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Wyświetlanie 1-15 z 57
Tytuł:
Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
Autorzy:
Andrysiak, Tomasz
Saganowski, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/1373708.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
Tematy:
dictionary learning
sparse representation
anomaly detection
Opis:
In this article we present the use of sparse representation of a signal and incoherent dictionary learning method for the purpose of network traffic analysis. In learning process we use 1D INK-SVD algorithm to detect proper dictionary structure. Anomaly detection is realized by parameter estimation of the analyzed signal and its comparative analysis to network traffic profiles. Efficiency of our method is examined with the use of extended set of test traces from real network traffic. Received experimental results confirm effectiveness of the presented method.
Źródło:
Schedae Informaticae; 2015, 24; 63-71
0860-0295
2083-8476
Pojawia się w:
Schedae Informaticae
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Weak Saturation Numbers for Sparse Graphs
Autorzy:
Faudree, Ralph J.
Gould, Ronald J.
Jacobson, Michael S.
Powiązania:
https://bibliotekanauki.pl/articles/29787240.pdf
Data publikacji:
2013-09-01
Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:
saturated graphs
sparse graphs
weak saturation
Opis:
For a fixed graph F, a graph G is F-saturated if there is no copy of F in G, but for any edge e ∉ G, there is a copy of F in G + e. The minimum number of edges in an F-saturated graph of order n will be denoted by sat(n, F). A graph G is weakly F-saturated if there is an ordering of the missing edges of G so that if they are added one at a time, each edge added creates a new copy of F. The minimum size of a weakly F-saturated graph G of order n will be denoted by wsat(n, F). The graphs of order n that are weakly F-saturated will be denoted by wSAT(n, F), and those graphs in wSAT(n, F) with wsat(n, F) edges will be denoted by wSAT(n, F). The precise value of wsat(n, T) for many families of sparse graphs, and in particular for many trees, will be determined. More specifically, families of trees for which wsat(n, T) = |T|−2 will be determined. The maximum and minimum values of wsat(n, T) for the class of all trees will be given. Some properties of wsat(n, T) and wSAT(n, T) for trees will be discussed. Keywords: saturated graphs, sparse graphs, weak saturation.
Źródło:
Discussiones Mathematicae Graph Theory; 2013, 33, 4; 677-693
2083-5892
Pojawia się w:
Discussiones Mathematicae Graph Theory
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:
Recognition of the numbers in the Polish language
Autorzy:
Plichta, A.
Gąciarz, T.
Krzywdziński, T.
Powiązania:
https://bibliotekanauki.pl/articles/308844.pdf
Data publikacji:
2013
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
Automatic Speech Recognition
compressed sensing
Sparse Classification
Opis:
Automatic Speech Recognition is one of the hottest research and application problems in today’s ICT technologies. Huge progress in the development of the intelligent mobile systems needs an implementation of the new services, where users can communicate with devices by sending audio commands. Those systems must be additionally integrated with the highly distributed infrastructures such as computational and mobile clouds, Wireless Sensor Networks (WSNs), and many others. This paper presents the recent research results for the recognition of the separate words and words in short contexts (limited to the numbers) articulated in the Polish language. Compressed Sensing Theory (CST) is applied for the first time as a methodology of speech recognition. The effectiveness of the proposed methodology is justified in numerical tests for both separate words and short sentences.
Źródło:
Journal of Telecommunications and Information Technology; 2013, 4; 70-78
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efektywna metoda przetwarzania macierzy rzadkich
Effective method of processing of sparse matrix
Autorzy:
Jabłoński, J.
Turos, J.
Powiązania:
https://bibliotekanauki.pl/articles/152889.pdf
Data publikacji:
2007
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
kompresja
macierze rzadkie
compression
sparse matrix
Opis:
Wiele problemów optymalizacyjnych sprowadzanych jest do przetwarzania macierzy rzadkich o bardzo dużych rozmiarach. Tradycyjne metody operacji na macierzach o tak dużych rozmiarach jest czasochłonne dlatego poszukiwane są rozwiązania gwarantujące większą efektywność i krótszy czas przetwarzania. W artykule omówiono wybrane metody i zaproponowano autorską metodę wykorzystania kompresji w poprawie efektywności przetwarzania macierzy rzadkich.
