Wyniki wyszukiwania - Biblioteka Nauki

1

Ocena stanu lotniczego zespołu napędowego z użyciem algorytmu EMD

100%

Jonak J. , Machrowska A.

Zeszyty Naukowe. Mechanika / Politechnika Opolska

|

2015

|

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

Diagnostyka elementów środków transportu z wykorzystaniem dekompozycji empirycznej na przykładzie łożysk tocznych

85%

Majkut L. , Marczuk R.

Logistyka

|

2009

|

tom nr 6

PL

Dekompozycja empiryczna (ang. Empirical Mode Decomposition – EMD) jest czasowo-częstotliwościową transformatą sygnału, której bazą jest sam sygnał. W wyniku opisanego w pracy algorytmu EMD sygnał rozkładany jest na tzw. Intrinsic Mode Functions (IMFs), które są funkcjami ortogonalnymi. W pracy zastosowano opisaną wyżej procedurę do diagnostyki uszkodzeń łożysk tocznych. W eksperymencie diagnostycznym uszkodzone zostały kulki i bieżnia wewnętrzna metodą obróbki elektroerozyjnej (ang. EDM - Electrical Discharge Machining). Pomiarów dokonano przy czterech rożnych wartościach poziomu uszkodzenia, a jako symptom uszkodzenia w łożysku wybrano przebieg zmian kąta fazowego sygnału analitycznego pierwszej funkcji IMF.

EN

Empirical Mode Decomposition (EMD) is a time-frequency transformation of signal where base are some components in the signal. The key feature of EMD is to decompose a signal into so-called intrinsic mode functions (IMFs), who are orthogonal. In this work EMD decomposition was used for diagnostics of rolling bearings faults. In diagnostic experiment balls and inner raceway was damaged by Electrical Discharge Machining (EDM). Measurements was made for four levels of damage. As a failure mode selected change phase angle of first IMF function’s analytic signal in time.

3

Tracking error analysis method of digital pulse power supply for heavy ion accelerator based on emd reconstruction

80%

Wang R. , Sun S. , Hu B.

Metrology and Measurement Systems

|

2020

|

tom Vol. 27, nr 2

339--353

EN

Assessment of the state of a pulse power supply requires effective and accurate methods to measure and reconstruct the tracking error. This paper proposes a tracking error measurement method for a digital pulse power supply. A de-noising algorithm based on Empirical Mode Decomposition (EMD) is used to analyse the energy of each Intrinsic Mode Function (IMF) component, identify the turning point of energy, and reconstruct the signal to obtain the accurate tracking error. The effectiveness of this EMD method is demonstrated by simulation and actual measurement. Simulation was used to compare the performance of time domain filtering, wavelet threshold de-noising, and the EMD de-noising algorithm. In practical use, the feedback of current on the prototype of the power supply is sampled and analysed as experimental data.

4

Wykorzystanie EMD w diagnostyce uszkodzeń kół zębatych

71%

Łazarz B. , Madej H. , Czech P.

Zeszyty Naukowe. Transport / Politechnika Śląska

|

2005

|

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.

5

Evaluation of fractured–vuggy reservoir by electrical imaging logging based on a de noising method

71%

Xu F. , Wang Z. , Wang W.

Acta Geophysica

|

2021

|

tom Vol. 69, no. 3

761--772

EN

The existence of fractures and vugs in igneous formation is a key factor to determine the productivity of oil and gas reservoirs. Fracture–vug plane porosity and porosity spectrum (fracture–vug parameters) are important parameters to evaluate the development of fractures and vugs. In the process of drilling, the bit forms shallow holes and scratches on the borehole wall which is characterized by pitting, strip and block noise in the electrical imaging logging static image. The background noise afects the identifcation of fractures and vugs and the extraction of parameters. It is found that the background noise mainly exists in the high-frequency conductivity data. In order to suppress the background noise, empirical mode decomposition is applied to conductivity data of electrical imaging logging, and the wavelet hard threshold de-noising is applied to high-frequency intrinsic mode function components. The de-noising fracture-vug parameters have a good correspondence with the electrical imaging logging static image, and have a better linear relationship with the core porosity. These illustrate that the application of the de-noising method in the electrical imaging logging is reasonable and efective. The de-noising porosity spectrum becomes narrower in the reservoir with poor fractures and vugs, which can reveal the development of secondary pores more clearly. In reservoir interpretation, the de-noising fracture-vug plane porosity and porosity spectrum have good consistency with conventional and acoustic logging data, which can efectively evaluate the fractures and vugs in reservoirs.

