Temat: EMD - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Zastosowanie Empirical Mode Decomposition do analizy zmian chropowatości w czasie skrawania
Application of Empirical Mode Decomposition to analysis of surface roughness variability when turning with cubic boron nitride tools
Autorzy:: Zawada-Tomkiewicz, A.
Powiązania:: https://bibliotekanauki.pl/articles/158448.pdf
Data publikacji:: 2010
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:: chropowatość
dekompozycja EMD
częstotliwości chwilowe
surface roughness
EMD decomposition
instantaneous frequencies
Opis:: Artykuł dotyczy analizy powierzchni po toczeniu ostrzami z regularnego azotku boru. Profile powierzchni obrobionej dla pierwszych 18 sekund skrawania zostały zebrane metodą stykową, a następnie przeanalizowane metodami tradycyjnymi oraz z wykorzystaniem Empirical Mode Decomposition (EMD). Opisywana metoda dokonuje dekompozycji na składowe, które określa się w literaturze mianem Intrinsic Mode Functions (IMFs), a które wynikają nie z cech funkcji bazowej, lecz tylko z cech sygnału. Zastosowanie dekompozycji EMD dało możliwość rozdzielenia danych profilu na zbiór składowych, które mają znaczenie fizyczne.
The paper introduces the topic of analyzing a machined surface after turning with use of Cubic Boron Nitride tools. Two different types of wedge material, TiN coated and uncoated, are discussed. For both of cutting tools the surface profiles for the first 18 seconds of cutting were collected with the stylus method, and then examined traditionally [1-3] as well as with use of Empirical Mode Decomposition (EMD). The EMD makes decomposition of the original signal into several components specified as Intrinsic Mode functions (IMFs) in the literature. IMFs are not determined by the basic function features, but only by signal characteristics [4]. Application of EMD to the surface profile has the ability to separate the profile data into a set of components which have physical meaning. EMD of the surface profile depends on the size and distribution of data and creates from a few to several IMFs. Most components are characterised by small energy and can be omitted in the analysis. The components of highest energy collected together form the surface roughness. These components responsible for creating roughness were analysed using Hilbert-Huang transform [5,6]. The analysis results show that the EMD method is suitable for decomposition of machined surface profiles and Hilbert-Huang Spectrum is appropriate for their description.
Źródło:: Pomiary Automatyka Kontrola; 2010, R. 56, nr 4, 4; 326-329
0032-4140
Pojawia się w:: Pomiary Automatyka Kontrola
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Wykrywanie luzu w układzie tłok-cylinder przy wykorzystaniu analizy EMD
Detection of clearance in the piston-cylinder assembly using EMD analysis
Autorzy:: Czech, P.
Madej, H.
Powiązania:: https://bibliotekanauki.pl/articles/258109.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy
Tematy:: diagnostyka
silnik spalinowy
analiza EMD
diagnosis
combustion engine
EMD analysis
empirical mode decomposition analysis
Opis:: W artykule przedstawiono próbę oceny zużycia złożenia tłok-cylinder za pomocą sygnału drgań rejestrowanego na kadłubie silnika ZI. Obiektem badań był czterocylindrowy silnik spalinowy o pojemności 1,2 dm3. W badaniach zastosowano metodę empirycznej dekompozycji jako nowe podejście do diagnozowania uszkodzeń silników spalinowych. Poprzez zastosowanie tej metody w przeprowadzonych badaniach złożony sygnał drgań bloku silnika został rozłożony na szereg poziomów dekompozycji, umożliwiających rozróżnienie lokalnych właściwości sygnału w dziedzinie czasu. Uzyskane sygnały z procesu dekompozycji poddano analizie widmowej, w celu określenia ich cech energetycznych. Analizę przeprowadzono dla różnych pasm częstotliwości. Z przeprowadzonych badań wynika, że istnieje możliwość wykorzystania analizy EMD do oceny luzu w układzie tłok-cylinder.
The paper presents an attempt to evaluate the wear of a piston-cylinder assembly with the aid of vibration signal recorded on the spark ignition (SI) engine body. The subject of the study was a four-cylinder combustion engine 1.2 dm3. In this research, an empirical mode decomposition (EMD) based approach for internal combustion engine fault diagnosis is investigated. EMD is a new time-frequency methods for analysing nonlinear and nonstationary signals generated by an IC engine. By using this method, the complicated vibration signal of the engine block was decomposed into an intrinsic mode function. It allowed us to differentiate local features of the vibration signal in time domain. Obtained signals were given under the analysis of the frequency domain to determine their energetic features. Analysis was conducted for various frequency bands. According to our studies, it is possible to utilise empirical mode decomposition (EMD) for the evaluation of the clearance in a piston-cylinder assembly.
Źródło:: Problemy Eksploatacji; 2008, 4; 65-72
1232-9312
Pojawia się w:: Problemy Eksploatacji
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 3.

