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Wyświetlanie 1-15 z 23
Tytuł:
A COMPARATIVE STUDY OF FastICA AND GRADIENT ALGORITHMS FOR STOCK MARKET ANALYSIS
Autorzy:
Nermend, Kesra
Rajihy, Yasen
Powiązania:
https://bibliotekanauki.pl/articles/452923.pdf
Data publikacji:
2014
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
independent component analysis
nangaussianity
negentropy
stock market analysis
Opis:
In this paper we proved that a fast fixed point algorithm known as FastICA algorithm depending on maximization the nongaussianity by using the ne-gentropy approach is one of the best algorithm for solving ICA model. We compare this algorithm with Gradient algorithm. The Abu Dhabi Islamic Bank (ADIB) used as illustrative example to evaluate the performance of these two algorithms. Experimental results show that the FastICA algorithm is more robust and faster than Gradient algorithm in stock market analysis.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2014, 15, 1; 142-152
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Quality enhancement of eddy-current-based non-destructive evaluation data through independent component analysis
Autorzy:
Frulloni, E.
Fiori, S.
Powiązania:
https://bibliotekanauki.pl/articles/1954088.pdf
Data publikacji:
2004
Wydawca:
Politechnika Gdańska
Tematy:
neural network applications
eddy current testing
independent component analysis
Opis:
The aim of this paper is to examine the performance of an independent component analysis algorithm based on neural networks applied to the solution of an electrical engineering problem related to non-destructive evaluation of conductive objects. The proposed application is assessed through computer experiments carried out on real-world data, which prove the usefulness of this non-destructive evaluation technique.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2004, 8, 3; 359-375
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of complex-valued functional magnetic resonance imaging data: are we just going through a "phase"?
Autorzy:
Calhoun, V.
Adali, T.
Powiązania:
https://bibliotekanauki.pl/articles/201555.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fMRI
independent component analysis
ICA
phase
complex-valued
brain
Opis:
Functional magnetic resonance imaging (fMRI) data are acquired as a natively complex data set, however for various reasons the phase data is typically discarded. Over the past few years, interest in incorporating the phase information into the analyses has been growing and new methods for modeling and processing the data have been developed. In this paper, we provide an overview of approaches to understand the complex nature of fMRI data and to work with the utilizing the full information, both the magnitude and the phase. We discuss the challenges inherent in trying to utilize the phase data, and provide a selective review with emphasis on work in our group for developing biophysical models, preprocessing methods, and statistical analysis of the fully-complex data. Of special emphasis are the use of data-driven approaches, which are particularly useful as they enable us to identify interesting patterns in the complex-valued data without making strong assumptions about how these changes evolve over time, something which is challenging for magnitude data and even more so for the complex data. Finally, we provide our view of the current state of the art in this area and make suggestions for what is needed to make efficient use of the fully-complex fMRI data.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 371-418
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Inverse method for a one-stage spur gear diagnosis
Autorzy:
Akrout, A.
Tounsi, D.
Taktak, M.
Abbes, M. S.
Haddar, M.
Powiązania:
https://bibliotekanauki.pl/articles/949292.pdf
Data publikacji:
2015
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
Independent Component Analysis (ICA)
source identification
gear mechanism
geometrical defects
Opis:
In this paper, a source separation approach based on the Blind Source Separation (BSS) is presented. In fact, the Independent Component Analysis (ICA), which is the main technique of BSS, consists in extracting different source signals from several observed mixtures. This inverse method is very useful in many fields such as telecommunication, signal processing and biomedicine. It is also very attractive for diagnosis of mechanical systems such as rotating machines. Generally, dynamic responses of a given mechanical system (displacements, accelerations and speeds) measured through sensors are used as inputs for the identification of internal defaults. In this study, the ICA concept is applied to the diagnosis of a one-stage gear mechanism in which two types of defects (the eccentricity error and the localized tooth defect)are introduced. The finite element method allows determination of the signals corresponding to the acceleration in some locations of the system, and those signals may be used also in the ICA algorithm. Hence, the vibratory signatures of each defect can be identified by the ICA concept. Thus, a good agreement is obtained by comparing the expected default signatures to those achieved by the developed inverse method.
