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Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

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Chinese Journal of Aeronautics 2015年第2期28卷 488-498页

作者： Deng Sen Jing Bo Sheng Sheng Huang Yifeng Zhou Hongliang Aeronautics and Astronautics Engineering College Air Force Engineering University Unit 94371 of People's Liberation Army

Impulse components in vibration signals are important fault features of complex machines. sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding （FSC） and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.

关键词： Dictionary learning Fault detection Impulse feature extraction Information fusion sparse coding

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The HTM Spatial Pooler - A Neocortical Algorithm for Online sparse Distributed coding

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2017年 11卷 111页

作者： Cui, Yuwei Ahmad, Subutai Hawkins, Jeff Numenta Inc Redwood City CA 94063 USA

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

关键词： hierarchical temporal memory spatial pooler sparse coding competitive learning Hebbian learning online learning sparse distributed representations

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RETINAL VESSEL ENHANCEMENT USING MULTI-DICTIONARY AND sparse coding

RETINAL VESSEL ENHANCEMENT USING MULTI-DICTIONARY AND SPARSE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Bin Chen Yang Chen Zhuhong Shao Limin Luo Laboratory of Image Science and Technology Southeast University Nanjing China

ISBN: (纸本)9781479999897

A novel retinal vessel enhancement method based on multi-dictionary and sparse coding is proposed in this paper. Two dictionaries are utilized to gain the retinal vascular structures and details, one is the representation dictionary (RD) generated from the original retinal images, and another is the enhancement dictionary (ED) extracted from the corresponding label images. The proposed method represents the input image with RD to get the sparse coefficients via a sparse coding process. Then the enhanced retinal vessel image is obtained from the solved sparse coefficients and ED. Experimental results performed on the DRIVE and STARE databases indicate that the proposed method not only can effectively improve the image contrast but also enhance the details of the retinal vessels.

关键词： Image enhancement multi-dictionary retinal vessel sparse coding

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Research on the Primary Features of the Internet of Things System and the Corresponding Data Communication Characteristics based on sparse coding and Joint Deep Neural Network

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2016年第10期9卷 135-148页

作者： Pan, Jianping Huang, Wenzhun Zhang, Shanwen Taiyuan Satellite Launch Ctr Dept Technol Taiyuan 030027 Peoples R China Xijing Univ Dept Elect Informat Engn Xian 710123 Peoples R China

In this paper, we conduct research on issues related to the primary features of the Internet of things system and the corresponding data communication characteristics based on sparse coding and joint deep neural network. Internet of things is more than the underlying device difference communication method and it is the Internet of things needs to study in the field of hot issue. Using traditional algorithm for Internet communication equipment need particle filter was carried out on the acquisition of communication signal processing. Communication technology enables the Internet of things will perceive the information between different terminals for efficient transmission and exchange, exchange and sharing and the information resources is the key to the functions of things. To enhance the robustness and efficiency of the current IOT systems, we adopt the sparse coded dictionary learning theory to detect the size of the data and optimize the compressive sensing technique to modify the resolution. With the advances of the deep neural network, we analyze the topology of the system network structure and extract the pattern features and characteristics to make the signal transmission process more quickly and feasible. To enhance the objective function, we obtain the restricted optimization algorithm to help terminate the iteration for the higher efficiency. In the final part, we simulation our algorithm for times compared with other well-performed approaches. The result indicates that our method outperforms both in the accuracy layer an in the time-consuming layer which will hold specific meaning.

关键词： Internet of Things Data Communication sparse coding Deep Neural Network Signal Detection Compressive Sensing Feature Extraction Mathematical Optimization

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sparse coding Of Natural Images Using A Prior On Edge Co-Occurences 23

Sparse Coding Of Natural Images Using A Prior On Edge Co-Occ...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Perrinet, Laurent U. Bednar, James A. Aix Marseille Univ Inst Neurosci Timone CNRS Marseille France Univ Edinburgh Inst Adapt & Neural Computat Edinburgh EH8 9YL Midlothian Scotland

ISBN: (纸本)9780992862633

Oriented edges in images commonly occur in co-linear and co-circular arrangements, obeying the "good continuation law" of Gestalt psychology. The human visual system appears to exploit this property of images, with contour detection, line completion, and grouping performance well predicted by such an "association field" between edge elements [1, 2]. In this paper, we show that an association field of this type can he used to enhance the sparse representation of natural images. First, we define the sparseLets framework as an efficient representation of images based on a discrete wavelet transform. Second, we extract second-order information about edge co-occurrences from a set of images of natural scenes. Finally, we incorporate this prior information into our framework and show that it allows for the extraction of features relevant to natural scenes, like a round shape. This novel approach points the way to practical computer vision algorithms with human-like performance.

关键词： sparse coding natural scene statistics sparselets lateral connections association field

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Spectrum enhancement with sparse coding for robust speech recognition

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DIGITAL SIGNAL PROCESSING 2015年 43卷 59-70页

作者： He, Yongjun Sun, Guanglu Han, Jiqing Harbin Univ Sci & Technol Harbin 150080 Peoples R China Harbin Inst Technol Harbin 150001 Peoples R China

Recently, a trend in speech recognition is to introduce sparse coding for noise robustness. Although several methods have been proposed, the performance of sparse coding in speech denoising is not so optimistic. One assumption with sparse coding is that the representation of speech over the speech dictionary is sparse, while that of the noise is dense. This assumption is obviously not sustained in the speech denoising scenario. Many noises are also sparse over the speech dictionary. In such a condition, the representation of noisy speech still contains noise components, resulting in degraded performance. To solve this problem, we first analyze the assumption of sparse coding and then propose a novel method to enhance speech spectrum. This method first finds out the atoms which represent the noise sparsely, and then selectively ignores them in the reconstruction of speech to reduce the residual noise. Speech features are then extracted from the enhanced spectrum for speech recognition. Experimental results show that the proposed method can improve the noise robustness of a speech recognition system substantially. (C) 2015 Elsevier Inc. All rights reserved.