6

Diagnostic factors for opened and closed kinematic chain of vibroarthrography signals

71%

Machrowska A. , Karpiński R. , Krakowski P. , Jonak J.

Applied Computer Science

|

2019

|

tom Vol. 15, no 3

34--44

EN

The paper presents results of preliminary research of vibroarthrography signals recorded from one healthy volunteer. The tests were carried out for the open and closed kinematic chain in the range of motion 90° – 0° – 90°. Analysis included initial signal filtration using the EMD algorithm. The aim was to investigate the occurrence of differences in the values of selected energy and statistical parameters for the cases studied.

7

Ranking of Sudden Ionospheric Disturbances by Means of the Duration of Vlf Perturbed Signal in Agreement with Satellite X-Ray Flux Classification

71%

Ammar A. , Ghalila H.

Acta Geophysica

|

2016

|

tom Vol. 64, no. 6

2794--2809

EN

Ionosphere undergoes permanently solar flares that quickly change its properties inducing sometime unwanted effects. These changes, or events, are known as Sudden Ionospheric Disturbances (SIDs) and the knowledge of their magnitude may be of great interest to anticipate probable damages. Currently, there does not exist any classification of these ionospheric changes based on their amplitude due to the wide variability of its responses. The only way to surmise their importance is to study them indirectly, throughout the classification of the X-ray flux intensity recorded by satellites. An attempt of classification based on their duration was proposed by the American Association of Variable Star Observers (AAVSO) but it is not very accurate because SID’s duration is measured directly from the raw signal of the Very Low Frequency (VLF) signal and/or the Low Frequency (LF) signal. The aim of this work is to investigate, through a set of simple mathematical techniques applied to VLF/LF signals recorded by ground based receivers, the best method to estimate SIDs durations and then propose a new classification based on these durations.

8

Analysis of effectiveness and computational complexity of trend removal methods

71%

Lentka Ł. , Smulko J.

|

tom Nr 51

111--114

EN

The paper presents a method of processing measurement data due to remove slowly varying component of the trend occurring in the recorded waveforms. Comparison of computational complexity and trend removal efficiency between some commonly used methods is presented. The impact of these procedures on probability distribution and power spectral density is shown. Effectiveness and computational complexity of these methods depend essentially on nature of the removed trend. This paper describes several procedures: Moving Average Removal (MAR), fitting a polynomial of degree appropriate to the analyzed data, Empirical Mode Decomposition (EMD).

PL

W pracy przedstawiono sposób przetwarzania danych pomiarowych w celu usunięcia wolnozmiennej składowej trendu występującego w rejestrowanych przebiegach. Porównano kilka często stosowanych w tym celu metod pod względem ich złożoności obliczeniowej oraz skuteczności w usuwaniu trendu. Pokazano wpływ tych procedur na rozkład prawdopodobieństwa wartości chwilowych oraz przebieg gęstości widmowej mocy. W ogólności operację usuwania trendu możemy traktować jako filtrację górnoprzepustową danych pomiarowych. W celu usunięcia trendu można użyć filtru górnoprzepustowego (analogowego lub cyfrowego) już na etapie akwizycji danych pomiarowych. Jednakże często mamy do czynienia z danymi, w których składowa trendu jest potrzebna do przeprowadzania innych analiz i nie może być usunięta na etapie rejestracji danych pomiarowych. Ponadto, może mieć charakter niestacjonarny i metody filtracji górnoprzepustowej nie będą skuteczne. W takich przypadkach należy rozważyć inne, często bardziej zaawansowane metody. Skuteczność i złożoność obliczeniowa takich metod zależy istotnie od charakteru usuwanego trendu. W pracy opisano procedurę usuwania średniej kroczącej (ang. Moving Average Removal – MAR), metody o niskiej złożoności obliczeniowej, ale dającej zadowalające rezultaty w dużej liczbie potencjalnych zastosowań. Rozważono usuwanie trendu przez dopasowanie wielomianem odpowiedniego stopnia do analizowanych danych pomiarowy. Procedura ta może być powtarzana kilkukrotnie, nawet ze zwiększaniem stopnia wielomianu przy każdym z kroków, aż do uzyskania przebiegu, w którym usunięto składową trendu. Część pracy poświęcono prezentacji bardziej złożonych obliczeniowo metod, które zostały rozwinięte dopiero w ostatnich latach i wymagają znacznie bardziej intensywnych obliczeń.

9

Selection of an efficient feature space for EEG-based mental task discrimination

71%

Noshadi S. , Abootalebi V. , Sadeghi M. T. , Shahvazian M. S.