Many optimization problems can be imported to processing of thin matrices about very large sizes. The traditional methods of operation on the matrices about so it is the large sizes time-consuming therefore the guaranteeing the larger efficiency solutions be in the demand and the shorter time of processing. This paper presented the author's method of utilization in improvement of efficiency the compression of processing of sparse matrix in presented study was introduced.
Źródło:
Pomiary Automatyka Kontrola; 2007, R. 53, nr 5, 5; 63-65
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
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:
The versatile hardware accelerator framework for sparse vector calculations
Autorzy:
Karwatowski, R.
Wiatr, K.
Powiązania:
https://bibliotekanauki.pl/articles/114705.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
FPGA
sparse vectors
cosine similarity
Zynq
hardware accelerator
Opis:
In this paper, we present the advantage of the ability of FPGAs to perform various computationally complex calculations using deep pipelining and parallelism. We propose an architecture that consists of many small stream processing blocks. The designed framework maintains proper data movement and synchronization. The architecture can be easily adapted to be implemented in FPGA devices of a various size and cost - from small SoC devices to high-end PCIe accelerator cards. It is capable to perform a selected operation on a sparse data that are loaded as the stream of vectors. As an example application, we have implemented the cosine similarity measure for the text similarity calculations that uses the TF-IDF weighting scheme. The presented example application calculates the similarity of texts from the set of input documents to documents from the large database. The scheme is used to find the most similar documents. The proposed design can decrease the service time of search queries in computer centers while reducing power consumption.
Źródło:
Measurement Automation Monitoring; 2015, 61, 7; 327-329
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Asymmetric double image encryption, compression and watermarking scheme based on orthogonal-triangular decomposition with column pivoting
Autorzy:
Anjana, Savita
Rakheja, Pankaj
Yadav, Ak
Singh, Phool
Singh, Hukum
Powiązania:
https://bibliotekanauki.pl/articles/2086764.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
QR decomposition
Fresnel transform
sparse matrix
asymmetric cryptosystem
Opis:
A novel asymmetric scheme for double image encryption, compression and watermarking based on QR decomposition in the Fresnel domain has been presented. The QR decomposition provides a permutation matrix as a ciphertext, and the product of orthogonal and triangular matrix as a key. The ciphertext obtained through this process is a sparse matrix that is compressed by the CSR method to give compressed encrypted data, which when combined with a host image, gives a watermarked image. Thus, a cryptosystem that involves compression and watermarking is proposed. The proposed scheme is validated for grayscale images. To check the efficacy of the proposed scheme, histograms, statistical parameters, and key sensitivity are analyzed. The scheme is also tested against various attacks. Numerical simulations are performed to validate the security of the scheme.
Źródło:
Optica Applicata; 2022, 52, 2; 283--295
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Równoległa implementacja metod iteracyjnych dla układu równań z macierzami rzadkimi
Parallel implementation of iterative methods for sparse linear systems
Autorzy:
Schubring, T.
Powiązania:
https://bibliotekanauki.pl/articles/250420.pdf
Data publikacji:
2013
Wydawca:
Instytut Naukowo-Wydawniczy TTS
Tematy:
metoda iteracyjna
macierze rzadkie
UPC
iterative method
sparse matrix
Opis:
W artykule podano równoległą implementację metod rozwiązywania układu równań liniowych z macierzą rzadką w języku programowania UPC (Unified Parallel C). Uwzględniono możliwości środowiska programistycznego Berkeley UPC oraz format spakowanych wierszy CSR (Compressed Sparse Row).
Paper described a parallel implementation of the iterative methods for solving linear equation systems with sparse matrix in the UPC programming language (Unified Parallel C). Possibilities of the Berkeley UPC development environment and the CSR packed rows format (Compressed Sparse Row) were included.
