Wyniki wyszukiwania - BazTech

1

Analysis of the single stage cycloidal gearbox with lobe defects. Fault diagnosis attempts using coherence function and Morris minimum-bandwidth wavelets

Król Roman

Archive of Mechanical Engineering

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2023

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LXX, nr 3

409--431

EN

In this article, the frequency characteristics of the forces and torques in the various cycloidal gearbox designs were investigated. The aim of the article is the search for frequency patterns that could be used in the formulation of a fault diagnosis methodology. Numerical analysis was performed in the cycloidal gearbox without defects as well as in cycloidal gearboxes with lobe defects or with removed lobes. The results of the numerical analysis were obtained in the multibody dynamics model of the cycloidal gearbox, implemented in Fortran and using the 2nd-order Runge-Kutta method for the integration of the motion equations. The used model is planar and uses Hunt and Crossley’s nonlinear contact modelling algorithm, which was modified using the Heaviside function and backlash to fit cycloidal gearbox model convergence demands. In the analysis of fault diagnosis methods, the coherence function and Morris minimum-bandwidth wavelets were used. It is difficult to find a unique pattern in the results to use in the fault diagnosis because of the random characteristics of the torques at the input and output shafts. Based on obtained results, a promising, low-vibration cycloidal gearbox design with removed 7 lobes of the single wheel was studied using the FFT algorithm.

2

A wavelet-based approach for business protocol discovery of web services from log files

Moudjari A., Kezzouli I., Talbi H., Draa A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2019

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Vol. 67, nr 3

535--546

EN

Recently, business protocol discovery has taken more attention in the field of web services. This activity permits a better description of the web service by giving information about its dynamics. The latter is not supported by theWSDL language which concerns only the static part. The problem is that the only information available to construct the dynamic part is the set of log files saving the runtime interaction of the web service with its clients. In this paper, a new approach based on the Discrete Wavelet Transformation (DWT) is proposed to discover the business protocol of web services. The DWT allows reducing the problem space while preserving essential information. It also overcomes the problem of noise in the log files. The proposed approach has been validated using artificially-generated log files.

3

Periodic trends in two-phase flow through a vertical minichannel: wavelet and multiscale entropy analyses based on digital camera data

Górski Grzegorz, Mosdorf Romuald, Rysak Andrzej, Litak Grzegorz

Acta Mechanica et Automatica

|

2019

|

Vol. 13, no. 1

51--56

EN

By changing the air and water flow relative rates in the two-phase (air-water) flow through a minichannel, we observe aggregation and partitioning of air bubbles and slugs of different sizes. An air bubble arrangement, which show non-periodic and periodic patterns. The spatiotemporal behaviour was recorded by a digital camera. Multiscale entropy analysis is a method of measuring the time series complexity. The main aim of the paper was testing the possibility of implementation of multiscale entropy for two-phase flow patterns classification. For better understanding, the dynamics of the two-phase flow patterns inside the minichannel histograms and wavelet methods were also used. In particular, we found a clear distinction between bubbles and slugs formations in terms of multiscale entropy. On the other hand, the intermediate region was effected by appearance of both forms in non-periodic and periodic sequences. The preliminary results were confirmed by using histograms and wavelets.

4

Investigations into solar flare effects using wavelet‑based local intermittency measure

Gopinath Sumesh, Prince P. R.

Acta Geophysica

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2019

|

Vol. 67, no. 2

687--701

EN

The present study analyzes the efficiency of local intermittency measure based on wavelet transforms in identifying solar flare effects on magnetograms. If we observe the flare-time features in geomagnetic components, most often, disturbances associated with other solar phenomena will enhance or mask the solar flare signatures. Similarly, diurnal and high-latitude geomagnetic variabilities will suppress solar flare effects on magnetograms. The measurements of amplitudes taken directly from temporal variations of weak geomagnetic components have certain limitations regarding the identification of the proper base and peak values from which the deviation due to solar flare has to be measured. In such situations, local intermittency measure based on cross-wavelet analysis can be employed which could remarkably identify the flare effects, even if the signatures are weak or masked by other disturbance effects. The present study shows that local intermittency measure based on wavelet analysis could act as an alternate quantification technique for analyzing solar flare effects on geomagnetic activity.

