Wyniki wyszukiwania - Biblioteka Nauki

1

Ocena stanu lotniczego zespołu napędowego z użyciem algorytmu EMD

100%

Jonak J. , Machrowska A.

Zeszyty Naukowe. Mechanika / Politechnika Opolska

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2015

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tom z. 106

54

PL

W referacie przedstawiono wyniki badań nad możliwością użycia algorytmu Empirical Mode Decomposition (EMD) w ocenie stanu śmigłowcowego zespołu napędowego, w którego skład wchodzą dwa silniki napędzające przekładnię. Z uwagi na specyfikę konstrukcji i pracy silników turbinowych, stwierdzono występowanie ograniczeń w efektywnym wykorzystaniu omawianego algorytmu w praktyce.

2

A window based method to reduce the end-effect in Empirical Mode Decomposition

80%

Cotogno M. , Cocconcelli M. , Rubini R.

Diagnostyka

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2013

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tom Vol. 14, No. 1

3--10

EN

Empirical Mode Decomposition technique (EMD) is a recent development in non-stationary and non-linear data analysis. It is an algorithm which adaptively decomposes the signal in the sum of Intrinsic Mode Functions (IMFs) from which the instantaneous frequency can be easily computed. EMD has proven its effectiveness but is still affected from various problems. One of these is the “end-effect”, a phenomenon occurring at the start and at the end of the data due to the splines fitting on which the EMD is based. Various techniques have been tried to overcome the end-effect, like different data extension or mirroring procedures at the data boundary. In this paper we made use of the IMFs orthogonality property to apply a symmetrical window to the data before EMD for end-effect reduction. Subsequently the IMFs are post-processed to compensate for data alteration due to windowing. The simulations show that IMFs obtained with this method are of better quality near the data boundaries while remaining almost identical to classical EMD ones.

3

Wykorzystanie EMD w diagnostyce uszkodzeń kół zębatych

71%

Łazarz B. , Madej H. , Czech P.

Zeszyty Naukowe. Transport / Politechnika Śląska

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2005

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tom z. 57

205-210

PL

W opracowaniu przedstawiono wyniki eksperymentu, którego celem było zastosowanie empirical mode decomposition (EMD) w zadaniu diagnostyki uszkodzeń kół zębatych.

EN

The work presents results of an experiment that employs empirical mode decomposition in the task of identification of the degree of tooth root cracking.

4

A Robust Audio Watermarking Scheme using Higher-order Statistics in Empirical Mode Decomposition Domain

70%

Wang W. , Niu P. , Yang H. , Zhang Y. , Ma T.

Fundamenta Informaticae

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2014

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tom Vol. 130, nr 4

467--490

EN

The development of a desynchronization invariant audio watermarking scheme without degrading acoustical quality is a challenging work. This paper proposes a robust audio watermarking scheme in Empirical Mode Decomposition (EMD) domain, in which the higher-order statistics and synchronization code are utilized. Firstly, the wavelet de-noising is performed on the original host audio, the de-noised digital audio is segmented, and then each segment is cut into two parts. Secondly, with the spatial watermarking technique, synchronization code is embedded into the statistics average value of audio samples in the first part. Thirdly, for the second part, EMD is performed, and a series of Intrinsic Mode Functions (IMFs) and a residual are given, and then the higher-order statistics of residual are obtained by using the Hausdorff distance. Finally, the digital watermark is embedded into the residual in EMD domain by using the higher-order statistics. Simulation results show that the proposed watermarking scheme is not only inaudible and robust against common signal processing operations such as MP3 compression, noise addition,resampling, and re-quantization etc, but also robust against the desynchronization attacks such as random cropping, amplitude variation, pitch shifting, and jittering etc.

5

Using Empirical Mode Decomposition of Backscattered Ultrasound Signal Power Spectrum for Assessment of Tissue Compression

70%

Byra M. , Wójcik J. , Nowicki A.