Źródło:
Journal of Theoretical and Applied Mechanics; 2015, 53, 3; 617-628
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
CDMA wireless system with blind multiuser detector
Autorzy:
Leong, W. Y.
Homer, J.
Powiązania:
https://bibliotekanauki.pl/articles/309140.pdf
Data publikacji:
2006
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
code division multiple access
independent component analysis
blind source separation
Opis:
In this paper we present an approach capable of countering the presence of multiple access interference (MAI) in code division multiple access (CDMA) channels. We develop and implement a blind multiuser detector, based on an independent component analysis (ICA) to mitigate both MAI and noise. This algorithm has been utilized in blind source separation (BSS) of unknown sources from their linear mixtures. It can also be used for estimation of the basis vectors of BSS. The aim is to include an ICA algorithm within a wireless receiver in order to reduce the level of interference in CDMA systems. This blind multiuser detector requires less precise knowledge of the channel than does the conventional single-user receiver. The proposed blind multiuser detector is made robust with respect to imprecise knowledge of the received signature waveforms of the user of interest. Several experiments are performed in order to verify the validity of the proposed learning algorithm.
Źródło:
Journal of Telecommunications and Information Technology; 2006, 1; 69-75
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimating independent components by mapping onto an orthogonal manifold
Autorzy:
Fiori, S.
Powiązania:
https://bibliotekanauki.pl/articles/1933183.pdf
Data publikacji:
2008
Wydawca:
Politechnika Gdańska
Tematy:
independent component analysis
ICA
orthogonal group of matrices
mappings onto manifolds
Opis:
Algorithms for independent component analysis (ICA) based on information-theoretic criteria optimization over differential manifolds have been devised over the last few years. The principles informing their design lead to various classes of learning rules, including the fixed-point and the geodesic-based ones. Such learning algorithms mainly differ by the way in which single learning steps are effected in the neural system's parameter space, i. e. by the action that a connection variable is moved by in the parameter space toward the optimal connection pattern. In the present paper, we introduce a new class of learning algorithms by recalling from the literature on differential geometry the concept of mapping onto manifolds, which provides a general way of acting upon a neural system's connection variable in order to optimize the learning criteria. The numerical behavior of the introduced learning algorithms is illustrated and compared with experiments carried out on mixtures of statistically-independent signals.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2008, 12, 1-2; 105-120
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
ICA-based Single Channel Audio Separation: New Bases and Measures of Distance
Autorzy:
Mika, D.
Kleczkowski, P.
Powiązania:
https://bibliotekanauki.pl/articles/177414.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
audio unmixing
blind signal separation
independent component analysis
measures of distance
Opis:
Independent Component Analysis (ICA) can be used for single channel audio separation, if a mixed signal is transformed into time-frequency domain and the resulting matrix of magnitude coefficients is processed by ICA. Previous works used only frequency (spectral) vectors and Kullback-Leibler distance measure for this task. New decomposition bases are proposed: time vectors and time-frequency components. The applicability of several different measures of distance of components are analysed. An algorithm for clustering of components is presented. It was tested on mixes of two and three sounds. The perceptual quality of separation obtained with the measures of distance proposed was evaluated by listening tests, indicating “beta” and “correlation” measures as the most appropriate. The “Euclidean” distance is shown to be appropriate for sounds with varying amplitudes. The perceptual effect of the amount of variance used was also evaluated.