关键词： sparse coding Speech denoising Residual noise Basis pursuit denoising

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Iterative Semi-Supervised sparse coding Model for Image Classification

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2015年第1期81卷 99-110页

作者： Zheng, Haixia Ip, Horace H. S. City Univ Hong Kong Dept Comp Sci Ctr Innovat Applicat Internet & Multimedia Techno AIMtech Ctr Kowloon Hong Kong Peoples R China

The scarcity of labeled data and the high-dimensionality of multimedia data are the major obstacles for image classification. Due to these concerns, this paper proposes a novel algorithm, Iterative Semi-supervised sparse coding (ISSC), which jointly explores the advantages of both sparse coding and graph-based semi-supervised learning in order to learn discriminative sparse codes as well as an effective classification function. The ISSC algorithm fully exploits initial labels and the subsequently predicted labels for sparse codes learning. At the same time, during the graph-based semi-supervised learning stage, similarity matrix is firstly adjusted through the latest learned sparse codes, and then is utilized to obtain a better classification function. To make the ISSC scale up to larger databases, a novel online dictionary learning algorithm is also proposed to update the dictionary incrementally. In particular, by solving quadratic optimization, the ISSC approach can give rise to closed-form solutions for sparse codes and classification function, respectively. It has been extensively evaluated over three widely used datasets for image classification task. The experimental results in terms of classification accuracy demonstrate the proposed ISSC approach can achieve significant performance improvements with respect to the state-of-the-arts.

关键词： Image classification sparse coding Graph-based semi-supervised learning Incremental dictionary learning

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An adaptive bimodal recognition framework using sparse coding for face and ear

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PATTERN RECOGNITION LETTERS 2015年 53卷 69-76页

作者： Huang, Zengxi Liu, Yiguang Li, Xuwei Li, Jie Xihua Univ Sch Math & Comp Engn Chengdu 610039 Peoples R China Sichuan Univ Coll Comp Sci Vis & Image Proc Lab Chengdu 610065 Peoples R China

In this paper, we propose an adaptive face and ear based bimodal recognition framework using sparse coding, namely ABSRC, which can effectively reduce the adverse effect of degraded modality. A unified and reliable biometric quality measure based on sparse coding is presented for both face and ear, which relies On the collaborative representation by all classes. For adaptive feature fusion, a flexible piecewise function is carefully designed to select feature weights based on their qualities. ABSRC utilizes a two-phase sparse coding strategy. At first, face and ear features are separately coded on their associated dictionaries for individual quality assessments. Secondly, the weighted features are concatenated to form a unique feature vector, which is then coded and classified in multimodal feature space. Experiments demonstrate that ABSRC achieves quite encouraging robustness against image degeneration, and outperforms many up-to-date methods. Very impressively, even when query sample of one modality is extremely degraded by random pixel corruption, illumination variation, etc., ABSRC can still get performance comparable to the unimodal recognition based on the other modality. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multimodal recognition sparse coding Biometric quality assessment Adaptive feature fusion

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Noise-robust semi-supervised learning via fast sparse coding

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PATTERN RECOGNITION 2015年第2期48卷 605-612页

作者： Lu, Zhiwu Wang, Liwei Renmin Univ China Sch Informat Beijing 100872 Peoples R China Peking Univ Sch EECS Key Lab Machine Percept MOE Beijing 100871 Peoples R China

This paper presents a novel noise-robust graph-based semi-supervised learning algorithm to deal with the challenging problem of semi-supervised learning with noisy initial labels. Inspired by the successful use of sparse coding for noise reduction, we choose to give new L-1-norm formulation of Laplacian regularization for graph-based semi-supervised learning. Since our L-1-norm Laplacian regularization is explicitly defined over the eigenvectors of the normalized Laplacian matrix, we formulate graph-based semi-supervised learning as an L-1-norm linear reconstruction problem which can be efficiently solved by sparse coding. Furthermore, by working with only a small subset of eigenvectors, we develop a fast sparse coding algorithm for our L-1-norm semi-supervised learning. Finally, we evaluate the proposed algorithm in noise-robust image classification. The experimental results on several benchmark datasets demonstrate the promising performance of the proposed algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Graph-based semi-supervised learning Noise reduction Laplacian regularization sparse coding Noise-robust image classification

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Automated Depression Diagnosis Based on Facial Dynamic Analysis and sparse coding

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2015年第7期10卷 1432-1441页

作者： Wen, Lingyun Li, Xin Guo, Guodong Zhu, Yu W Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

Depression is a severe psychiatric disorder preventing a person from functioning normally in both work and daily lives. Currently, diagnosis of depression requires extensive participation from clinical experts. It has drawn much attention to develop an automatic system for efficient and reliable diagnosis of depression. Under the influence of depression, visual-based behavior disorder is readily observable. This paper presents a novel method of exploring facial region visual-based nonverbal behavior analysis for automatic depression diagnosis. Dynamic feature descriptors are extracted from facial region subvolumes, and sparse coding is employed to implicitly organize the extracted feature descriptors for depression diagnosis. Discriminative mapping and decision fusion are applied to further improve the accuracy of visual-based diagnosis. The integrated approach has been tested on the AVEC2013 depression database and the best visual-based mean absolute error/root mean square error results have been achieved.

关键词： Depression nonverbal behavior automatic diagnosis dynamic feature descriptor local phase quantization histograms from three orthogonal planes sparse coding discriminative mapping fusion support vector regression

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