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tom Vol. 43, No. 3

447--453

EN

Quantitative ultrasound has been widely used for tissue characterization. In this paper we propose a new approach for tissue compression assessment. The proposed method employs the relation between the tissue scatterers’ local spatial distribution and the resulting frequency power spectrum of the backscattered ultrasonic signal. We show that due to spatial distribution of the scatterers, the power spectrum exhibits characteristic variations. These variations can be extracted using the empirical mode decomposition and analyzed. Validation of our approach is performed by simulations and in-vitro experiments using a tissue sample under compression. The scatterers in the compressed tissue sample approach each other and consequently, the power spectrum of the backscattered signal is modified. We present how to assess this phenomenon with our method. The proposed in this paper approach is general and may provide useful information on tissue scattering properties.

6

Continuous Wavelet and Hilbert-Huang Transforms Applied for Analysis of Active and Reactive Power Consumption

70%

Avdakovic S. , Bosovic A.

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7

Two-dimensional EMD with shape-preserving spline interpolation

70%

Brown W. G. , Walt M. D. v. d.

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tom Vol. 29, nr 2

287--296

EN

Empirical mode decomposition (EMD) is a popular, user-friendly, data-driven algorithm to decompose a given (non-stationary) signal into its constituting components, utilizing spline interpolation. This algorithm was first proposed in 1998 in the one-dimensional setting, and it employed standard cubic spline interpolation. Since then, different two-dimensional extensions of EMD have been proposed. In this paper, we consider one of these two-dimensional extensions and adapt it to use a shape-preserving interpolation scheme based on quadratic B-splines, ensuring that monotonicity and concavity in the input data are preserved. Using multiple numerical experiments, we show that this new scheme outperforms the original EMD, both qualitatively and quantitatively.

8

Continuous wavelet and Hilbert-Huang Transforms applied for analysis of active and reactive power consumption

70%

Avdakovic S. , Bosovic A.

Metrology and Measurement Systems

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2014

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tom Vol. 21, nr 3

413--422

EN

Analysis of power consumption presents a very important issue for power distribution system operators. Some power system processes such as planning, demand forecasting, development, etc.., require a complete understanding of behaviour of power consumption for observed area, which requires appropriate techniques for analysis of available data. In this paper, two different time-frequency techniques are applied for analysis of hourly values of active and reactive power consumption from one real power distribution transformer substation in urban part of Sarajevo city. Using the continuous wavelet transform (CWT) with wavelet power spectrum and global wavelet spectrum some properties of analysed time series are determined. Then, empirical mode decomposition (EMD) and Hilbert-Huang Transform (HHT) are applied for the analyses of the same time series and the results showed that both applied approaches can provide very useful information about the behaviour of power consumption for observed time interval and different period (frequency) bands. Also it can be noticed that the results obtained by global wavelet spectrum and marginal Hilbert spectrum are very similar, thus confirming that both approaches could be used for identification of main properties of active and reactive power consumption time series.

9

Comparison of multiband filtering, empirical mode decomposition and short-time fourier transform used to extract physiological components from long-term heart rate variability

60%

Adamczyk K. , Polak A. G.

Metrology and Measurement Systems

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2021

|

tom Vol. 28, nr 4

643--660

EN

Heart rate is constantly changing under the influence of many control signals, as manifested by heart rate variability (HRV). HRV is a nonstationary, irregularly sampled signal, the spectrum of which reveals distinct bands of high, low, very low and ultra-low frequencies (HF, LF, VLF, ULF). VLF and ULF components are the least understood, and their analysis requires HRV records lasting many hours. Moreover, there are still no well-established methods for the reliable extraction of these components. The aim of this work was to select, implement and compare methods which can solve this problem. The performance of multiband filtering (MBF), empirical mode decomposition and the short-time Fourier transform was tested, using synthetic HRV as the ground truth for methods evaluation as well as real data of three patients selected from 25 polysomnographic records with a clear HF component in their spectrograms. The study provided new insights into the components of long-term HRV, including the character of its amplitude and frequency modulation obtained with the Hilbert transform. In addition, the reliability of the extracted HF, LF, VLF and ULF waveforms was demonstrated, and MBF turned out to be the most accurate method, though the signal is strongly nonstationary. The possibility of isolating such waveforms is of great importance both in physiology and pathophysiology, as well as in the automation of medical diagnostics based on HRV.

