In this paper, based on the partial discharge ultrasonic signals of four typical discharge types of needle plate discharge, internal discharge, suspension discharge and creeping discharge in the switchgear, the time-f...
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ISBN:
(纸本)9781728143903
In this paper, based on the partial discharge ultrasonic signals of four typical discharge types of needle plate discharge, internal discharge, suspension discharge and creeping discharge in the switchgear, the time-frequency diagram of the ultrasonic signal is obtained by using the short-time Fourier transform, and the time-frequency is used by the sparserepresentation algorithm. The classification is performed to quickly and accurately determine whether a partial discharge has occurred and to determine which type of discharge it belongs to. In the process of using the sparse representation method, the orthogonal matching pursuit method and the accelerated near-end gradient method are used to solve the sparse solution respectively. The conditions applicable to the two methods are illustrated by experiments.
Focused on the issue that the object structure discontinuity and poor texture detail occurred in image inpainting method, the image inpainting algorithm based on self-adaptive group structure has proposed in this pape...
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Focused on the issue that the object structure discontinuity and poor texture detail occurred in image inpainting method, the image inpainting algorithm based on self-adaptive group structure has proposed in this paper. The conception of self-adaptive group structure is different from traditional image patching operation and fixed group structure, which refers to the fact that a patch on the structure has fewer similar patches than the one within the textured region. A self-adaptive dictionary as well as the sparserepresentation model was established in the domain of self-adaptive group. Finally, the target cost function was solved by Split Bregman Iterational operation. The experimental results on target removing with Criminisi's algorithm, GSR's algorithm and SALSA's algorithm in image pixels losting of image inpainting had shown that the proposed algorithm has better performance than other algorithms.
Space-frequency modulation (SFM) signal is a potential waveform for multifunction radar with high degrees of freedom of space, time, and frequency. However, the introduction of the additional communication function wi...
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Space-frequency modulation (SFM) signal is a potential waveform for multifunction radar with high degrees of freedom of space, time, and frequency. However, the introduction of the additional communication function will modulate the transmitting signal, which will severely deteriorate the autocorrelation function (ACF). Here, a modified CLEAN method is proposed to eliminate the influence of undesired sidelobes in ACF on the air target detection. In the proposed method, undesired sidelobes are treated as extra features of real target to obtain more precise estimation of its complex reflection coefficient. Moreover, by considering about the sparsity of air targets, the authors employ the sparse representation method to estimate the response of the current strongest target. Then the target occlusion is eliminated by iterative cancellation. Simulation results demonstrate that the proposed detector is reliable and effective for the SFM-based integrated system.
Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and pos...
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Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition(FPPR) model and the dualdictionary sparserepresentation classification(DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory(ORL) and face recognition technology(FERET) databases, and also reflect comparisons with SRC, linear regression classification(LRC), and two-phase test sample sparserepresentation(TPTSSR).
Feature selection and fusion is of crucial importance in multi-feature visual tracking. This study proposes a multi-task kernel-based sparse learning method for multi-feature visual tracking. The proposed sparse learn...
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Feature selection and fusion is of crucial importance in multi-feature visual tracking. This study proposes a multi-task kernel-based sparse learning method for multi-feature visual tracking. The proposed sparse learning method can discriminate the reliable and unreliable features for optimal multi-feature fusion through using a Fisher discrimination criterion-based multi-objective model to adaptively train the kernel weights of different features such as pixel intensity, edge and texture. To guarantee a robustness of the sparse representation method, a mixed norm is employed in the sparse leaning method to adaptively select correlated particle observations for multi-task sparse reconstruction. Experimental results show that the proposed sparse learning method can achieve a better tracking performance than state-of-the-art tracking methods do.
Using the original and 'symmetrical face' training samples to perform representation based face recognition was first proposed in [1]. It simultaneously used the original and 'symmetrical face' trainin...
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Using the original and 'symmetrical face' training samples to perform representation based face recognition was first proposed in [1]. It simultaneously used the original and 'symmetrical face' training samples to perform a two-step classification and achieved an outstanding classification result. However, in [1] the "symmetrical face" is devised only for one method. In this paper, we do some improvements on the basis of [1] and combine this "symmetrical faces" transformation with several representation based methods. We exploit all original training samples, left "symmetrical face" training samples and right "symmetrical face" training samples for classification and use the score fusion for ultimate face recognition. The symmetry of the face is first used to generate new samples, which is different from original face image but can really reflect some possible appearance of the face. It effectively overcomes the problem of non-sufficient training samples. The experimental results show that the proposed scheme can be used to improve a number of traditional representation based methods including those that are not presented in the paper. (C) 2014 Published by Elsevier GmbH.
A limited number of available training samples have become one bottleneck of face recognition. In real-world applications, the face image might have various changes owing to varying illumination, facial expression and...
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A limited number of available training samples have become one bottleneck of face recognition. In real-world applications, the face image might have various changes owing to varying illumination, facial expression and poses. However, non-sufficient training samples cannot comprehensively convey these possible changes, so it is hard to improve the accuracy of face recognition. In this paper, we propose to exploit the symmetry of the face to generate new samples and devise a representation based method to perform face recognition. The new training samples really reflect some possible appearance of the face. The devised representation based method simultaneously uses the original and new training samples to perform a two-step classification, which ultimately uses a small number of classes that are 'near' to the test sample to represent and classify it and has a similar advantage as the sparse representation method. This method also takes advantages of the score level fusion, which has proven to be very competent and usually performs better than the decision level and feature level fusion. The experimental results show that the proposed method outperforms state-of-the-art face recognition methods including the sparserepresentation classification (SRC), linear regression classification (LRC), collaborative representation (CR) and two-phase test sample sparserepresentation (TPTSSR). (C) 2012 Elsevier Ltd. All rights reserved.
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