Źródło:
TTS Technika Transportu Szynowego; 2013, 10; 499-505, CD
1232-3829
2543-5728
Pojawia się w:
TTS Technika Transportu Szynowego
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
High-resolution Direction of Arrival Estimation Method Based on Sparse Arrays with Minimum Number of Elements
Autorzy:
Mohammed, Jafar Ramadhan
Powiązania:
https://bibliotekanauki.pl/articles/1839475.pdf
Data publikacji:
2021
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
compressed sensing
direction of arrival
DOA
estimation
sparse array
Opis:
Regular fully filled antenna arrays have been widely used in direction of arrival (DOA) estimation. However, practical implementation of these arrays is rather complex and their resolutions are limited to the beamwidth of the array pattern. Therefore, higher resolution and simpler methods are desirable. In this paper, the compressed sensing method is first applied to an initial fully filled array to randomly select the most prominent and effective elements which are used to form the sparse array. To keep the dimension of the sparse array equal to that of the fully filled array, the first and the last elements were excluded from the sparseness process. In addition, some constraints on the sparse spectrum are applied to increase estimation accuracy. The optimization problem is then solved iteratively using the iterative reweighted l1 norm. Finally, a simple searching algorithm is used to detect peaks in the spectrum solution that correspond to the directions of the arriving signals. Compared with the existing scanned beam methods, such as the minimum variance distortionless response (MVDR) technique, and with subspace approaches, such as multiple signal classification (MUSIC) and ESPIRT algorithms, the proposed sparse array method offers better performance even with a lower number of array elements and in severely noisy environments. Effectiveness of the proposed sparse array method is verified via computer simulations.
Źródło:
Journal of Telecommunications and Information Technology; 2021, 1; 8-14
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
High-resolution Direction of Arrival Estimation Method Based on Sparse Arrays with Minimum Number of Elements
Autorzy:
Mohammed, Jafar Ramadhan
Powiązania:
https://bibliotekanauki.pl/articles/1839489.pdf
Data publikacji:
2021
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
compressed sensing
direction of arrival
DOA
estimation
sparse array
Opis:
Regular fully filled antenna arrays have been widely used in direction of arrival (DOA) estimation. However, practical implementation of these arrays is rather complex and their resolutions are limited to the beamwidth of the array pattern. Therefore, higher resolution and simpler methods are desirable. In this paper, the compressed sensing method is first applied to an initial fully filled array to randomly select the most prominent and effective elements which are used to form the sparse array. To keep the dimension of the sparse array equal to that of the fully filled array, the first and the last elements were excluded from the sparseness process. In addition, some constraints on the sparse spectrum are applied to increase estimation accuracy. The optimization problem is then solved iteratively using the iterative reweighted l1 norm. Finally, a simple searching algorithm is used to detect peaks in the spectrum solution that correspond to the directions of the arriving signals. Compared with the existing scanned beam methods, such as the minimum variance distortionless response (MVDR) technique, and with subspace approaches, such as multiple signal classification (MUSIC) and ESPIRT algorithms, the proposed sparse array method offers better performance even with a lower number of array elements and in severely noisy environments. Effectiveness of the proposed sparse array method is verified via computer simulations.
Źródło:
Journal of Telecommunications and Information Technology; 2021, 1; 8-14
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pre-trained deep neural network using sparse autoencoders and scattering wavelet transform for musical genre recognition
Autorzy:
Kleć, M.
Korzinek, D.
Powiązania:
https://bibliotekanauki.pl/articles/952940.pdf
Data publikacji:
2015
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Sparse Autoencoders
deep learning
genre recognition
Scattering Wavelet Transform
Opis:
Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scatter- Ing Wavelet Transform (SWT) for classifying musical genres. The SWT uses A sequence of Wavelet Transforms to compute the modulation spectrum coef- Ficients of multiple orders, which has already shown to be promising for this Task. The DNN in this work uses pre-trained layers using Sparse Autoencoders (SAE). Data obtained from the Creative Commons website jamendo.com is Used to boost the well-known GTZAN database, which is a standard bench- mark for this task. The final classifier is tested using a 10-fold cross validation To achieve results similar to other state-of-the-art approaches.