5

Falkowe metody estymacji zmian ukrytych w diagnostyce obrazowej wczesnego udaru mózgu

Ostrek G.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2017

|

nr 6

346--349, CD

PL

Niniejsza praca dotyczy metod komputerowego wspomagania mających na celu zwiększenie skuteczność rozpoznania słabo widocznych objawów udaru niedokrwiennego mózgu na podstawie przetwarzania i analizy obrazów tomografii komputerowej. Efekty przetwarzania były ocenione i porównywane przy wykorzystaniu obiektywizowanej miary obliczeniowej. Ponadto przedstawiono algorytm automatycznej detekcji i rozpoznania podejrzanych zmian chorobowych na podstawie obrazów oraz z uwzględnieniem danych klinicznych.

EN

Presented work was aimed for developing methods supporting effective diagnosis of early ischemic stroke based on the processing and analysis of computer tomography images. The proposed algorithms were designed to improved recognition of hidden symptoms of early stroke, and automatic detection of suspected lesions with respect to clinical data. Image processing results were evaluated and compared by proposed objectified measures.

6

Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Surówka G.

Schedae Informaticae

|

2016

|

Vol. 25

189--207

EN

This article addresses the Computer Aided Diagnosis (CAD) of melanoma pigmented skin cancer. We present back-propagated Artificial Neural Network (ANN) classifiers discriminating dermoscopic skin lesion images into two classes: malignant melanoma and dysplastic nevus. Features used for our classification experiments are derived from wavelet decomposition coefficients of the image. Our research objective is i) to select the most efficient topology of the hidden layers and the network learning algorithm for full-size and downgraded image resolutions and, ii) to search for resolution-invariant topologies and learning methods. The analyzed classifiers should be fit to work on ARM-based hand-held devices, hence we take into account only limited learning setups.

7

Water demand forecasting using extreme learning machines

Tiwari M., Adamowski J., Adamowski K.

Journal of Water and Land Development

|

2016

|

no. 28

37--52

EN

The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELMW, ELMB), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANNW, ANNB). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELMB and ANNB models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANNW and ELMW models provided greater accuracy, with the ELMW model outperforming the ANNW model. Significant improvement in peak urban water demand prediction was only achieved with the ELMW model. The superiority of the ELMW model over both the ANNW or ANNB models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

PL

Oceniono zdolność modelowania z użyciem ekstremalnej maszyny uczącej się (ELM) stosowanej samodzielnie bądź w połączeniu z analizą falkową (W) lub metodami bootstrapowymi (B) (tzn. ELM, ELMW, ELMB) do przewidywania dobowego zapotrzebowania na wodę w mieście. Wyniki porównano z uzyskanymi tradycyjnymi metodami bazującymi na sztucznych sieciach neuronowych (tzn. ANN, ANNW, ANNB). Modele przewidujące zapotrzebowanie na wodę zbudowano z wykorzystaniem trzyletniego zapotrzebowania na wodę i zestawu danych klimatycznych dla miasta Calgary w kanadyjskiej prowincji Alberta. Hybrydowe modele ELMB i ANNB zapewniały satysfakcjonujące prognozy jednodniowe o podobnej dokładności, natomiast wyniki uzyskane z zastosowaniem modeli ELMW i ANNW były bardziej dokładne, przy czym model ELMW okazał się lepszy niż ANNW. Istotną poprawę prognozowania szczytowego zapotrzebowania na wodę w mieście uzyskano jedynie z zastosowaniem modelu ELMW. Wyższość modelu ELMW nad modelami ANNW czy ANNB dowodzi znaczącej roli transformacji falkowej w usprawnianiu działania modeli prognozujących zapotrzebowanie na wodę w mieście.

8

Differentiation of random structure properties using wavelet analysis of backscattered ultrasound

Gambin B., Wojcik J., Doubrovina O.

Hydroacoustics

|

2016

|

Vol. 19

121--128

EN

The aim of this work was to find the differences between random media by analyzing the properties of the ultrasound signals backscattered from the inhomogeneities. A numerical model is used to generate two types of random media. The first has the randomness in scatterers’ positions and the second has the randomness in the size and acoustical properties of scatterers. The numerical model of wave scattering has been used to simulate the RF (radio frequency) signals caused by the incident pulse traveling as a plane wave. The markers of randomness type differences between the scattering media were obtained with the help of the spectral and wavelet analysis. The effect of differences in randomness type is more spectacular when the wavelet analysis is performed.