Biocybernetics and Biomedical Engineering

|

2014

|

tom Vol. 34, no. 3

159--168

EN

The aim of this paper is to contribute toward exploring an optimal feature space for discriminating mental tasks. Empirical mode decomposition (EMD) algorithm seems useful for designing such a feature space. The adjustment of nonlinear and non-stationary properties of the EEG signals with this algorithm and the successful application of this approach together biomedical signal processing problems encourage us to examine a variety of statistical and spectral measures within the EMD space as the adapted features. In this sense, as a measure of complexity, the Lempel–Ziv algorithm is utilized within the frame-work of the EMD algorithm. A modified form of the Lempel–Ziv complexity algorithm is then proposed. The features derived from the modified algorithm outperform the other features individually. By combining the modified Lempel–Ziv features with the other adopted features, in average, 97.78% classification accuracy is achieved for different subjects. It is concluded that the EMD–LZ kernel allows for achieving of better performances in classifying mental tasks than the results obtained with other methods.

10

A novel hybrid framework to model the relationship of daily river discharge with meteorological variables

70%

Shabbir M. , Chand S. , Iqbal F.

Meteorology Hydrology and Water Management. Research and Operational Applications

|

2023

|

tom Vol. 11, Iss. 2

70--94

EN

River discharge is affected by many factors, such as water level, rainfall, and precipitation. This study proposes a new hybrid framework named LAES (LASSO-ANN-EMD-SVM) to model the relationship of daily river discharge with meteorological variables. This hybrid framework is a composite of the least absolute shrinkage and selection operator (LASSO), an artificial neural network (ANN), and an error correction method. In the first stage, LASSO identifies meteorological variables that have a significant influence on the generation of river discharge. Next, the ANN model is used to predict river discharge using meteorological variables selected by LASSO, and the error series is determined. The error series is decomposed into intrinsic mode functions and residuals using empirical mode decomposition (EMD). The EMD components are modeled using the support vector machine (SVM) model, and the error predictions are aggregated. In the last stage, the LASSO-ANN predictions and the predicted error series are aggregated as the final discharge prediction. The proposed hybrid framework is illustrated on the Kabul River of Pakistan. The performance of the proposed hybrid framework is compared with six models using various performance measures and the Diebold-Mariano test. These models include multiple linear regression (MLR), SVM, ANN, LASSO-MLR, LASSO-SVM, and LASSO-ANN models. The findings reveal that the proposed hybrid model outperforms all other models considered in the study. In the testing phase, the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) of the proposed LAES hybrid model are 337.143 m3/s, 32.354%, and 218.353 m3/s which are smaller than all other models compared in the study. Our proposed hybrid system is an efficient model for river discharge prediction that will be helpful in water management and protection against floods. Long-term prediction can help to identify the major effects of climate change and to make evidence-based environmental policies.

11

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

|

2018

|

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.

12

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

|

2016

|

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.

13

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

60%

Poovarasan S. , Chandra E.

Archives of Acoustics

|

2019

|

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.

14

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.

15

Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm

57%

Bavkar S. , Iyer B. , Deosarkar S.

Biocybernetics and Biomedical Engineering

|

2021

|

tom Vol. 41, no. 1

83--96

EN

Alcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal subset of EEG channels for alcoholism detection is a challenging task. The paper reports a new methodology for the detection of optimal channels for alcoholism analysis using EEG data. The proposed technique employs the Empirical Mode Decomposition (EMD) technique to extract the amplitude and frequency modulated bandwidth features from the Intrinsic Mode Function (IMF) and ensemble subspace K-NN as a classifier to classify alcoholics and normal. The optimum channels are selected, using a harmony search algorithm. The fitness value of discrete binary harmony search (DBHS) optimization algorithms is calculated using accuracy and sensitivity achieved by the ensemble subspace K-Nearest Neighbor classifier. Experimental outcomes indicate that the optimal channel selected by the harmony search algorithm has biological inference related to the alcoholic subject. The proposed approach reports a classification accuracy of 93.87%, with only 12 detected EEG channels.