Źródło:
Computer Science; 2015, 16 (2); 133-144
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effects of Sparse Initialization in Deep Belief Networks
Autorzy:
Grzegorczyk, K.
Kurdziel, M.
Wójcik, P. I.
Powiązania:
https://bibliotekanauki.pl/articles/305264.pdf
Data publikacji:
2015
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
sparse initialization
Deep Belief Networks
Noisy Rectified Linear Units
Opis:
Deep neural networks are often trained in two phases: first, hidden layers are pretrained in an unsupervised manner, and then the network is fine-tuned with error backpropagation. Pretraining is often carried out using Deep Belief Networks (DBNs), with initial weights set to small random values. However, recent results established that well-designed initialization schemes, e.g., Sparse Initialization (SI), can greatly improve the performance of networks that do not use pretraining. An interesting question arising from these results is whether such initialization techniques wouldn’t also improve pretrained networks. To shed light on this question, in this work we evaluate SI in DBNs that are used to pretrain discriminative networks. The motivation behind this research is our observation that SI has an impact on the features learned by a DBN during pretraining. Our results demonstrate that this improves network performance: when pretraining starts from sparsely initialized weight matrices, networks achieve lower classification errors after fine-tuning.
Źródło:
Computer Science; 2015, 16 (4); 313-327
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Real-time motion tracking using optical flow on multiple GPUs
Autorzy:
Mahmoudi, S. A.
Kierzynka, M.
Manneback, P.
Kurowski, K.
Powiązania:
https://bibliotekanauki.pl/articles/200476.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Lucas-Kanade method
sparse optical flow
multiple GPU computations
Opis:
Motion tracking algorithms are widely used in computer vision related research. However, the new video standards, especially those in high resolutions, cause that current implementations, even running on modern hardware, no longer meet the needs of real-time processing. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have recently been proposed. Although they present a great potential of a GPU platform, hardly any is able to process high definition video sequences efficiently. Thus, a need arose to develop a tool being able to address the outlined problem. In this paper we present software that implements optical flow motion tracking using the Lucas-Kanade algorithm. It is also integrated with the Harris corner detector and therefore the algorithm may perform sparse tracking, i.e. tracking of the meaningful pixels only. This allows to substantially lower the computational burden of the method. Moreover, both parts of the algorithm, i.e. corner selection and tracking, are implemented on GPU and, as a result, the software is immensely fast, allowing for real-time motion tracking on videos in Full HD or even 4K format. In order to deliver the highest performance, it also supports multiple GPU systems, where it scales up very well.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2014, 62, 1; 139-150
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of sparse linear discriminant analysis for prediction of protein-protein interactions
Autorzy:
Stąpor, K.
Fabian, P.
Powiązania:
https://bibliotekanauki.pl/articles/95137.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
sparse discriminant analysis
feature selection
protein-protein interaction
Opis:
To understand the complex cellular mechanisms involved in a biological system, it is necessary to study protein-protein interactions (PPIs) at the molecular level, in which prediction of PPIs plays a significant role. In this paper we propose a new classification approach based on the sparse discriminant analysis [10] to predict obligate (permanent) and non-obligate (transient) protein-protein interactions. The sparse discriminant analysis [10] circumvents the limitations of the classical discriminant analysis [4, 9] in the high dimensional low sample size settings by incorporating inherently the feature selection into the optimization procedure. To characterize properties of protein interaction, we proposed to use the binding free energies. The performance of our proposed classifier is 75% ± 5%.
Źródło:
Information Systems in Management; 2016, 5, 1; 109-118
2084-5537
2544-1728
Pojawia się w:
Information Systems in Management
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 57

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