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    Wyświetlanie 1-6 z 6
    Tytuł:
    Large-scale hyperspectral image compression via sparse representations based on online learning
    Autorzy:
    Ülkü, İ.
    Kizgut, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/331241.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    hyperspectral imaging
    compression algorithm
    dictionary learning
    sparse coding
    obrazowanie wielospektralne
    algorytm kompresji
    nauczanie online
    kodowanie rzadkie
    Opis:
    In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 1; 197-207
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A proximal-based algorithm for piecewise sparse approximation with application to scattered data fitting
    Autorzy:
    Zhong, Yijun
    Li, Chongjun
    Li, Zhong
    Duan, Xiaojuan
    Powiązania:
    https://bibliotekanauki.pl/articles/2172132.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    piecewise sparse approximation
    proximal gradient
    scattered data fitting
    aproksymacja rzadka
    gradient proksymalny
    dopasowanie danych
    Opis:
    In some applications, there are signals with a piecewise structure to be recovered. In this paper, we propose a piecewise sparse approximation model and a piecewise proximal gradient method (JPGA) which aim to approximate piecewise signals. We also make an analysis of the JPGA based on differential equations, which provides another perspective on the convergence rate of the JPGA. In addition, we show that the problem of sparse representation of the fitting surface to the given scattered data can be considered as a piecewise sparse approximation. Numerical experimental results show that the JPGA can not only effectively fit the surface, but also protect the piecewise sparsity of the representation coefficient.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 671--682
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes
    Autorzy:
    Huang, Lei
    Ren, Hao
    Chai, Yi
    Qu, Jianfeng
    Powiązania:
    https://bibliotekanauki.pl/articles/1838177.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    fault detection
    sparse autoencoder
    sparse restricted Boltzmann machine
    hybrid industrial processes
    detekcja błędów
    autoenkoder
    proces przemysłowy
    Opis:
    This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid industrial processes. The method is mainly built on the basis of a sparse auto-encoder and a sparse restricted Boltzmann machine (SAE-SRBM), so as to take advantages of their adaptive extraction and fusion on strong nonlinear symptoms. In the present work, SAEs are employed to reconstruct inputs and accomplish feature extraction by unsupervised mode, and their outputs present a knotty problem of an unknown probability distribution. In order to solve it, SRBMs are naturally used to fuse these unknown probability distribution features by transforming them into energy characteristics. The contribution of this method is the capability of further mining and learning of nonlinear features without considering the nonstationary problem. Also, this paper introduces a method of constructing labeled and unlabeled training samples while maintaining time series features. Unlabeled samples can be adopted to train the part for feature extraction and fusion, while labeled samples can be used to train the classification part. Finally, a simulation on the Tennessee Eastman process is carried out to demonstrate the effectiveness and excellent performance on fault detection for nonstationary and stationary hybrid industrial processes.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 29-43
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes
    Autorzy:
    Huang, Lei
    Ren, Hao
    Chai, Yi
    Qu, Jianfeng
    Powiązania:
    https://bibliotekanauki.pl/articles/1838184.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    fault detection
    sparse autoencoder
    sparse restricted Boltzmann machine
    hybrid industrial processes
    detekcja błędu
    autoenkoder
    proces przemysłowy
    Opis:
    This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid industrial processes. The method is mainly built on the basis of a sparse auto-encoder and a sparse restricted Boltzmann machine (SAE-SRBM), so as to take advantages of their adaptive extraction and fusion on strong nonlinear symptoms. In the present work, SAEs are employed to reconstruct inputs and accomplish feature extraction by unsupervised mode, and their outputs present a knotty problem of an unknown probability distribution. In order to solve it, SRBMs are naturally used to fuse these unknown probability distribution features by transforming them into energy characteristics. The contribution of this method is the capability of further mining and learning of nonlinear features without considering the nonstationary problem. Also, this paper introduces a method of constructing labeled and unlabeled training samples while maintaining time series features. Unlabeled samples can be adopted to train the part for feature extraction and fusion, while labeled samples can be used to train the classification part. Finally, a simulation on the Tennessee Eastman process is carried out to demonstrate the effectiveness and excellent performance on fault detection for nonstationary and stationary hybrid industrial processes.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 29-43
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A parallel block Lanczos algorithm and its implementation for the evaluation of some eigenvalues of large sparse symmetric matrices on multicomputers
    Autorzy:
    Guarracino, M. R.
    Perla, F.
    Zanetti, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/908413.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    cluster architecture
    symmetric block Lanczos algorithm
    sparse matrices
    parallel eigensolver
    algorytm Lanczosa
    macierze rzadkie
    architektura klastrowa
    Opis:
    In the present work we describe HPEC (High Performance Eigenvalues Computation), a parallel software package for the evaluation of some eigenvalues of a large sparse symmetric matrix. It implements an efficient and portable Block Lanczos algorithm for distributed memory multicomputers. HPEC is based on basic linear algebra operations for sparse and dense matrices, some of which have been derived by ScaLAPACK library modules. Numerical experiments have been carried out to evaluate HPEC performance on a cluster of workstations with test matrices from Matrix Market and Higham’s collections. A comparison with a PARPACKroutine is also detailed. Finally, parallel performance is evaluated on random matrices, using standard parameters.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2006, 16, 2; 241-249
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Two implementations of the preconditioned conjugate gradient method on heterogeneous computing grids
    Autorzy:
    Collignon, T. P.
    Van Gijzen, M. B.
    Powiązania:
    https://bibliotekanauki.pl/articles/907778.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    przetwarzanie siatkowe
    system liniowy
    metoda iteracyjna
    gradient sprzężony
    przepływ pęcherzykowy
    grid computing
    large sparse linear systems
    iterative methods
    conjugate gradient method
    Chronopoulos/Gear CG
    GridSolve middleware
    bubbly flows
    Opis:
    Efficient iterative solution of large linear systems on grid computers is a complex problem. The induced heterogeneity and volatile nature of the aggregated computational resources present numerous algorithmic challenges. This paper describes a case study regarding iterative solution of large sparse linear systems on grid computers within the software constraints of the grid middleware GridSolve and within the algorithmic constraints of preconditioned Conjugate Gradient (CG) type methods. We identify the various bottlenecks induced by the middleware and the iterative algorithm. We consider the standard CG algorithm of Hestenes and Stiefel, and as an alternative the Chronopoulos/Gear variant, a formulation that is potentially better suited for grid computing since it requires only one synchronisation point per iteration, instead of two for standard CG. In addition, we improve the computation-to-communication ratio by maximising the work in the preconditioner. In addition to these algorithmic improvements, we also try to minimise the communication overhead within the communication model currently used by the GridSolve middleware. We present numerical experiments on 3D bubbly flow problems using heterogeneous computing hardware that show lower computing times and better speed-up for the Chronopoulos/Gear variant of conjugate gradients. Finally, we suggest extensions to both the iterative algorithm and the middleware for improving granularity.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2010, 20, 1; 109-121
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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