Tytuł:: A novel hybrid framework to model the relationship of daily river discharge with meteorological variables
Autorzy:: Shabbir, Maha
Chand, Sohail
Iqbal, Farhat
Powiązania:: https://bibliotekanauki.pl/articles/31339501.pdf
Data publikacji:: 2023
Wydawca:: Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
Tematy:: LASSO
river discharge
ANN
SVM
EMD
Opis:: 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.
Źródło:: Meteorology Hydrology and Water Management. Research and Operational Applications; 2023, 11, 2; 70-94
2299-3835
2353-5652
Pojawia się w:: Meteorology Hydrology and Water Management. Research and Operational Applications
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 4.

Tytuł:: Tracking error analysis method of digital pulse power supply for heavy ion accelerator based on emd reconstruction
Autorzy:: Wang, Rongkun
Sun, Sigun
Hu, Bingtao
Powiązania:: https://bibliotekanauki.pl/articles/221317.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: pulse power supply
tracking error
EMD
signal reconstruction
Opis:: 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.
Źródło:: Metrology and Measurement Systems; 2020, 27, 2; 339-353
0860-8229
Pojawia się w:: Metrology and Measurement Systems
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 5.

Tytuł:: CNN and LSTM for the classification of parkinsons disease based on the GTCC and MFCC
Autorzy:: Boualoulou, Nouhaila
Drissi, Taoufiq Belhoussine
Nsiri, Benayad
Powiązania:: https://bibliotekanauki.pl/articles/30148250.pdf
Data publikacji:: 2023
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: Parkinson's disease
voice signal
GTCC
MFCC
DWT
EMD
CNN and LSTM
Opis:: Parkinson's disease is a recognizable clinical syndrome with a variety of causes and clinical presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 percent of Parkinson's disease sufferers have some form of early speech impairment, recent studies on tele diagnosis of Parkinson's disease have focused on the recognition of voice impairments from vowel phonations or the subjects' discourse. This paper presents a new approach for Parkinson's disease detection from speech sounds that are based on CNN and LSTM and uses two categories of characteristics. These are Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC) obtained from noise-removed speech signals with comparative EMD-DWT and DWT-EMD analysis. The proposed model is divided into three stages. In the first step, noise is removed from the signals using the EMD-DWT and DWT-EMD methods. In the second step, the GTCC and MFCC are extracted from the enhanced audio signals. The classification process is carried out in the third step by feeding these features into the LSTM and CNN models, which are designed to define sequential information from the extracted features. The experiments are performed using PC-GITA and Sakar datasets and 10-fold cross validation method, the highest classification accuracy for the Sakar dataset reached 100% for both EMD-DWT-GTCC-CNN and DWT-EMD-GTCC-CNN, and for the PC-GITA dataset, the accuracy is reached 100% for EMD-DWT-GTCC-CNN and 96.55% for DWT-EMD-GTCC-CNN. The results of this study indicate that the characteristics of GTCC are more appropriate and accurate for the assessment of PD than MFCC.
Źródło:: Applied Computer Science; 2023, 19, 2; 1-24
1895-3735
2353-6977
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 6.