9

Inversion of the Riemann-Liouville operator and its dual using wavelets

Baccar C., Hamadi N. B., Herch H., Meherzi F.

Opuscula Mathematica

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2015

|

Vol. 35, no. 6

867--887

EN

We define and study the generalized continuous wavelet transform associated with the Riemann-Liouville operator that we use to express the new inversion formulas of the Riemann-Liouville operator and its dual.

10

Application of cubic box spline wavelets in the analysis of signal singularities

Rakowski W.

International Journal of Applied Mathematics and Computer Science

|

2015

|

Vol. 25, no. 4

927--941

EN

In the subject literature, wavelets such as the Mexican hat (the second derivative of a Gaussian) or the quadratic box spline are commonly used for the task of singularity detection. The disadvantage of the Mexican hat, however, is its unlimited support; the disadvantage of the quadratic box spline is a phase shift introduced by the wavelet, making it difficult to locate singular points. The paper deals with the construction and properties of wavelets in the form of cubic box splines which have compact and short support and which do not introduce a phase shift. The digital filters associated with cubic box wavelets that are applied in implementing the discrete dyadic wavelet transform are defined. The filters and the algorithme à trous of the discrete dyadic wavelet transform are used in detecting signal singularities and in calculating the measures of signal singularities in the form of a Lipschitz exponent. The article presents examples illustrating the use of cubic box spline wavelets in the analysis of signal singularities.

11

SoBT-RFW : Rough-Fuzzy Computing and Wavelet Analysis Based Automatic Brain Tumor Detection Method from MR Images

Maji P., Roy S.

Fundamenta Informaticae

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2015

|

Vol. 142, nr 1-4

237--267

EN

One of the important problems in medical diagnosis is the segmentation and detection of brain tumor in MR images. The accurate estimation of brain tumor size is important for treatment planning and therapy evaluation. In this regard, this paper presents a new method, termed as SoBT-RFW, for segmentation of brain tumor fromMR images. It integrates judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method starts with a simple skull stripping algorithm to remove non-cerebral tissues such as skull, scalp, and dura from brain MR images. To extract the scale-space feature vector for each pixel of brain region, the dyadic wavelet analysis is used, while an unsupervised feature selection method, based on maximum relevance-maximum significance criterion, is used to select relevant and significant textural features for brain tumor segmentation. To address the uncertainty problem of brain MR image segmentation, the proposed SoBT-RFW method uses the robust rough-fuzzy c-means algorithm. After the segmentation process, asymmetricity is analyzed by using the Zernike moments of each of the tissues segmented in the brain to identify the tumor. Finally, the location of the tumor is searched by a region growing algorithm based on the concept of rough sets. The performance of the proposed SoBT-RFW method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

12

Multiwavelets and multiwavelet packets of Legendre functions in the direct method for solving variational problems

Jarczewska K., Glabisz W., Zielichowski-Haber W.

Archives of Civil and Mechanical Engineering

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2015

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Vol. 15, no. 1

1--10

EN

A numerical technique for solving the linear problems of the calculus of variations is presented in this paper. Multiwavelets and multiwavelet packets of Legendre functions are used as basis functions in the Ritz method of formulation. An operational matrix of integration of multiwavelets and multiwavelet packets is introduced and is used to reduce the calculus of variation problem to the solution of the system of algebraic equations. The algorithm is applied to the analysis of mechanic problems which are formulated as func-tionals. Two examples are considered in this paper. The first example concerns the stability problem of a Euler–Bernoulli beam and the second one presents the calculation of the extreme value of the functional which defines the potential energy of an elastic string. The presented method yields the approximate solutions which are convergent to accurate results.

13

Diagnostyka generatora prądu stałego oparta na analizie sygnałów akustycznych z użyciem transformaty falkowej biortogonalnej i klasyfikatora najbliższej średniej

Głowacz A., Głowacz W., Głowacz Z.