10

An empirical wavelet transform based approach for multivariate data processing application to cardiovascular physiological signals

60%

Singh O. , Sunkaria R. K.

Bio-Algorithms and Med-Systems

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2018

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tom Vol. 14, no. 4

art. no. 20180030

EN

Background: This article proposes an extension of empirical wavelet transform (EWT) algorithm for multivariate signals specifically applied to cardiovascular physiological signals. Materials and methods: EWT is a newly proposed algorithm for extracting the modes in a signal and is based on the design of an adaptive wavelet filter bank. The proposed algorithm finds an optimum signal in the multivariate data set based on mode estimation strategy and then its corresponding spectra is segmented and utilized for extracting the modes across all the channels of the data set. Results: The proposed algorithm is able to find the common oscillatory modes within the multivariate data and can be applied for multichannel heterogeneous data analysis having unequal number of samples in different channels. The proposed algorithm was tested on different synthetic multivariate data and a real physiological trivariate data series of electrocardiogram, respiration, and blood pressure to justify its validation. Conclusions: In this article, the EWT is extended for multivariate signals and it was demonstrated that the component-wise processing of multivariate data leads to the alignment of common oscillating modes across the components.

11

Taking advantage of empirical mode decomposition in diagnosing gear faults

60%

Łazarz B. , Madej H. , Czech P.

Diagnostyka

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2009

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tom nr 1(49)

67-72

EN

The study presents the application of empirical mode decomposition as a tool useful in diagnosing faults in gears. The method is a modern algorithm used for non-linear and non-stationary signals. Using this algorithm, it is possible to decompose a signal into a finite sum of component called intrinsic mode functions (IMF). For each IMF, the number of extremes and the number of transitions through zero is equal or different, by maximum one, and the mean value of envelope determined by the signal extremes equals zero. In practice, natural signals do not meet these conditions. In the experiment, a gearbox operating in a circulating power system was used, with 16 and 24 pinion and wheel teeth, respectively. The measurements were carried out for a non-damaged gear and for a gear with a modelled fault, operating at various rotational speeds and under different loads.

PL

W opracowaniu przedstawiono zastosowanie empirycznej dekompozycji sygnału jako narzędzia przydatnego w diagnostyce uszkodzeń przekładni zębatych. Metoda ta jest nowoczesnym algorytmem stosowanym dla sygnałów nieliniowych i niestacjonarnych. Wykorzystując ten algorytm można rozłożyć sygnał na skończoną sumę składowych zwanych funkcjami wewnętrznymi (IMF). Dla każdego IMF liczba ekstremów i liczba przejść przez zero jest równa bądź różna o maksimum jeden, a wartość średnia obwiedni określonej przez ekstrema sygnału równa się zero. W praktyce naturalne sygnały nie spełniają tych warunków. W eksperymencie wykorzystano przekładnie zębatą pracującą w układzie mocy krążącej o licznie zębów zębnika i koła odpowiednio 16 i 24. Pomiary przeprowadzono dla przekładni nieuszkodzonej oraz z zamodelowanym uszkodzeniem, pracującej przy różnych prędkościach obrotowych i różnych obciążeniach.

12

Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions

60%

Zheng G. , Sun W. , Zhang H. , Zhou Y. , Gao C.

Eksploatacja i Niezawodność

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2021

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tom Vol. 23, no. 4

612--618

EN

Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.

13

Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks

60%

Lei Y. , Zhao D. , Cai H.

