The Experts below are selected from a list of 207993 Experts worldwide ranked by ideXlab platform
Jinliang He - One of the best experts on this subject based on the ideXlab platform.
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detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder
IEEE Transactions on Smart Grid, 2018Co-Authors: Kunjin Chen, Jun Hu, Jinliang HeAbstract:We present in this paper a novel method for fault detection and classification in power transmission lines based on convolutional sparse autoencoder. Contrary to conventional methods, the proposed method automatically learns features from a dataset of voltage and current Signals, on the basis of which a framework for fault detection and classification is created. Convolutional feature mapping and mean pooling are implemented in order to generate feature vectors with local translation-invariance for half-cycle multi-Channel Signal segments. Fault detection and classification are achieved by a softmax classifier using the feature vectors. Further, the proposed method is tested under different sampling frequencies and Signal types. The generalizability of the proposed method is also verified by adding noise and measurement errors to the data. Results show that the proposed method is fast and accurate in detecting and classifying faults, and is practical for online transmission line protection for its high robustness and generalizability.
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detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder
Power and Energy Society General Meeting, 2017Co-Authors: Kunjin Chen, Jun Hu, Jinliang HeAbstract:We present in this paper a novel method for fault detection and classification in power transmission lines based on convolutional sparse autoencoder (CSAE). In contrary to conventional methods, the proposed method automatically learns features from a dataset of voltage and current Signals, on the basis of which a framework for fault detection and classification is created. Convolutional feature mapping and mean pooling are implemented in order to generate feature vectors with local translation-invariance for half-cycle multi-Channel Signal segments. Detection and classification are done by a softmax classifier using the feature vectors. Further, the proposed method is tested under different sampling frequencies and Signal types. Results show that the proposed method is fast and accurate in detecting and classifying faults, and is practical for online transmission line protection.
Gang Liu - One of the best experts on this subject based on the ideXlab platform.
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a novel blind source separation method for single Channel Signal
Signal Processing, 2010Co-Authors: Qinbo Jiang, Zhiqiang Liu, Gang LiuAbstract:The blind separation of single-Channel Signal is one of the most important aspects in many fields. Our research is carried out to develop a blind separation method of single-Channel Signal, in which the singular spectrum analysis (SSA) and blind source separation (BSS) techniques are jointly used, i.e. the single-Channel Signal is firstly changed into pseudo-MIMO (multi-input and multi-output) mode, and then each source Signal is separated via a fast BSS algorithm. A Signal preprocessing procedure, which is mainly focused on testing the nonstationarity of single-Channel Signal, is conducted before the operations of mixed Signal transform and separation. In this research, the approach of heuristic segmentation of a nonstationary time-series is proposed. Throughout the experiment, the effectiveness of the proposed method is validated with a data set taken from a digital wideband receiver in an outdoor test. Then, a comparison is made between the proposed method and the Hilbert-Huang transform (HHT)-based Signal separation method. The advantage of the proposed method is exhibited.
Kunjin Chen - One of the best experts on this subject based on the ideXlab platform.
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detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder
IEEE Transactions on Smart Grid, 2018Co-Authors: Kunjin Chen, Jun Hu, Jinliang HeAbstract:We present in this paper a novel method for fault detection and classification in power transmission lines based on convolutional sparse autoencoder. Contrary to conventional methods, the proposed method automatically learns features from a dataset of voltage and current Signals, on the basis of which a framework for fault detection and classification is created. Convolutional feature mapping and mean pooling are implemented in order to generate feature vectors with local translation-invariance for half-cycle multi-Channel Signal segments. Fault detection and classification are achieved by a softmax classifier using the feature vectors. Further, the proposed method is tested under different sampling frequencies and Signal types. The generalizability of the proposed method is also verified by adding noise and measurement errors to the data. Results show that the proposed method is fast and accurate in detecting and classifying faults, and is practical for online transmission line protection for its high robustness and generalizability.