Przegląd Elektrotechniczny

|

2014

|

R. 90, nr 11

185--188

PL

W artykule przedstawiono problematykę diagnostyki stanów przedawaryjnych generatora prądu stałego. Przedstawiono stanowisko badawcze razem z oprogramowaniem pozwalającym na przeprowadzenie badań. Badania zostały przeprowadzone dla algorytmów przetwarzania danych: Dyskretnej transformaty falkowej Biortogonalnej i klasyfikatora Najbliższej Średniej z metryką Minkowskiego. Na podstawie zaimplementowanego systemu, przeanalizowano możliwość diagnostyki defektu generatora prądu stałego. Opracowane algorytmy metod diagnostycznych mają wysoką skutecznością rozpoznawania. To sprawia, że możliwe jest stosowanie ich w przemyśle.

EN

The paper presented problems of diagnostics of imminent failure conditions of Direct Current generator. There is presented a measuring stand together with a software enabling to research the diagnostic processes. Studies were conducted for algorithms of data processing: Biorthogonal Wavelet Transform and Nearest Mean classifier with Minkowski distance. On the basis of implemented system, a possibility of diagnosing of Direct Current generator defect has been analyzed. Developed algorithms of diagnostic methods have high efficiency of recognition. This makes it possible to use them in the industry.

14

Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods

Schluter S., Deuschle C.

Managerial Economics

|

2014

|

Vol. 15, no. 1

107--131

EN

By means of wavelet transform, an ARIMA time series can be split into different frequency components. In doing so, one is able to identify relevant patters within this time series, and there are different ways to utilize this feature to improve existing time series forecasting methods. However, despite a considerable amount of literature on the topic, there is hardly any work that compares the different wavelet-based methods with each other. In this paper, we try to close this gap. We test various wavelet-based methods on four data sets, each with its own characteristies. Eventually we come to the conclusion that using wavelets does improve forecasting quality especially for time horizons longer than one-day-ahead. However, there is no single superior method: either wavelet-based denoising or wavelet-based time series decomposition is best. Performance depends on the data set as well as the forecasting time horizon.

15

Falkowa kompresja obrazów kolorowych

Niedziela T.

Logistyka

|

2014

|

nr 4

PL

W artykule przedstawiono podstawy teoretyczne wielorozdzielczej analizy sygnałów jednowymiarowych i dwuwymiarowych (1 – D oraz 2-D). Scharakteryzowano także dekompozycję i rekonstrukcję sygnałów 2-D. Omówiono podstawowe własności falek i funkcji skalujących. Zdefiniowano transformaty funkcji dwuwymiarowej w przestrzeni aproksymacji i szczegółów. Wskazano na wysoką skuteczność falkowej kompresji obrazów kolorowych.

EN

The paper presents the theoretical basis of multiresolution signal analysis of one-dimensional and two-dimensional (1 - D and 2- D). The decomposition and reconstruction of 2-D signals were characterized. The basic properties of wavelets and scaling functions were discussed. Also transform function in the space of two-dimensional approximation and detail were defined. Author pointed to the high efficiency of wavelet compression of color images.

16

Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses

Caswell J. M.

International Letters of Chemistry, Physics and Astronomy

|

2014

|

Vol. 15

55--63

EN

Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present study, a combination of wavelet neural networks was adapted from previous research in order to predict daily average values of cosmic ray impulses 30 days in advance. Additional comparison of both neural network and linear regression modelling with and without wavelet decomposition was conducted for further demonstration of increased accuracy with wavelet neural networks in a simple input-output fitting model.

17

Wavelet Decomposition of Signal and Feature Selection by LASSO for Pattern Recognition

Libal U.

Przegląd Elektrotechniczny

|

2013

|

R. 89, nr 4

89-91

EN

There is searched the balance between an increase of pattern recognition risk and a decrease of a model size. The experiments are performed for noisy signals, decomposed in wavelet bases. Wavelet representation of signals, i.e. representation by wavelet coefficients called signal features, constitutes the full model. The presented feature selection method is based on the Lasso algorithm (Least Absolute Shrinkage and Selection Operator). The aim of the experiment is to find an optimal model size and investigate the relations between the risk, the number of signal features and the noise level. A new criterion of feature selection is proposed that minimizes both the risk and the number of signal features. The experimental risk of classification is analysed for all possible reduced by Lasso models and for several values of noise levels.

