis coefficients) of the mixtures.After these process steps,the weighted mixtures are used as the input of neural network,so the independent source of EEGs can be separated one by one.The experimental results show that our method is effective for ISS of multichannel EEGs.展开更多
文摘变电设备内部有时会存在多个缺陷的局部放电,其放电模式识别及危险度评估的难度大大增加,为更有效地诊断设备绝缘状况,该文提出了一种基于累积能量函数特征参量优化提取的多源局放分离技术。利用时频域累积能量函数表征脉冲电流脉冲或特高频(UHF)信号的时频域变化,并采用数学形态学梯度运算提取了时频域累积能量的上升陡度参量。提出了以上升陡度参量的标准差作为分离性能评价指标,优化选取数学形态学梯度运算中的结构元素长度,提取此时的上升陡度参量,达到最优分离效果的目标。最后在实验室252k V GIS模型内建立了3种典型多缺陷模型,将所提出的多源放电分离技术应用于该混合缺陷放电UHF信号的分离,进而将该方法成功应用于一起现场1100k V GIS多源局放案例。结果表明,特征参量优化提取分离方法适用于内外置UHF传感器信号,在多源放电混合UHF信号分离中具有良好的应用效果。
文摘Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.
基金Supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry.
文摘Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant. Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops,and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis(ICA).
基金Natural Science Foundation of Fujian Province of Chinagrant number:C0710036 and T0750008
文摘A neural network method for independent source separation (ISS) of multichannel electroencephalogram (EEG) is proposed in this paper.Using the denoising function of wavelet multiscale decomposition,the high-frequency noises are removed from the original (raw) EEGs.Then the multichannel EEGs are treated as the weighted mixtures and the expression of weight vector is obtained by seeking the local extrema of the fourth-order cumulants (i.e.kurtosis coefficients) of the mixtures.After these process steps,the weighted mixtures are used as the input of neural network,so the independent source of EEGs can be separated one by one.The experimental results show that our method is effective for ISS of multichannel EEGs.
