We propose a novel flow measurement method for gas–liquid two-phase slug flow by using the blind source separation technique. The flow measurement model is established based on the fluctuation characteristics of diff...We propose a novel flow measurement method for gas–liquid two-phase slug flow by using the blind source separation technique. The flow measurement model is established based on the fluctuation characteristics of differential pressure(DP) signals measured from a Venturi meter. It is demonstrated that DP signals of two-phase flow are a linear mixture of DP signals of single phase fluids. The measurement model is a combination of throttle relationship and blind source separation model. In addition, we estimate the mixture matrix using the independent component analysis(ICA) technique. The mixture matrix could be described using the variances of two DP signals acquired from two Venturi meters. The validity of the proposed model was tested in the gas–liquid twophase flow loop facility. Experimental results showed that for most slug flow the relative error is within 10%.We also find that the mixture matrix is beneficial to investigate the flow mechanism of gas–liquid two-phase flow.展开更多
The proposed Blind Source Separation method(BSS),based on sparse representations,fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of...The proposed Blind Source Separation method(BSS),based on sparse representations,fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources.The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique(DUET) which assumes the number of sources a priori.In the proposed algorithm,the Short-Time Fourier Transform(STFT) is used to obtain the sparse rep-resentations,a clustering method called Unsupervised Robust C-Prototypes(URCP) which can ac-curately identify multiple clusters regardless of the number of them is adopted to replace the histo-gram-based technique in DUET,and the binary time-frequency masks are constructed to separate the mixtures.Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio(SIR),and maintains good speech quality in the separation results.展开更多
基金Supported by the National Natural Science Foundation of China(51304231)the Natural Science Foundation of Shandong Province(ZR2010EQ015)
文摘We propose a novel flow measurement method for gas–liquid two-phase slug flow by using the blind source separation technique. The flow measurement model is established based on the fluctuation characteristics of differential pressure(DP) signals measured from a Venturi meter. It is demonstrated that DP signals of two-phase flow are a linear mixture of DP signals of single phase fluids. The measurement model is a combination of throttle relationship and blind source separation model. In addition, we estimate the mixture matrix using the independent component analysis(ICA) technique. The mixture matrix could be described using the variances of two DP signals acquired from two Venturi meters. The validity of the proposed model was tested in the gas–liquid twophase flow loop facility. Experimental results showed that for most slug flow the relative error is within 10%.We also find that the mixture matrix is beneficial to investigate the flow mechanism of gas–liquid two-phase flow.
文摘The proposed Blind Source Separation method(BSS),based on sparse representations,fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources.The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique(DUET) which assumes the number of sources a priori.In the proposed algorithm,the Short-Time Fourier Transform(STFT) is used to obtain the sparse rep-resentations,a clustering method called Unsupervised Robust C-Prototypes(URCP) which can ac-curately identify multiple clusters regardless of the number of them is adopted to replace the histo-gram-based technique in DUET,and the binary time-frequency masks are constructed to separate the mixtures.Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio(SIR),and maintains good speech quality in the separation results.