Artificial Satellites : Journal of Planetary Geodesy

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2016

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tom Vol. 51, No. 4

149--161

EN

It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques.

14

Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique

60%

Kumar S. , Panigrahy D. , Sahu P. K.

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tom Vol. 38, no. 2

297--312

EN

In this paper, the investigation on effectiveness of the empirical mode decomposition (EMD) with non-local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed. Differential standard deviation is calculated for collecting information related to the input noise so that appropriate formation in EMD and NLM framework can be performed. EMD framework in the proposed methodology is used for reduction of the noise from the ECG signal. The output of the EMD passes through NLM framework for preservation of the edges and cancel the noise present in the ECG signal after the EMD process. The performance of the proposed methodology has been validated by using added white and color Gaussian noise to the clean ECG signal from MIT-BIH arrhythmia database at different signal to noise ratio (SNR). The proposed denoising technique shows lesser mean of percent root mean square difference (PRD), mean square error (MSE), and better mean SNR improvement compared to other well-known methods at different input SNR. The proposed methodology also shows lesser standard deviation PRD, MSE, and SNR improvement compared to other well-known methods at different input SNR.

15

Speech Enhancement Using Sliding Window Empirical Mode Decomposition and Hurst-based Technique

60%

Poovarasan S. , Chandra E.

Archives of Acoustics

|

2019

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tom Vol. 44, No. 3

429--437

EN

The most challenging in speech enhancement technique is tracking non-stationary noises for long speech segments and low Signal-to-Noise Ratio (SNR). Different speech enhancement techniques have been proposed but, those techniques were inaccurate in tracking highly non-stationary noises. As a result, Empirical Mode Decomposition and Hurst-based (EMDH) approach is proposed to enhance the signals corrupted by non-stationary acoustic noises. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by considering the least corrupted IMF. Though it increases SNR, the time and resource consumption were high. Also, it requires a significant improvement under nonstationary noise scenario. Hence, in this article, EMDH approach is enhanced by using Sliding Window (SW) technique. In this SWEMDH approach, the computation of EMD is performed based on the small and sliding window along with the time axis. The sliding window depends on the signal frequency band. The possible discontinuities in IMF between windows are prevented by the total number of modes and the number of sifting iterations that should be set a priori. For each module, the number of lifting iterations is determined by decomposition of many signal windows by standard algorithm and calculating the average number of sifting steps for each module. Based on this approach, the time complexity is reduced significantly with suitable quality of decomposition. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.

16

An Improved EMD Method Based on Utilizing Certain Inflection Points in the Construction of Envelope Curves

60%

Kafil M. , Darabi K. , Ziaei-Rad S.

Archives of Acoustics

|

2023

|

tom Vol. 48, No. 3

389--401

EN

The empirical mode decomposition (EMD) algorithm is widely used as an adaptive time-frequency analysis method to decompose nonlinear and non-stationary signals into sets of intrinsic mode functions (IMFs). In the traditional EMD, the lower and upper envelopes should interpolate the minimum and maximum points of the signal, respectively. In this paper, an improved EMD method is proposed based on the new interpolation points, which are special inflection points (SIPn) of the signal. These points are identified in the signal and its first (n − 1) derivatives and are considered as auxiliary interpolation points in addition to the extrema. Therefore, the upper and lower envelopes should not only pass through the extrema but also these SIPn sets of points. By adding each set of SIPi (i = 1, 2, n) to the interpolation points, the frequency resolution of EMD is improved to a certain extent. The effectiveness of the proposed SIPn-EMD is validated by the decomposition of synthetic and experimental bearing vibration signals.

17

An improved performance of support vector machine to classify EEG motor imagery based on differential asymmetry

60%

Putranto Y. T. , Putra O. V. , Hafidz I. , Sardjono A. , Hariadi M. , Putranto Y. T.