Źródło:
Archives of Acoustics; 2011, 36, 2; 311-331
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Infrasound Signal Classification Based on ICA and SVM
Autorzy:
Lu, Quanbo
Wang, Meng
Li, Mei
Powiązania:
https://bibliotekanauki.pl/articles/31339863.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
independent component analysis
fast Fourier transform
support vector machine
infrasound signal
Opis:
A diagnostic technique based on independent component analysis (ICA), fast Fourier transform (FFT), and support vector machine (SVM) is suggested for effectively extracting signal features in infrasound signal monitoring. Firstly, ICA is proposed to separate the source signals of mixed infrasound sources. Secondly, FFT is used to obtain the feature vectors of infrasound signals. Finally, SVM is used to classify the extracted feature vectors. The approach integrates the advantages of ICA in signal separation and FFT to extract the feature vectors. An experiment is conducted to verify the benefits of the proposed approach. The experiment results demonstrate that the classification accuracy is above 98.52% and the run time is only 2.1 seconds. Therefore, the proposed strategy is beneficial in enhancing geophysical monitoring performance.
Źródło:
Archives of Acoustics; 2023, 48, 3; 191-199
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Robustness study of the road profile estimation technique under uncertainty
Autorzy:
Ben Jdidia, Anoire
Ben Hassen, Dorra
Hentati, Taissir
Abbes, Slim
Haddar, Mohamed
Powiązania:
https://bibliotekanauki.pl/articles/2104771.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
Independent Component Analysis (ICA) method
Monte Carlo (MC) method
road profile
estimation
Opis:
This paper studies profile estimation a road. The prediction has been achieved using the Independent Component Analysis Method (ICA). The vehicle dynamic responses were cal- culated for different road profiles which were defined using an ISO norm. The robustness of this method was proven by implementing the stochastic Monte Carlo (MC) technique in the presence of inevitable uncertainty parameters simultaneously associated with the vehicle mass, spring stiffness and damping for different vehicle speeds and wind values. Convergence was assessed when comparing real profiles to simulated ones. The obtained results prove the efficiency of the ICA in estimating the profile variabilities under uncertainties.
Źródło:
Journal of Theoretical and Applied Mechanics; 2022, 60, 3; 521--533
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparison of algorithms for separation of synchronous subspaces
Autorzy:
Almeida, M.
Bioucas-Dias, J.
Vigário, R.
Oja, E.
Powiązania:
https://bibliotekanauki.pl/articles/201566.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
phase locking
synchrony
source separation
subspaces
Independent Component Analysis (ICA)
Independent Subspace Analysis (ISA)
magnetoencephalogram (MEG)
Opis:
Independent Subspace Analysis (ISA) consists in separating sets (subspaces) of dependent sources, with different sets being independent of each other. While a few algorithms have been proposed to solve this problem, they are all completely general in the sense that they do not make any assumptions on the intra-subspace dependency. In this paper, we address the ISA problem in the specific context of Separation of Synchronous Sources (SSS), i.e., we aim to solve the ISA problem when the intra-subspace dependency is known to be perfect phase synchrony between all sources in that subspace. We compare multiple algorithmic solutions for this problem, by analyzing their performance on an MEG-like dataset.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 455-460
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Removing physiological artifacts from the EEG data by algorithms based on differential entropy
Eliminacja artefaktów fizjologicznych z zapisu EEG przez algorytmy stosujące entropię różniczkową
Autorzy:
Górecka, J.
Powiązania:
https://bibliotekanauki.pl/articles/152185.pdf
Data publikacji:
2012
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
entropia różniczkowa
analiza składowych niezależnych
zapis EEG
differential entropy
independent component analysis
EEG data
Opis:
A new form of the nonlinearity implemented in the ICA approach is presented in the paper. The proposed independent component analysis based on differential entropy can be used for elimination of physiological artifacts from electroencephalographic signals. For verification of the quality of separation of the EEG data, the PI index is proposed. The second measure of accuracy is the normalized kurtosis which can be used in analysis of the simulated EEG data. As it has been proved, the new sigmoid function used in the ICA approach can effectively separate the EEG data.
