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An Efficient Approach for Fault Detection, Isolation, and Data Recovery of Self-Validating Multifunctional Sensors

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2017年第3期66卷 543-558页

作者： Yang, Jingli Chen, Yinsheng Zhang, Lili Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China Harbin Univ Commerce Coll Basic Sci Harbin 150001 Peoples R China

A novel fault detection, isolation, and data recovery (FDIR) approach for self-validating multifunctional sensors is presented in this paper. To improve the fault detection accuracy under multiple steady conditions for multifunctional sensors, a sparse non-negative matrix factorization (SNMF)-based model is employed to accomplish fault detection through a combination of newly proposed C-2 and squared prediction error (SPE) statistics. Furthermore, a self-adaptive multiple-variable reconstruction strategy (SMVR) is proposed to achieve high accuracy on multiple-fault isolation and data recovery for faulty sensitive units. The performance of the proposed approach is fully verified in a real experimental system for self-validating multifunctional sensors, and it is compared with those of other fault detection models, such as principal component analysis (PCA), non-negative matrix factorization (NMF), and fault isolation algorithms, such as PCA-based contribution plots and SNMFbased contribution plots. The experimental results demonstrate that the proposed approach provides an excellent solution to the FDIR of self-validating multifunctional sensors.

关键词： Data recovery fault detection and isolation self-adaptive multiple-variable reconstruction self-validating multifunctional sensors sparse non-negative matrix factorization

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Spatio-spectral fusion of satellite images based on dictionary-pair learning

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INFORMATION FUSION 2014年第0期18卷 148-160页

作者： Song, Huihui Huang, Bo Zhang, Kaihua Zhang, Hankui Chinese Univ Hong Kong Dept Geog & Resource Management Hong Kong Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. By combining the spectral information from sensors with low spatial resolution but high spectral resolution (LSHS) and the spatial information from sensors with high spatial resolution but low spectral resolution (HSLS), this method aims to generate fused data with both high spatial and spectral resolution. Based on the sparse non-negative matrix factorization technique, this method first extracts spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatial unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, fused data are finally derived which are characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data. The experiments are carried out by comparing the proposed method with two representative methods on both simulation data and actual satellite images, including the fusion of Landsat/ETM+ and Aqua/MODIS data and the fusion of EO-1/Hyperion and SPOT5/HRG multispectral images. By visually comparing the fusion results and quantitatively evaluating them in term of several measurement indices, it can be concluded that the proposed method is effective in preserving both the spectral information and spatial details and performs better than the comparison approaches. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Spatio-spectral fusion High spatial resolution High spectral resolution Dictionary-pair learning sparse non-negative matrix factorization

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An Inertial Block Majorization Minimization Framework for nonsmooth nonconvex Optimization

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JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1-41页

作者： Hien, Le Thi Khanh Phan, Duy Nhat Gillis, Nicolas Univ Mons Mons Belgium Univ Massachusetts Dept Math & Stat Lowell MA USA Univ Mons Dept Math & Operat Res Mons Belgium

In this paper, we introduce TITAN, a novel inerTIal block majorizaTion minimizAtioN framework for nonsmooth nonconvex optimization problems. To the best of our knowledge, TITAN is the first framework of block-co ordinate update method that relies on the majorization-minimization framework while embedding inertial force to each step of the block updates. The inertial force is obtained via an extrapolation operator that subsumes heavy-ball and Nesterov-type accelerations for block proximal gradient methods as special cases. By choosing various surrogate functions, such as proximal, Lipschitz gradient, Bregman, quadratic, and composite surrogate functions, and by varying the extrapolation operator, TITAN produces a rich set of inertial block-co ordinate update methods. We study sub-sequential convergence as well as global convergence for the generated sequence of TITAN. We illustrate the effectiveness of TITAN on two important machine learning problems, namely sparse non-negative matrix factorization and matrix completion.

关键词： inertial method block coordinate method majorization minimization sur-rogate functions sparse non-negative matrix factorization matrix completion

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Semi-supervised transient noise suppression using OMLSA and SNMF algorithms

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APPLIED ACOUSTICS 2020年 170卷 107533-107533页

作者： Ullah, Rizwan Islam, Md Shohidul Ye, Zhongfu Asif, Muhammad Univ Sci & Technol China Dept Elect Engn & Informat Sci Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China Islamic Univ Dept Comp Sci & Engn Kushtia 7003 Bangladesh City Univ Sci & Informat Technol Dept Elect Engn Peshawar Kpk Pakistan

