Since roller bearing is one of the most vulnerable components, bearing faults usually occur in an unprepared situation with multiple faults, and the quantity of sensors is limited in the real-time working environment,...
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Since roller bearing is one of the most vulnerable components, bearing faults usually occur in an unprepared situation with multiple faults, and the quantity of sensors is limited in the real-time working environment, resulting in an underdetermined blind source separation (UBSS) problem to extract the fault features. Because the collected signals are usually not independent and not sparse enough, traditional methods of separating signals cannot perform well. In this paper, an optimized intrinsic characteristic-scale decomposition (OICD) method is proposed to solve the underdetermined problem. Meanwhile, the constraint error factor is introduced to overcome the drawback that the ideal ending condition of ICD is not proper for the vibration signal of bearing. In addition, given that non-negativematrixfactorization (NMF) is not limited by the source signal independence and sparsity, an improved UBSS model is constructed, and the PCs are used as the input matrix of local NMF to obtain the separation signal. Ultimately, envelope analysis is utilized to detect the source signal feature. Both simulated and experimental vibration signals are used to verify the effectiveness of the proposed approach. Besides, the traditional method is juxtaposed with the suggested method. The results indicate that the proposed method is effective in dealing with the compound faults separation of the rotating machinery.
With the popularization of media-capture devices and the development of the Internet's basic facilities, video has become the most popular media information in recent years. The massive capacity of video imposes t...
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With the popularization of media-capture devices and the development of the Internet's basic facilities, video has become the most popular media information in recent years. The massive capacity of video imposes the demand of automatic video identification techniques which are very important to various applications such as content based video retrieval and copy detection. Therefore, as a challenging problem, video identification has drawn more and more attention in the past decade. The problem addressed here is to identify a given video clip in a given set of video sequences. In this paper, a robust video identification algorithm based on local non-negative matrix factorization (LNMF) is presented. First, some concepts about LNMF are described and the way of finding the factorized matrix is given. Then, its convergence is proven. In addition, a LNMF based shot detection method is proposed for constructing a video identification framework completely based on LNMF. Finally, a LNMF based identification approach using Hausdorff distance is introduced and a two-stage search process is proposed. Experimental results show the robustness of the proposed approach to many kinds of content-preserved distortions and its superiority to other algorithms. (C) 2014 Elsevier GmbH. All rights reserved.
Active appearance models (AAMs) have been widely used in many face modeling and facial feature extraction methods. One of the problems of AAMs is that it is difficult to model a sufficiently wide range of human facial...
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Active appearance models (AAMs) have been widely used in many face modeling and facial feature extraction methods. One of the problems of AAMs is that it is difficult to model a sufficiently wide range of human facial appearances, the pattern of intensities across a face image patch. Previous researches have used principal component analysis (PCA) for facial appearance modeling, but there has been little analysis and comparison between PCA and many other facial appearance modeling methods such as non-negativematrixfactorization (NMF), local NMF (LNMF), and non-smooth NMF (ns-NMF). The main contribution of this paper is to find a suitable facial appearance modeling method for AAMs by a comparative study. In the experiments, PCA, NMF, LNMF, and ns-NMF were used to produce the appearance model of the AAMs and the root mean square (RMS) errors of the detected feature points were analyzed using the AR and BERC face databases. Experimental results showed that (1) if the appearance variations of testing face images were relatively non-sparser than those of training face images, the non-sparse methods (PCA, NMF) based AAMs outperformed the sparse methods (nsNMF, LNMF) based AAMs. (2) If the appearance variations of testing face images are relatively sparser than those of training face images, the sparse methods (nsNMF) based AAMs outperformed the non-sparse methods (PCA, NMF) based AAMs. (C) 2009 Elsevier B.V. Ail rights reserved.
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