scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

Feature Extraction of the Crack AE Signal Using HHT Transform

2013 ◽  
Vol 340 ◽  
pp. 441-444
Author(s):  
K.F. He ◽  
Z.J. Zhang ◽  
X.J. Li
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
Crack Depth ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Ae Signal

The use of Hilbert-Huang transform (Hilbert-Huang transform, HHT) on crack AE signal study, through empirical mode decomposition (empirical mode decomposition, EMD) AE signal is decompose into a number of intrinsic mode functions (Intrinsic mode Function, IMF), Hilbert spectrum and Hilbert marginal spectrum are calculated. The results show that crack depth structure bearing of acoustic emission are detected accurately by the number of acoustic emission events, time and crack the degree from Hilbert spectrum and Hilbert marginal spectrum.

A New Spectral Representation of Earthquake Recordings Using an Improved Hilbert-Huang Transform

2011 ◽  
Vol 255-260 ◽  
pp. 1671-1675
Author(s):  
Tian Li Huang ◽  
Wei Xin Ren ◽  
Meng Lin Lou
Keyword(s):  
Empirical Mode Decomposition ◽  
Spectral Representation ◽  
Low Frequency ◽  
Accurate Information ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Marginal Spectrum

A new spectral representation method of earthquake recordings using an improved Hilbert-Huang transform (HHT) is proposed in the paper. Firstly, the problem that the intrinsic mode functions (IMFs) decomposed by the empirical mode decomposition (EMD) in HHT is not exactly orthogonal is pointed out and improved through the Gram-Schmidt orthogonalization method which is referred as the orthogonal empirical mode decomposition (OEMD). Combined the OEMD and the Hilbert transform (HT) which is referred as the improved Hilbert-Huang transform (IHHT), the orthogonal intrinsic mode functions (OIMFs) and the orthogonal Hilbert spectrum (OHS) and the orthogonal Hilbert marginal spectrum (OHMS) are obtained. Then, the IHHT has been applied for the analysis of the El Centro earthquake recording. The obtained spectral representation result shows that the OHS gives more detailed and accurate information in a time–frequency–energy presentation than the Hilbert spectrum (HS) and the OHMS gives more faithful low-frequency energy presentation than the Fourier spectrum (FS) and the Hilbert marginal spectrum (HMS).

NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (01) ◽  
pp. 25-37 ◽  
Author(s):  
PO-HSIANG TSUI ◽  
CHIEN-CHENG CHANG ◽  
NORDEN E. HUANG
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
The Core ◽  
Mode Decomposition ◽  
Threshold Amplitude ◽  
The Mean ◽  
Mixing Problem ◽  
Emd Method

The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.

Acoustic Signals Analysis Based on Empirical Mode Decomposition and Spectrum Analysis Technique

2010 ◽  
Vol 40-41 ◽  
pp. 91-95 ◽  
Author(s):  
Yan Li Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Acoustic Signals ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Marginal Spectrum ◽  
Rock Interface ◽  
Coal Rock ◽  
Instantaneous Energy

A method to analyze the acoustic signals collected in fully-mechanized caving face is presented in this paper. Through analyzing the marginal spectrum and frequency spectrum of intrinsic mode functions obtained by empirical mode decomposition, acoustic signals’ frequency and amplitude characteristics are gotten, that is, high frequency signals about 1000Hz ~2800Hz are produced when the top coal is combined with gangue. Furthermore, the acoustic signals’ instantaneous energy spectrums in the frequency range of 1000Hz ~2800Hz can be used to identify the coal-rock interface.

WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION

2011 ◽  
Vol 03 (04) ◽  
pp. 509-526 ◽  
Author(s):  
R. FALTERMEIER ◽  
A. ZEILER ◽  
A. M. TOMÉ ◽  
A. BRAWANSKI ◽  
E. W. LANG
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Sampling Rate ◽  
Computational Effort ◽  
Intrinsic Mode Functions ◽  
Weighted Version ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Reconstruction Quality ◽  
Spectral Transform

The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, called Sliding empirical mode decomposition (SEMD), which, with a reasonable computational effort, extends the application area of EMD to a true on-line analysis of time series comprising a huge amount of data if recorded with a high sampling rate. Using nonlinear and nonstationary toy data, we demonstrate the good performance of the proposed algorithm. We also show that the new method extracts component signals that fulfill all criteria of an IMF very well and that it exhibits excellent reconstruction quality. The method itself will be refined further by a weighted version, called weighted sliding empirical mode decomposition (wSEMD), which reduces the computational effort even more while preserving the reconstruction quality.