Tytuł:: Diagnostic factors for opened and closed kinematic chain of vibroarthrography signals
Autorzy:: Machrowska, Anna
Karpiński, Robert
Krakowski, Przemysław
Jonak, Józef
Powiązania:: https://bibliotekanauki.pl/articles/118051.pdf
Data publikacji:: 2019
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: EMD
EEMD
knee joint
vibration
kinetic chain
staw kolanowy
wibracje
łańcuch kinetyczny
Opis:: 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.
Źródło:: Applied Computer Science; 2019, 15, 3; 34-44
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 7.

Tytuł:: A novel Parkinsons disease detection algorithm combined EMD, BFCC, and SVM classifier
Autorzy:: Boualoulou, Nouhaila
Mounia, Miyara
Nsiri, Benayad
Behoussine Drissi, Taoufiq
Powiązania:: https://bibliotekanauki.pl/articles/27313826.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:: EMD
BFCC
MFCC
SVM
Parkinson’s disease
sztuczna sieć neuronowa
choroba Parkinsona
Opis:: 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.
Źródło:: Diagnostyka; 2023, 24, 4; art. no. 2023404
1641-6414
2449-5220
Pojawia się w:: Diagnostyka
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 8.

Tytuł:: Vibration-based cavitation detection in centrifugal pumps
Autorzy:: Hajnayeb, A.
Azizi, R.
Ghanbarzadeh, A.
Changizian, M.
Powiązania:: https://bibliotekanauki.pl/articles/329512.pdf
Data publikacji:: 2017
Wydawca:: Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:: cavitation severity
centrifugal pump
vibration analysis
EEMD
EMD
DWT
kawitacja
pompa odśrodkowa
analiza drgań
Opis:: 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.
Źródło:: Diagnostyka; 2017, 18, 3; 77-83
1641-6414
2449-5220
Pojawia się w:: Diagnostyka
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 9.

Tytuł:: Tests of basic voice stress detection techniques
Autorzy:: Staroniewicz, Piotr
Powiązania:: https://bibliotekanauki.pl/articles/128166.pdf
Data publikacji:: 2019
Wydawca:: Politechnika Poznańska. Instytut Mechaniki Stosowanej
Tematy:: Voice Stress Analysis
Empirical Mode Decomposition
analiza napięcia głosowego
VSA
empiryczna dekompozycja sygnału
EMD
Opis:: The modern speech processing techniques enable new possibilities of potential applications. Besides speech and speaker recognition, also the information about speakers’ physical condition, emotional state or stress can be detected in speech signal. Since emotional stress can occur during deception, its detection in speech could be used for law or security services. The paper presents the comparative tests of two voice stress detection techniques: one based on trials of microtremors detection relying on an iterative EMD method (Empirical Mode Decomposition) and the second one based on the statistical analysis of fundamental frequency and MFCC parameters. The preliminary tests were carried on the group of 12 speakers (6 males and 6 females) answering yes/no to the list of a few dozen personal questions. The presented research revealed the speakers’ very high personal influence on the obtained results.
Źródło:: Vibrations in Physical Systems; 2019, 30, 1; 1-6
0860-6897
Pojawia się w:: Vibrations in Physical Systems
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 10.

Tytuł:: Speech Enhancement Using Sliding Window Empirical Mode Decomposition and Hurst-based Technique
Autorzy:: Poovarasan, Selvaraj
Chandra, Eswaran
Powiązania:: https://bibliotekanauki.pl/articles/176311.pdf
Data publikacji:: 2019
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:: speech enhancement
Empirical Mode Decomposition
EMD
Intrinsic Mode Functions
hurst exponent
Sliding Window
SW
Opis:: 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.
Źródło:: Archives of Acoustics; 2019, 44, 3; 429-437
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "EMD" wg kryterium: Temat

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język