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detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder
Power and Energy Society General Meeting, 2017Co-Authors: Kunjin Chen, Jun Hu, Jinliang HeAbstract:We present in this paper a novel method for fault detection and classification in power transmission lines based on convolutional sparse autoencoder (CSAE). In contrary to conventional methods, the proposed method automatically learns features from a dataset of voltage and current Signals, on the basis of which a framework for fault detection and classification is created. Convolutional feature mapping and mean pooling are implemented in order to generate feature vectors with local translation-invariance for half-cycle multi-Channel Signal segments. Detection and classification are done by a softmax classifier using the feature vectors. Further, the proposed method is tested under different sampling frequencies and Signal types. Results show that the proposed method is fast and accurate in detecting and classifying faults, and is practical for online transmission line protection.
Shaojiang Dong - One of the best experts on this subject based on the ideXlab platform.
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single Channel bearing vibration Signal blind source separation method based on morphological filter and optimal matching pursuit mp algorithm
Journal of Vibration and Control, 2015Co-Authors: Xiaoan Chen, Shaojiang DongAbstract:A novel method to solve the single-Channel source separation problem is proposed, which is based on a morphological filter to remove noise and the optimal matching pursuit (MP) algorithm to create pseudo sources. The Signal is first purified by the morphological filter. Then, the purified Signal is decomposed by the MP method, the Gabor atoms of the MP are selected by genetic algorithm in order to be less time-consuming. The selected atoms and the purified Signal are combined into multi-dimensional Signals, and the underdetermined problem of single-Channel source separation is solved. The singular value decomposition method is used to estimate the number of new constructed Signals. Then, the fast independent component analysis algorithm is used to achieve the separation of the Signals. The proposed method is applied to the separation of the simulated Signals and the mixed faults of bearing, the results indicate that the method can well solve the single-Channel Signal separation problem.
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a repeated single Channel mechanical Signal blind separation method based on morphological filtering and singular value decomposition
Measurement, 2012Co-Authors: Shaojiang Dong, Baoping Tang, Yan ZhangAbstract:Abstract This paper proposes a repeated blind source separation (BSS) method based on morphological filtering and singular value decomposition (SVD) to separate the mixed sources from a single-Channel Signal. Firstly the Signal is de-noised by the morphological filter and, the noise which affects the accuracy of the separation is removed. Next, the purified Signal is reconstructed in phase space, and the SVD is applied to this matrix. After choosing the effective singular values, the inverse transform is applied to the revised Signal matrix. From this, the pseudo Signal can be obtained. The pseudo Signal and the purified original Signal are used to achieve the mixed sources separation through the fast independent component analysis (FastICA) algorithm. Then, the methods above are repeated in order to separate the weaker Signals. The analysis of simulation and practical application demonstrates that that proposed method shows a high level of separating performance of a single-Channel Signal.
Qinbo Jiang - One of the best experts on this subject based on the ideXlab platform.
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a novel blind source separation method for single Channel Signal
Signal Processing, 2010Co-Authors: Qinbo Jiang, Zhiqiang Liu, Gang LiuAbstract:The blind separation of single-Channel Signal is one of the most important aspects in many fields. Our research is carried out to develop a blind separation method of single-Channel Signal, in which the singular spectrum analysis (SSA) and blind source separation (BSS) techniques are jointly used, i.e. the single-Channel Signal is firstly changed into pseudo-MIMO (multi-input and multi-output) mode, and then each source Signal is separated via a fast BSS algorithm. A Signal preprocessing procedure, which is mainly focused on testing the nonstationarity of single-Channel Signal, is conducted before the operations of mixed Signal transform and separation. In this research, the approach of heuristic segmentation of a nonstationary time-series is proposed. Throughout the experiment, the effectiveness of the proposed method is validated with a data set taken from a digital wideband receiver in an outdoor test. Then, a comparison is made between the proposed method and the Hilbert-Huang transform (HHT)-based Signal separation method. The advantage of the proposed method is exhibited.