The Experts below are selected from a list of 106881 Experts worldwide ranked by ideXlab platform
Wenming Zheng - One of the best experts on this subject based on the ideXlab platform.
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multichannel eeg based emotion recognition via group sparse canonical correlation analysis
IEEE Transactions on Cognitive and Developmental Systems, 2017Co-Authors: Wenming ZhengAbstract:In this paper, a novel group sparse canonical correlation analysis (GSCCA) method is proposed for simultaneous electroencephalogram (EEG) channel selection and emotion recognition. GSCCA is a group sparse extension of the conventional CCA method to model the linear correlationship between emotional EEG class label vectors and the corresponding EEG Feature vectors. In contrast to conventional CCA method or previous GSCCA methods, a major advantage of our GSCCA method is the ability of handling the group Feature selection problem from raw EEG Features, which makes it very suitable for simultaneously coping with both EEG emotion recognition and automatic channel selection issues where each EEG channel is associated with a group of raw EEG Features. To deal with EEG emotion recognition problem, we adopt the popularly used Frequency Feature to describe the EEG signal by dividing the full EEG Frequency band into five parts, i.e., $\boldsymbol {\delta }$ , $\boldsymbol {\theta }$ , $\boldsymbol {\alpha }$ , $\boldsymbol {\beta }$ , and $\boldsymbol {\gamma }$ Frequency bands, and then extract the Frequency band Features from each band for GSCCA model learning and emotion recognition. Finally, we conduct extensive experiments on EEG-based emotion recognition based on the SJTU emotion EEG dataset and experimental results demonstrate that the proposed GSCCA method would outperform the state-of-the-art EEG-based emotion recognition approaches.
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multichannel eeg based emotion recognition via group sparse canonical correlation analysis
IEEE Transactions on Cognitive and Developmental Systems, 2017Co-Authors: Wenming ZhengAbstract:In this paper, a novel group sparse canonical correlation analysis (GSCCA) method is proposed for simultaneous electroencephalogram (EEG) channel selection and emotion recognition. GSCCA is a group sparse extension of the conventional CCA method to model the linear correlationship between emotional EEG class label vectors and the corresponding EEG Feature vectors. In contrast to conventional CCA method or previous GSCCA methods, a major advantage of our GSCCA method is the ability of handling the group Feature selection problem from raw EEG Features, which makes it very suitable for simultaneously coping with both EEG emotion recognition and automatic channel selection issues where each EEG channel is associated with a group of raw EEG Features. To deal with EEG emotion recognition problem, we adopt the popularly used Frequency Feature to describe the EEG signal by dividing the full EEG Frequency band into five parts, i.e., $\boldsymbol {\delta }$ , $\boldsymbol {\theta }$ , $\boldsymbol {\alpha }$ , $\boldsymbol {\beta }$ , and $\boldsymbol {\gamma }$ Frequency bands, and then extract the Frequency band Features from each band for GSCCA model learning and emotion recognition. Finally, we conduct extensive experiments on EEG-based emotion recognition based on the SJTU emotion EEG dataset and experimental results demonstrate that the proposed GSCCA method would outperform the state-of-the-art EEG-based emotion recognition approaches.
Axel Jessner - One of the best experts on this subject based on the ideXlab platform.
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single pulse analysis of psr b1133 16 at 8 35 ghz and carousel circulation time
Monthly Notices of the Royal Astronomical Society, 2012Co-Authors: Sneha Honnappa, Wojciech Lewandowski, Jaroslaw Kijak, Avinash A. Deshpande, Olaf Maron, Axel JessnerAbstract:A successful attempt has been made to analyse about 6000 single pulses of PSR B1133+16 obtained with the 100-m Effelsberg radio telescope. The high-resolution (60 μs) data were taken at a Frequency of 8.35 GHz with a bandwidth of 1.1 GHz. In order to examine the pulse-to-pulse intensity modulations, we performed both longitude- and harmonic-resolved fluctuation spectral analysis. We identified the low-Frequency Feature associated with an amplitude modulation at , which can be interpreted as the circulation time P4≃ 30 P1 of the underlying subbeam carousel model. Despite the erratic nature of this pulsar, we also found evidence of periodic pseudo-nulls with P4= 28.44 P1. This is exactly the value at which Herfindal & Rankin found periodic pseudo-nulls in their 327 MHz data. We thus believe that this is the actual carousel circulation time in PSR B1133+16, particularly during orderly circulation.
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single pulse analysis of psr b1133 16 at 8 35 ghz and carousel circulation time
arXiv: Solar and Stellar Astrophysics, 2011Co-Authors: Sneha Honnappa, Wojciech Lewandowski, Jaroslaw Kijak, Avinash A. Deshpande, Olaf Maron, Axel JessnerAbstract:A successful attempt was made to analyse about 6000 single pulses of PSR B1133+16 obtained with the 100-meter Effelsberg radio-telescope. The high resolution (60 micro-seconds) data were taken at a Frequency of 8.35 GHz with a bandwidth of 1.1 GHz. In order to examine the pulse-to-pulse intensity modulations, we performed both the longitude- and the harmonic-resolved fluctuation spectral analysis. We identified the low Frequency Feature associated with an amplitude modulation at f4 ~ 0.033 P1^(-1), which can be interpreted as the circulation time P4 ~ 30 P1 of the underlying subbeam carousel model. Despite an erratic nature of this pulsar, we also found an evidence of periodic pseudo-nulls with P4 = 28.44 P1. This is exactly the value at which Herfindal & Rankin found periodic pseudo-nulls in their 327 MHz data. We thus believe that this is the actual carousel circulation time in PSR B1133+16, particularly during orderly circulation.