16

A novel Parkinson's disease detection algorithm combined EMD, BFCC, and SVM classifier

51%

Boualoulou N. , Mounia M. , Nsiri B. , Behoussine Drissi T.

|

2023

|

tom Vol. 24, No. 4

art. no. 2023404

EN

Identifying and assessing Parkinson's disease in its early stages is critical to effectively monitoring the disease's progression. Methodologies based on machine learning enhanced speech analysis are gaining popularity as the potential of this field is revealed. Acoustic features, in particular, are used in a variety of algorithms for machine learning and could serve as indicators of the general health of subjects' voices. In this research paper, a novel method is introduced for the automated detection of Parkinson's disease through speech signal analysis, a support vector machines classifier (SVM) and an Artificial Neural Network (ANN) are used to evaluate and classify the data based on two acoustic features: Bark Frequency Cepstral Coefficients (BFCC) and Mel Frequency Cepstral Coefficients (MFCC). These features are extracted from the denoised signals using Empirical Mode Decomposition (EMD). The most relevant results obtained for a dataset of 38 participants are by the BFCC coefficients with an accuracy up to 92.10%. These results confirm that EMD-BFCC-SVM method can contribute to the detection of Parkinson's disease.

17

Różnicowy układ pracy fotodetektora promieniowania optycznego

51%

Gilewski M.

Przegląd Elektrotechniczny

|

2009

|

tom R. 85, nr 11

249-252

PL

W publikacji przedstawiono koncepcję różnicowego układu pracy fotodetektora półprzewodnikowego. Zaproponowano nową strukturę układu zawierającą: jeden lub dwa fotodetektory, dwa wzmacniacze transimpedancyjne oraz różnicowy wzmacniacz wyjściowy. Proponowane rozwiązanie układu detekcji promieniowania jest bardziej czułe i mniej wrażliwe na zakłócenia niż pojedyńczy konwerter prąd - napięcie. W pracy przedstawiono wyniki badań eksperymentalnych oraz wskazówki dotyczące rozwiązań aplikacyjnych.

EN

This paper describes a differential detection circuit of optical radiation. The circuit contains: one or two silicon photodetectors, two transimpedance amplifiers and output differential amplifier. This solution of light detection is more sensitive and less susceptible to EMD than single transimpedance amplifier. The measurement results of the circuit are presented. At the final part the design recommendations are included.

18

EMD Method Applied to Identification of Logging Sequence Strata

51%

Zhao N. , Li R.

Acta Geophysica

|

2015

|

tom Vol. 63, no. 5

1256--1275

EN

In this work, we compare Fourier transform, wavelet transform, and empirical mode decomposition (EMD), and point out that EMD method decomposes complex signal into a series of component functions through curves of local mean value. Each of Intrinsic Mode Functions (IMFs - component functions) contains all the information on the original signal. Therefore, it is more suitable for the interface identification of logging sequence strata. Well logging data reflect rich geological information and belong to non-linear and non-stationary signals and EMD method can deal with non-stationary and non-linear signals very well. By selecting sensitive parameters combination that reflects the regional geological structure and lithology, the combined parameter can be decomposed through EMD method to study the correlation and the physical meaning of each intrinsic mode function. Meanwhile, it identifies the stratigraphy and cycle sequence perfectly and provides an effective signal treatment method for sequence interface.

19

Computer-aided design and simulation of double-band filters for radio systems

51%

Czawka G.

|

tom R. 88, nr 9b

278-279

EN

Mathematical model of multiband frequency transformation, synthesis of multiband networks and computer-aided design of double-band filters without of any mechanical commutations are presented in this paper. Multiband filters have a several transmitted frequency bands and some attenuation bands. Computer synthesis program Multiband and synthesis of double-band electric filters are presented.

PL

Model matematyczny wielopasmowej transformacji częstotliwości, synteza obwodów wielopasmowych, projektowanie komputerowe i synteza za pomocą programu Multiband dwupasmowych filtrów bez żadnych komutacji mechanicznych przedstawiono w artykule.

20

Vibration-based cavitation detection in centrifugal pumps

51%

Hajnayeb A. , Azizi R. , Ghanbarzadeh A. , Changizian M.

Diagnostyka

|

2017

|

tom Vol. 18, No. 3

77--83

EN

Cavitation is a common cause of failure in centrifugal pumps. Because of interaction of several mechanical parts and fluid, the vibration signal of a centrifugal pump is complicated. In this paper, the vibrations of a transparent-casing centrifugal pump are studied. Three states are studied experimentally: no cavitation, limited cavitation and developed cavitation. Each case was also confirmed by visually inspecting the cavitation bubbles. The vibrations of the pump was acquired by using an accelerometer that was attached to the casing. Discrete wavelet transform (DWT) analysis and empirical mode decomposition (EMD) are used to extract classification features from the acquired signals. Using these features, an artificial neural network (ANN) successfully diagnosed the cavitation condition of the pump. Finally, EEMD is also implemented. The results showed the success of EMD and DWT in cavitation diagnosis. The output of EEMD does not show significant change comparing to EMD.