Przegląd Elektrotechniczny

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2023

|

tom R. 99, nr 6

196--203

EN

One challenge in EEG motor imaging is th e low signal-to-noise ratio of brain signals. Its emergence in the accurate rendition of brain signals varies significantly from person to person. Here, we propose a framework to classify tasks based on fusion features using a Support Vector Machine. Our features are acquired from Discrete Wavelet Transform and Empirical Mode Decomposition. Subsequently, the disparity between measurements of left and right brain signals was calculated. Our proposed work significantly improves accuracy from 83.29 % to 93.16 % compared to previous work.

PL

Jednym z wyzwań w obrazowaniu motorycznym EEG jest niski stosunek sygnału do szumu sygnałów mózgowych. Jego pojawienie się w dokładnym przekazywaniu sygnałów mózgowych różni się znacznie w zależności od osoby. Tutaj proponujemy ramy do klasyfikowania zadań w oparciu o funkcje fuzji przy użyciu maszyny wektorów nośnych. Nasze funkcje są uzyskiwane z dyskretnej transformacji falkowej i dekompozycji trybu empirycznego. Następnie obliczono rozbieżność między pomiarami sygnałów lewego i prawego mózgu. Nasza proponowana praca znacznie poprawia dokładność z 83,29% do 93,16% w porównaniu z poprzednią pracą.

18

Energy of intrinsic mode function for gas-liquid flow pattern identification

60%

Sun Z. , Gong H.

Metrology and Measurement Systems

|

2012

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tom Vol. 19, nr 4

759-766

EN

Gas-liquid flows abound in a great variety of industrial processes. Correct recognition of the regimes of a gas-liquid flow is one of the most formidable challenges in multiphase flow measurement. Here we put forward a novel approach to the classification of gas-liquid flow patterns. In this method a flow-pattern map is constructed based on the average energy of intrinsic mode function and the volumetric void fraction of gas-liquid mixture. The intrinsic mode function is extracted from the pressure fluctuation across a bluff body using the empirical mode decomposition technique. Experiments adopting air and water as the working fluids are conducted in the bubble, plug, slug, and annular flow patterns at ambient temperature and atmospheric pressure. Verification tests indicate that the identification rate of the flow-pattern map developed exceeds 90%. This approach is appropriate for the gas-liquid flow pattern identification in practical applications.

19

Some characteristic wave energy dissipation patterns along the Polish coast

60%

Rozynski G. , Szmytkiewicz P.

|

tom 60

|

nr 4

20

Graphical representation and variability quantification of handwriting signals: New tools for Parkinson’s disease detection

60%

Dehghanpur Deharab E. , Ghaderyan P.

Biocybernetics and Biomedical Engineering

|

2022

|

tom Vol. 42, no. 1

158--172

EN

A reliable computer-aided method for Parkinson’s disease (PD) detection can slow down its progression and improve the life quality of patients. In this study, a new non-invasive and cost-effective method based on the online analysis of handwriting signals has been proposed. First, the dynamic handwriting signals have been converted into two graphical representations of the variability rate. Then, two new feasible features, including area of the analytic signal representation and area of the second-order difference plot, have been used to quantify the variability rate of handwriting signals. A statistical test and support vector machine classifier have been applied in a comparative study to test the impact of each variability feature, writing task, and time sequence on the detection performance, separately. The obtained results on PaHaW database with 35 Parkinson’s disease patients and 36 healthy controls have shown that the proposed method of handwriting variability feature extraction has effective performance and the capability for the PD detection. It has achieved an average sensitivity of 86.26% with only two types of features, providing a trade-off between the performance, the computational complexity, and interpretability of the motor patterns from the point of view of clinicians and neuropsychologists. Xcoordinate time-series and writing a sentence can achieve superior accuracy and robustness in the presence of individual differences. The experimental results have demonstrated that extracting the variability features that used graphical representations of the global changes in oscillatory mode has the ability to clinically describe the pathological dynamics of the handwriting signals for the PD identification.