W artykule przedstawiono nową propozycję nieliniowości - sigmoidalną funkcję algebraiczną, która została zaimplementowana w algorytmie stosującym metodę analizy składowych niezależnych (ang. Independent Component Analysis). Proponowana nowa postać algorytmu wykorzystująca właściwości entropii różniczkowej, może zostać użyta także do separacji a następnie eliminacji wybranych artefaktów fizjologicznych pochodzenia ocznego i mięśniowego zarejestrowanych w zapisach EEG. W celu weryfikacji dokładności separacji sygnałów EEG zaproponowano współczynnik jakości separacji PI (ang. Performance Index). Jako drugą miarę dokładności procesu separacji wybrano wartość znormalizowanej kurtozy, która może być stosowana jedynie w przypadku separacji elektroencefalogramów zarejestrowanych z symulatora EEG. W artykule udowodniono, że użycie nowej funkcji sigmoidalnej w rozszerzonej postaci algorytmu infomax prowadzi do efektywnej separacji sygnałów EEG umożliwiając eliminację wybranych składowych niepożądanych.
Źródło:
Pomiary Automatyka Kontrola; 2012, R. 58, nr 11, 11; 975-977
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie metody PCA i ICA do analizy sygnału EEG w kontekście usuwania zakłóceń
Use of PCA and ICA methods for analysis of EGG signal in context of removal of artefacts
Autorzy:
Paszkiel, S.
Powiązania:
https://bibliotekanauki.pl/articles/154789.pdf
Data publikacji:
2013
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
analiza artefaktów
analiza składowych głównych
analiza składowych niezależnych
EEG
analysis of artefacts
principal component analysis
independent component analysis
Opis:
W artykule przedstawiono metodę PCA (ang. Principal Component Analysis) oraz ICA (ang. Independent Component Analysis), jako narzędzia pomocne w procesie eliminacji artefaktów z sygnału elektroencefalograficznego. Proces rejestracji sygnału elektroencefalograficznego można zobrazować, jako BSS (ang. Blind Signals Separation). Dzięki temu możliwe jest dokonywanie estymacji nieznanych sygnałów źródłowych oraz ekstrakcji niepożądanych sygnałów zakłócających, w zakresie ich późniejszej eliminacji. W tym celu konieczne jest doskonalenie metod weryfikacji i eliminacji artefaktów z sygnału EEG. W artykule opisano możliwość zastosowania powyższych metod w zakresie sygnału EEG oraz zrealizowane zostało porównanie skuteczności ich działania.
: In the paper there are presented the Principal Component Analysis (PCA) and the Independent Component Analysis (ICA) as useful tools for elimination of artefacts in an electroencephalographic signal. The process of registration of the electroencephalographic signal can be described as BSS - Blind Signals Separation. It is possible to estimate unknown source signals and to extract intrusive disturbing signals in terms of their subsequent elimination. It is necessary to improve the methods of verification and elimination of artefacts from an EEG signal. The Brain Computer Interface (BCI) technology is presented briefly in the first part of the paper. EEG signal characteristics and its acqui-sition with the non-invasive method are described in the second part. Next, there is discussed the possibility of using the PCA and ICA methods in terms of analysis of an EEG signal. Comparison of the effectiveness of these methods is presented as well. A general profile of the EEG signal processing is shown in Fig. 1. An example of use of the infomax algorithm for a real EEG signal is depicted in Fig. 2. Fig. 3 shows an exemplary Event-Related Potential (ERP) of the EEG signal.