PL

Poszukiwana jest równowaga pomi˛edzy wzrostem ryzyka rozpoznawania obrazów oraz zmniejszeniem rozmiaru modelu. Badania przeprowadzono dla zaszumionych sygnałów, zdekomponowanych w bazach falkowych. Falkowa reprezentacja sygnałów, czyli reprezentacja za pomoca˛współczynników falkowych zwanych cechami sygnału, stanowi pełny model. Przedstawiona metoda selekcji cech jest oparta o algorytm Lasso (Least Absolute Shrinkage and Selection Operator). Celem eksperymentu jest znalezienie optymalnego rozmiaru modelu oraz zbadanie zale˙znosci pomie˛dzy ryzykiem, liczba˛ cech sygnału oraz poziomem szumu. Zaproponowano nowe kryterium selekcji cech, które minimalizuje ryzyko oraz liczbe˛ cech sygnału. Eksperymentalne ryzyko błe˛dnej klasyfikacji jest badane dla wszystkich moz˙liwych zredukowanych za pomoca˛ Lasso modeli oraz kilku wartosci poziomu szumu.

18

Adaptive synthesis of a wavelet transform using fast neural network

Stolarek J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2011

|

Vol. 59, nr 1

9-13

EN

This paper introduces a new method for an adaptive synthesis of a wavelet transform using a fast neural network with a topology based on the lattice structure. The lattice structure and the orthogonal lattice structure are presented and their properties are discussed. A novel method for unsupervised training of the neural network is introduced. The proposed approach is tested by synthesizing new wavelets with an expected energy distribution between low- and high-pass filters. Energy compaction of the proposed method and Daubechies wavelets is compared. Tests are performed using sound and image signals.

19

Wavelets and Hilbert Transform for detection of short disturbances in electrical power networks

Caciotta M., Giarnetti S., Leccese F., Leonowicz Z.

Przegląd Elektrotechniczny

|

2010

|

R. 86, nr 5

50-54

EN

In electrical energy power network, disturbances can cause problems in electronic devices therefore their monitoring is fundamental to properly design the protection and compensation devices. In this paper we address the problem of disturbances’ detection by using two different signal processing methods: Wavelets and Hilbert Transform (HT). Methods were tested under different conditions of noise and harmonic distortion (THD) showing the Hilbert Transform can be used as a valid approach for this type of phenomena.

PL

W sieciach elektroenergetycznych, zakłócenia mogą powodować problemy w urządzeniach elektronicznych. W związku z tym ich monitorowanie, ma fundamentalne znaczenie dla prawidłowego projektowania urządzeń ochronnych i kompensacyjnych. W artykule podjęto problem wykrywania zakłóceń "za pomocą dwóch różnych metod przetwarzania sygnałów: falek i transformaty Hilberta (HT). Metody te były badane przy różnych poziomach szumu i zniekształceń harmonicznych (THD). Stwierdzono przydatność HT do analizy krótkich przebiegów przejściowych i zakłóceń oraz podobnych zjawisk.

20

Methods for fast detection of voltage sags as a crucial prerequisite for reliable operation of dynamic voltage restorer

Janik P., Rezmer J., Leonowicz Z., Wacławek Z., Lobos T., Amaris H., Ortega C.

Przegląd Elektrotechniczny

|

2010

|

R. 86, nr 1

35-37

EN

Custom Power Devices like the Dynamic Voltage Restorer have been applied for voltage dip mitigation in the last years. These electronic equipments need fast and reliable voltage dip detection algorithms. Such detection methodologies must be able to detect a voltage dip as fast as possible and be immune to other types of perturbations. In this paper we address the problem of voltage dip estimation by different signal processing methods such as Fourier based algorithm and Wavelet processing.

PL

W ostatnich latach odnotowano wzrost zainteresowania układami zapobiegającymi zapadom napięcia po stronie odbiorcy. Szybkie i pewne wyznaczenie początku zapadu jest kluczowym zagadnieniem dla energoelektronicznych urządzeń tego typu. Zapad powinien być wykryty możliwie szybko, jednocześnie algorytm powinien być nieczuły na inne rodzaje zakłóceń. Artykuł omawia zagadnienia wyznaczania parametrów zapadu z wykorzystaniem algorytmu bazującego na transformacie Fouriera oraz z zastosowaniem falek.