Diagnosis of Ball Screw Preload Loss by Vibration Signals through the Hilbert-Huang Transform and Multiscale Measure

2011 ◽  
Vol 58-60 ◽  
pp. 636-641
Author(s):  
Yan Chen Shin ◽  
Yi Cheng Huang ◽  
Jen Ai Chao
Keyword(s):  
Machine Tool ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ball Screw ◽  
Multiscale Entropy ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Diagnosis Method

This paper proposes a diagnosis method of ball screw preload loss through the Hilbert-Huang Transform (HHT) and Multiscale entropy (MSE) process when machine tool is in operation. Maximum dynamic preload of 2% and 4% ball screws are predesigned, manufactured and conducted experimentally. Vibration signal patterns are examined and revealed by Empirical Mode Decomposition (EMD) with Hilbert Spectrum. Different preload features are extracted and discriminated by using HHT. The irregularity development of ball screw with preload loss is determined and abstracting via MSE based on complexity perception. The experiment results successfully show preload loss can be envisaged by the proposed methodology.

scholarly journals Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means

2020 ◽  
Vol 206 ◽  
pp. 03019
Author(s):  
Kun Zhao ◽  
Jisheng Ding ◽  
YanFei Sun ◽  
ZhiYuan Hu
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Side Scan Sonar ◽  
Sonar Image ◽  
Local Means ◽  
Residual Component ◽  
Mode Decomposition ◽  
Non Local ◽  
Bidimensional Empirical Mode Decomposition

In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

scholarly journals An Improvement in Representation of Audio Signal in Time-Frequency Plane using EMD-2TEMD Based Approach

2016 ◽  
Vol 44 ◽  
pp. 141-150
Author(s):  
Kazi Mahmudul Hassan ◽  
Md. Ekramul Hamid ◽  
Takayoshi Nakai
Keyword(s):  
Empirical Mode Decomposition ◽  
Source Separation ◽  
Audio Signal ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Separation Result ◽  
Mode Decomposition ◽  
Recent Approach ◽  
Non Stationary Signal

This study proposed an enhanced time-frequency representation of audio signal using EMD-2TEMD based approach. To analyze non-stationary signal like audio, timefrequency representation is an important aspect. In case of representing or analyzing such kind of signal in time-frequency-energy distribution, hilbert spectrum is a recent approach and popular way which has several advantages over other methods like STFT, WT etc. Hilbert-Huang Transform (HHT) is a prominent method consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA). An enhanced method called Turning Tangent empirical mode decomposition (2T-EMD) has recently developed to overcome some limitations of classical EMD like cubic spline problems, sifting stopping condition etc. 2T-EMD based hilbert spectrum of audio signal encountered some issues due to the generation of too many IMFs in the process where EMD produces less. To mitigate those problems, a mutual implementation of 2T-EMD & classical EMD is proposed in this paper which enhances the representation of hilbert spectrum along with significant improvements in source separation result using Independent Subspace Analysis (ISA) based clustering in case of audio signals. This refinement of hilbert spectrum not only contributes to the future work of source separation problem but also many other applications in audio signal processing.

BREATHING PATTERN RECOGNITION OF ABDOMINAL WALL MOVEMENT BY USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

2014 ◽  
Vol 06 (01) ◽  
pp. 1450002 ◽  
Author(s):  
YA-CHEN CHEN ◽  
TZU-CHIEN HSIAO ◽  
JU-HSIN HSU ◽  
JIN-LONG CHEN
Keyword(s):  
Empirical Mode Decomposition ◽  
Breathing Pattern ◽  
Recognition Rate ◽  
Ensemble Empirical Mode Decomposition ◽  
Feature Extraction Method ◽  
Statistical Representation ◽  
Wall Movement ◽  
Mode Decomposition ◽  
Abdominal Breathing ◽  
Physiological Benefits

Thoracic breathing (TB), abdominal breathing (AB), and mixing breathing are common respiratory functions. Individuals usually breathe thoracically, whereas the breathing pattern of AB is vague. Despite the statistical representation of the physiological benefits of AB, coping with a time-variant environment still remains challenging. Therefore, based on ensemble empirical mode decomposition (EEMD), this study compares the identification types of using R value, power proportion, and modified significant test (MST). Respiratory maneuver of 26 subjects results that MST varied with a paced breathing frequency is the highest accurate recognition rate of TB (80.8% in 0.2 Hz and 88.5% in 0.1 Hz) and of AB (73.1% in 0.2 and 0.1 Hz). Results of this study demonstrate that EEMD is an adaptive algorithm to decompose respiratory movement. Furthermore, MST is a highly promising feature extraction method for breathing type recognition.

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