Transient interferences such as keystrokes, mouse clicks and hammering pose a significant challenge in the single channel speech enhancement due to their abrupt and non-continuous nature. Traditional noise suppression algorithms and even many non-stationary noise reduction algorithms do not adequately suppress transient interference. Therefore, in this work, we propose a semi-supervised single channel transient noise suppression method to effectively suppress the transient interference without significant audible distortion. The proposed algorithm consists of training and testing stages. In the training stage, the proposed technique first uses the optimally modified-log spectral amplitude (OMLSA) estimator to estimate the transient noise from the noisy speech signal. After that, we eliminate the residual speech components from the estimated noise obtained from OMLSA based on the correlation coefficient, by taking correlation between the estimated noise with the available clean speech data from the dataset passed through the voice activity detector for silence zones removal. Afterwards, we use this noise for training the noise dictionary in sparse non-negative matrix factorization. Clean speech data is used for speech dictionary training. In the enhancement stage, the dictionaries are fixed and concatenated, to obtain the corresponding activation matrices. The clean speech dictionary and the corresponding weight matrix are used to reconstruct the estimated speech. The experimental results reveal that the proposed algorithm provided better performance compared to other existing algorithms in the speech quality evaluation metrics. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Transient Noise Noise Suppression Optimally Modified-Log Spectral Amplitude Estimation sparse non-negative matrix factorization Voice Activity Detection

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Genome-Wide SNPs Identify Limits to Connectivity in the Extreme Freshwater Disperser, Spangled Perch Leiopotherapon unicolor (Terapontidae)

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JOURNAL OF HEREDITY 2018年第3期109卷 320-325页

作者： Schmidt, Daniel J. Huey, Joel A. Hughes, Jane M. Griffith Univ Australian Rivers Inst Nathan Qld 4111 Australia Western Australian Museum Terr Zool & Mol Systemat Unit 49 Kew St Welshpool WA 6986 Australia

The utility of restriction-site associated DNA sequencing (RADseq) to resolve fine-scale population structure was tested on an abundant and vagile fish species in a tropical river. Australia's most widespread freshwater fish, the "extreme disperser" Leiopotherapon unicolor was sampled from 6 locations in an unregulated system, the Daly River in Australia's Northern Territory. Despite an expectation of high connectivity based on life history knowledge of this species derived from arid zone habitats, L. unicolor was not a panmictic population in the tropical lower Daly. Using similar to 14 000 polymorphic RADseq loci, we found a pattern of upstream versus downstream population subdivision and evidence for differentiation among tributary populations. The magnitude of population structure was low with narrow confidence intervals (global F-ST = 0.014;95% CI = 0.012-0.016). Confidence intervals around pairwise F-ST estimates were all nonzero and consistent with the results of clustering analyses. This population structure was not explained by spatially heterogeneous selection acting on a subset of loci, or by sampling groups of closely related individuals (average within-site relatedness approximate to 0). One implication of the low but significant structure observed in the tropics is the possibility that L. unicolor may exhibit contrasting patterns of migratory biology in tropical versus arid zone habitats. We conclude that the RADseq revolution holds promise for delineating subtle patterns of population subdivision in species characterized by high within-population variation and low among-population differentiation.

关键词： DAPC F-ST outlier outflank RADseq sNMF sparse non-negative matrix factorization

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Polarization Image Fast Fusion Method Based on Online Dictionary Learning

Polarization Image Fast Fusion Method Based on Online Dictio...

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5th International Symposium on Photoelectronic Detection and Imaging (ISPDI) - Imaging Spectrometer Technologies and Applications

作者： Xu Guo-ming Xue Mo-gen Yuan Guang-lin Zhou Pu-cheng Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China

ISBN: (纸本)9780819497796

In this paper we propose a polarization image fast fusion approach based on online dictionary learning for sparse non-negative matrix factorization, aiming at improving the efficiency of fusion methods for polarization image based on non-negative matrix factorization. Firstly, all of the polarization parameter images are taken as source data sets for sparse non-negative matrix factorization using online dictionary learning algorithm, so as to extract three feature basis images. Then, after histogram matching, the three feature basis images are mapped into three color channels of IHS color space. Finally, the fused image is achieved via the transform from IHS to RGB color model. Experimental results show that, the proposed method not only has better capacity of color representation capability and effectively pop out detailed information of objects but enhances the running efficiency evidently as well.

关键词： image fusion polarization image online dictionary learning sparse non-negative matrix factorization

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Learning Part-based Dictionary by sparse NMF for Face Gender Recognition 8

Learning Part-based Dictionary by Sparse NMF for Face Gender...

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8th International Symposium on Computational Intelligence and Design (ISCID)

作者： Ge, Jiali Zhou, Tong Zhang, Fan Tse, Ken Nanjing Tech Univ Sch Control Sci & Elect Engn Nanjing Jiangsu Peoples R China Xidian Univ Dept Comp Sci & Technol Xian Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian Peoples R China Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

ISBN: (纸本)9781467395878

Face gender recognition is a very challenging problem in computer vision, which plays an important role in many visual applications. In this paper, we present a framework that combines the unsupervised dictionary learning and supervised classifier training together to this gender recognition problem. We firstly apply sparse non-negative matrix factorization (sparse NMF) to learn intrinsic part-based dictionary from face images in an unsupervised manner. After that we encode all the data by the learned dictionary, and train a SVM or logistic regression classifier in a supervised manner on those representations. Our experimental results show that the learned dictionaries by sparse NMF can not only capture meaningful features from the faces, but also boost the performance of the subsequent classifier in terms of classification accuracies and speeds.