G F Bin - One of the best experts on this subject based on the ideXlab platform.
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early fault diagnosis of rotating machinery based on wavelet packets empirical mode decomposition Feature extraction and neural network
Mechanical Systems and Signal Processing, 2012Co-Authors: G F Bin, Jinji Gao, B S DhillonAbstract:Abstract After analyzing the shortcomings of current Feature extraction and fault diagnosis technologies, a new approach based on wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) are combined to extract fault Feature Frequency and neural network for rotating machinery early fault diagnosis is proposed. Acquisition signals with fault Frequency Feature are decomposed into a series of narrow bandwidth using WPD method for de-noising, then, the intrinsic mode functions (IMFs), which usually denoted the Features of corresponding Frequency bandwidth can be obtained by applying EMD method. Thus, the component of IMF with signal Feature can be separated from all IMFs and the energy moment of IMFs is proposed as eigenvector to effectively express the failure Feature. The classical three layers BP neural network model taking the fault Feature Frequency as target input of neural network, the 5 spectral bandwidth energy of vibration signal spectrum as characteristic parameter, and the 10 types of representative rotor fault as output can be established to identify the fault pattern of a machine. Lastly, the fault identification model of rotating machinery with rotor lateral early crack based on BP neural network is taken as an example. The results show that the proposed method can effectively get the signal Feature to diagnose the occurrence of early fault of rotating machinery.
Sneha Honnappa - One of the best experts on this subject based on the ideXlab platform.
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single pulse analysis of psr b1133 16 at 8 35 ghz and carousel circulation time
Monthly Notices of the Royal Astronomical Society, 2012Co-Authors: Sneha Honnappa, Wojciech Lewandowski, Jaroslaw Kijak, Avinash A. Deshpande, Olaf Maron, Axel JessnerAbstract:A successful attempt has been made to analyse about 6000 single pulses of PSR B1133+16 obtained with the 100-m Effelsberg radio telescope. The high-resolution (60 μs) data were taken at a Frequency of 8.35 GHz with a bandwidth of 1.1 GHz. In order to examine the pulse-to-pulse intensity modulations, we performed both longitude- and harmonic-resolved fluctuation spectral analysis. We identified the low-Frequency Feature associated with an amplitude modulation at , which can be interpreted as the circulation time P4≃ 30 P1 of the underlying subbeam carousel model. Despite the erratic nature of this pulsar, we also found evidence of periodic pseudo-nulls with P4= 28.44 P1. This is exactly the value at which Herfindal & Rankin found periodic pseudo-nulls in their 327 MHz data. We thus believe that this is the actual carousel circulation time in PSR B1133+16, particularly during orderly circulation.
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single pulse analysis of psr b1133 16 at 8 35 ghz and carousel circulation time
arXiv: Solar and Stellar Astrophysics, 2011Co-Authors: Sneha Honnappa, Wojciech Lewandowski, Jaroslaw Kijak, Avinash A. Deshpande, Olaf Maron, Axel JessnerAbstract:A successful attempt was made to analyse about 6000 single pulses of PSR B1133+16 obtained with the 100-meter Effelsberg radio-telescope. The high resolution (60 micro-seconds) data were taken at a Frequency of 8.35 GHz with a bandwidth of 1.1 GHz. In order to examine the pulse-to-pulse intensity modulations, we performed both the longitude- and the harmonic-resolved fluctuation spectral analysis. We identified the low Frequency Feature associated with an amplitude modulation at f4 ~ 0.033 P1^(-1), which can be interpreted as the circulation time P4 ~ 30 P1 of the underlying subbeam carousel model. Despite an erratic nature of this pulsar, we also found an evidence of periodic pseudo-nulls with P4 = 28.44 P1. This is exactly the value at which Herfindal & Rankin found periodic pseudo-nulls in their 327 MHz data. We thus believe that this is the actual carousel circulation time in PSR B1133+16, particularly during orderly circulation.
B S Dhillon - One of the best experts on this subject based on the ideXlab platform.
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early fault diagnosis of rotating machinery based on wavelet packets empirical mode decomposition Feature extraction and neural network
Mechanical Systems and Signal Processing, 2012Co-Authors: G F Bin, Jinji Gao, B S DhillonAbstract:Abstract After analyzing the shortcomings of current Feature extraction and fault diagnosis technologies, a new approach based on wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) are combined to extract fault Feature Frequency and neural network for rotating machinery early fault diagnosis is proposed. Acquisition signals with fault Frequency Feature are decomposed into a series of narrow bandwidth using WPD method for de-noising, then, the intrinsic mode functions (IMFs), which usually denoted the Features of corresponding Frequency bandwidth can be obtained by applying EMD method. Thus, the component of IMF with signal Feature can be separated from all IMFs and the energy moment of IMFs is proposed as eigenvector to effectively express the failure Feature. The classical three layers BP neural network model taking the fault Feature Frequency as target input of neural network, the 5 spectral bandwidth energy of vibration signal spectrum as characteristic parameter, and the 10 types of representative rotor fault as output can be established to identify the fault pattern of a machine. Lastly, the fault identification model of rotating machinery with rotor lateral early crack based on BP neural network is taken as an example. The results show that the proposed method can effectively get the signal Feature to diagnose the occurrence of early fault of rotating machinery.