Źródło:
Pomiary Automatyka Kontrola; 2013, R. 59, nr 3, 3; 204-207
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
NEUROPSYCHOLOGY AND THE NEUROPHYSIOLOGY OF PERCEPTUAL MICROGENESIS
Autorzy:
Pąchalska, Maria
Góral-Pólrola, Jolanta
Mueller, Andreas
Kropotov, Juri D
Powiązania:
https://bibliotekanauki.pl/articles/2137785.pdf
Data publikacji:
2017-12-18
Wydawca:
Fundacja Edukacji Medycznej, Promocji Zdrowia, Sztuki i Kultury Ars Medica
Tematy:
microgenetic theory
event related potentials
stages of information flow
ventral visual stream
independent component analysis
Opis:
Perception is one of the psychological operations that can be analyzed from the point of view of microgenetic theory. Our study tests the basic premise of microgenesis theory – the existence of recurrent stages of visual information processing. The event related potentials in two variants of a cued GO/NOGO task (contrasting images of Animals and Plants in the first variant, and contrasting images of Angry and Happy faces in the second variant) were studied during the first 300 ms following stimulus presentation. The independent component analysis was applied to a large collection of ERPs. The functional independent components associated with visual category discrimination, comparison to working memory, action initiation and conflict detection were separated. Information processing in the ventral visual stream (the temporal independent components) occurs at two sequential stages with positive/negative fluctuations of the cortical potential as indexes of the stages. The first stage represents the comparison of the pure physical features of the visual input with the memory trace. The second stage represents the comparison of more sophisticated semantic/emotional features with the working memory. The two stages are the results of interplay between bottom-up and top-down projections in the visual ventral stream.
Źródło:
Acta Neuropsychologica; 2017, 15(4); 365-389
1730-7503
2084-4298
Pojawia się w:
Acta Neuropsychologica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Impulse Noise Suppression Based on Power Iterative ICA in Power Line Communication
Autorzy:
Zhang, Wei
Luo, Zhongqiang
Xiong, Xingzhong
Powiązania:
https://bibliotekanauki.pl/articles/226439.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
power line communication(PLC)
orthogonal frequency division multiplexing(OFDM) signal
independent component analysis(ICA)
impulse noise
Opis:
To overcome the detrimental influence of impulse noise in power line communication and the trap of scarce prior information in traditional noise suppression schemes , a power iteration based fast independent component analysis (PowerICA) based noise suppression scheme is designed in this paper. Firstly, the pseudo-observation signal is constructed by weighted processing so that single-channel blind separation model is transformed into the multi-channel observed model. Then the proposed blind separation algorithm is used to separate noise and source signals. Finally, the effectiveness of the proposed algorithm is verified by experiment simulation. Experiment results show that the proposed algorithm has better separation effect, more stable separation and less implementation time than that of FastICA algorithm, which also improves the real-time performance of communication signal processing.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 4; 651-656
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Usuwanie artefaktów z danych EEG przy użyciu analizy składowych niezależnych
Removal of artifacts from EEG data by means of Independent Component Analysis
Autorzy:
Górecka, J.
Kaszyński, R.
Powiązania:
https://bibliotekanauki.pl/articles/158376.pdf
Data publikacji:
2008
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
ślepa separacja sygnałów
analiza składowych niezależnych (ICA)
sygnały EEG
artefakt
blind signal separation
independent component analysis
EEG data
artifacts
Opis:
Celem przedstawionych wyników badań jest eliminacja wybranych niepożądanych sygnałów przy użyciu analizy składowych niezależnych. W artykule przedstawiono następujące algorytmy BSS (z ang. Blind Signal Separation): HJ oraz Infomax jako narzędzia do separacji i usuwania wybranej grupy artefaktów (mruganie powiek, artefakty mięśniowe) z przebiegów EEG. Jak udowodniono w eksperymentach proponowane algorytmy adaptacyjne mogą efektywnie wykrywać i usuwać wybrane artefakty z przebiegów EEG.
The aim of the performed investigations is to remove selected undesired signals by means of ICA approach. In the paper there are presented the following algorithms BSS (Blind Signal Separation): HJ and Infomax for separation and removal of selected group of artifacts (eye blinks, muscle activity) from EEG recordings. It has been proved in the experiments which are described in the paper that the proposed adaptive algorithms can effectively detect and remove these selected artifacts from EEG recordings.
Źródło:
Pomiary Automatyka Kontrola; 2008, R. 54, nr 12, 12; 827-830
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 23

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