关键词： face gender reconition sparse non-negative matrix factorization SVM logistic regression dictionary learning

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TOPIC EXTRACTION FROM DIVIDED DOCUMENT SETS

TOPIC EXTRACTION FROM DIVIDED DOCUMENT SETS

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5th International Conference on Web Information Systems and Technologies

作者： Yokoi, Takeru Yanagimoto, Hidekazu Tokyo Metropolitan Coll Ind Technol Tokyo Japan Osaka Perfecture Univ Dept Engn Osaka Japan

ISBN: (纸本)9789898111814

We propose here a method to extract topics from a large document set with the topics included in its divisions and the combination of them. In order to extract topics, the sparse non-negative matrix factorization that imposes sparse constrain only to a basis matrix, which we call SNMF/L, is applied to document sets. It is useful to combine the topics from some small document sets since if the number of documents is large, the procedure of topic extraction with the SNMF/L from a large corpus takes a long time. In this paper, we have shortened the procedure time for the topic extraction from a large document set with the combining topics that are extracted from respective divided document set. In addition, an evaluation of our proposed method has been carried out with the corresponding topics between the combined topics and the topics from the large document set by the SNMF/L directly, and the procedure times of the SNMF/L.

关键词： Topic extraction sparse non-negative matrix factorization Clustering

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A multi-channel UNet framework based on SNMF-DCNN for robust heart-lung-sound separation

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期164卷 107282-107282页

作者： Wang, Weibo Qin, Dimei Wang, Shubo Fang, Yu Zheng, Yongkang Xihua Univ Coll Elect & Elect Informat Chengdu 610036 Peoples R China State Grid Sichuan Elect Power Res Inst Chengdu 610096 Peoples R China

Cardiopulmonary and cardiovascular diseases are fatal factors that threaten human health and cause many deaths worldwide each year, so it is essential to screen cardiopulmonary disease more accurately and efficiently. Auscultation is a non-invasive method for physicians' perception of the disease. The Heart Sounds (HS) and Lung Sounds (LS) recorded by an electronic stethoscope consist of acoustic information that is helpful in the diagnosis of pulmonary conditions. Still, inter-interference between HS and LS presented in both the time and frequency domains blocks diagnostic efficiency. This paper proposes a blind source separation (BSS)strategy that first classifies Heart-Lung-Sound (HLS) according to its LS features and then separates it into HS and LS. sparse non negative matrix factorization (SNMF) is employed to extract the LS features in HLS, then proposed a network constructed by Dilated Convolutional Neural Network (DCNN) to classify HLS into five types by the magnitude features of LS. Finally, Multi-Channel UNet (MCUNet) separation model is utilized for each category of HLS. This paper is the first to propose the HLS classification method SNMF-DCNN and apply UNet to the cardiopulmonary sound separation domain. Compared with other state-of-the-art methods, the proposed framework in this paper has higher separation quality and robustness.

关键词： Cardiovascular diseases Deep learning Convolutional neural network sparse non-negative matrix factorization UNet

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Unsupervised method for normalization of hematoxylin-eosin stain in histological images

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2019年 77卷 101646-000页

作者： Azevedo Tosta, Thaina A. de Faria, Paulo Rogerio Silva Servato, Joao Paulo Neves, Leandro Alves Roberto, Guilherme Freire Martins, Alessandro Santana do Nascimento, Marcelo Zanchetta Fed Univ ABC Ctr Math Comp & Cognit Av Estados 5001 BR-09210580 Santo Andre SP Brazil Univ Fed Uberlandia Inst Biomed Sci Dept Histol & Morphol Av Amazonas S-N BR-38405320 Uberlandia MG Brazil Univ Uberaba Sch Dent Av Nene Sabino 1801 BR-38055500 Uberaba MG Brazil Sao Paulo State Univ Dept Comp Sci & Stat R Cristovao Colombo 2265 BR-15054000 Sao Jose Do Rio Preto SP Brazil Univ Fed Uberlandia Fac Comp Sci Av Joao Naves de Avila 2121 BR-38400902 Uberlandia MG Brazil Fed Inst Triangulo Mineiro R Belarmino Vilela Junqueira S-N BR-38305200 Ituiutaba MG Brazil

Histological images stained with hematoxylin-eosin are widely used by pathologists for cancer diagnosis. However, these images can have color variations that highly influence the histological image processing techniques. To deal with this potential limitation, normalization methods are useful for color correction. In this paper, a histological image color normalization is presented by considering the biological and hematoxylin-eosin properties. To this end, the stain representation of a reference image was applied in place of the original images representation, allowing the preservation of histological structures. This proposal was evaluated on histological images with great variations of contrast, and both visual and quantitative analyzes yielded promising results. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Histopathological image analysis Normalization Spectral matching sparse non-negative matrix factorization

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