scholarly journals Discrimination of Different AE and EMR Signals during Excavation of Coal Roadway Based on Wavelet Transform

Minerals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 63
Author(s):  
Baolin Li ◽  
Zhonghui Li ◽  
Enyuan Wang ◽  
Nan Li ◽  
Jing Huang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Rock Fracture ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Danger Signals ◽  
Long Period ◽  
Coal Roadway ◽  
Interference Signals ◽  
Precursor Information

During the process of coal road excavation, various interference signals, induced by environmental noise, drilling, and scraper loader, will affect the risk assessment of coal and gas outburst using acoustic emission (AE) and electromagnetic radiation (EMR) monitoring technology. To distinguish between different interference signals and danger signals, discrete wavelet transform (DWT) was used to decompose and reconstruct signals, and continuous wavelet transform (CWT) was used to obtain the time-frequency plane. The research results show that: (1) interference signals generally exhibit fluctuating changes within small ranges; in comparison, the intensity of AE and EMR signals caused by coal and rock fracture is found to continuously rise for a long period (longer than 2 h). (2) Different interference signals and danger signals differ significantly in their time-frequency plane. (3) Through decomposition and reconstruction of original signal, obvious precursor information can be found in the time-frequency plane of reconstructed signals.

scholarly journals Study on Response Characteristics of Surrounding Rock Rupture Microseismic Events during Coal Roadway Excavation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yajuan Wang ◽  
Dongji Lei ◽  
Yuanyuan Zheng ◽  
Tao Ma
Keyword(s):  
Total Energy ◽  
Rock Fracture ◽  
Surrounding Rock ◽  
Dynamic State ◽  
Efficient Production ◽  
Mining Activities ◽  
Time Frequency ◽  
Coal Roadway ◽  
Interference Signals ◽  
Microseismic Events

In order to solve the problems such as coal burst and abnormal gas overrun caused by the fracture of surrounding rock in the process of mining in the coal roadway, the ESG microseismic monitoring system is built on the driving face of 8005 transportation roadway of Wuyang Coal Mine to carry out a real-time, continuous, and omnidirectional dynamic state monitoring. In this way, the characteristics of time-frequency evolution and energy distribution of the acquired signals are systematically analyzed, and the location of the roadway microseismic events is studied. The results show the following: (1) influenced by mining activities, the localized microseismic events are mostly distributed in front and on both sides of the working face. Due to mining activities and geological changes, the equilibrium of original stress in coal and rock mass is broken. The stress thus is released accompanied by fracture before reaching a new equilibrium. (2) By comparing the coal and rock fracture signals with the interference signals, it is found that the fracture signals have short duration and large amplitude with obvious abrupt change characteristics. The interference signals have long duration and relatively small amplitude with less obvious change. (3) Fourier transform analysis shows that the main frequency of coal rock fracture signals is 100–200 Hz with large total energy concentrated in the first frequency band, while that of interference signals is mostly less than 100 Hz with small total energy. The research results can effectively identify the coal and rock dynamic disasters, provide technical support for the prediction and early warning of hidden danger, and guide the safe and efficient production.

scholarly journals Time Frequency Analysis of Wavelet and Fourier Transform

2020 ◽  
Author(s):  
Karlton Wirsing
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Efficient Access ◽  
The Fourier Transform

Signal processing has long been dominated by the Fourier transform. However, there is an alternate transform that has gained popularity recently and that is the wavelet transform. The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.

scholarly journals Comparative Application of Time-Frequency Methods on Strong Motion Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mohammad Ashtari Jafari
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Strong Motion ◽  
Energy Concentration ◽  
Signal Frequency ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Frequency Content ◽  
Fourier Decomposition ◽  
Time Frequency

Real-world physical signals are commonly nonstationary, and their frequency details change with time and do not remain constant. Fourier transform that uses infinite sine/cosine waves as basis functions represents frequency constituents of signals but does not show the variations of the signal frequency contents over time. Multiresolution demonstration of the time-frequency domain may be achieved by the techniques that can support adjustable resolution in time and frequency. Earthquake strong motion signals are nonstationary and indicate time-varying frequency content due to the scattering from the source to the site. In this paper, we applied short-time Fourier transform, S-transform, continuous wavelet transform, fast discrete wavelet transform, synchrosqueezing transform, synchroextracting transform, continuous wavelet synchrosqueezing, filter bank synchrosqueezing, empirical mode decomposition, and Fourier decomposition methods on the near-source strong motion signals from the 7 May 2020 Mosha-Iran earthquake to study and compare the frequency content of this event estimated by these methods. According to the results that are examined by Renyi entropy and relative error, synchroextracting performed better in terms of energy concentration, and the Fourier decomposition method revealed the lowest difference between the original and reconstructed records.

CONTINUOUS WAVELET ANALYSIS OF GENE EXPRESSION SIGNALS FOR CANCER CLASSIFICATION

2009 ◽  
Vol 17 (03) ◽  
pp. 377-396 ◽  
Author(s):  
XIN-PING XIE ◽  
XUAN-HAO DING ◽  
HONG-QIANG WANG ◽  
YING-CHUN JIANG
Keyword(s):  
Gene Expression ◽  
Wavelet Transform ◽  
Single Gene ◽  
Expression Patterns ◽  
Cancer Classification ◽  
Time Signal ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Simple Combination

This paper proposes a continuous wavelet transform (CWT)-based approach for extracting gene expression patterns associated with cancer. By viewing a particular arrangement of genes as a pseudo-time series and gene expression profile of a patient as a pseud-time signal, CWT can be used to extract hidden expression patterns for cancer classification. Generally, gene expression data are highly redundant and very noisy, and hidden expression patterns play crucial roles for cancer classification rather than any single gene or a simple combination of genes. The CWT can detect consistent patterns in a time-frequency manner, and is more powerful than discrete wavelet transform (DWT) due to the availability of more detail information. Experimental results on four publicly available gene expression datasets show the effectiveness and efficiency of the CWT in extracting useful expression patterns.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

2010 ◽  
Vol 49 (03) ◽  
pp. 230-237 ◽  
Author(s):  
K. Lweesy ◽  
N. Khasawneh ◽  
M. Fraiwan ◽  
H. Wenz ◽  
H. Dickhaus ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Sleep Stage ◽  
Continuous Wavelet ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Mother Wavelets ◽  
Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

scholarly journals Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Timur Düzenli ◽  
Nalan Özkurt
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Vanishing Moments ◽  
Zero Crossings ◽  
Time Frequency ◽  
Common Time ◽  
Classification Tool

The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.

scholarly journals Ground Penetrating Radar for Belowground Phenotyping of High-biomass Grasses for Soil Carbon Sequestration

2021 ◽  
Author(s):  
Matthew Wolfe ◽  
Da Huo ◽  
Henry Ruiz-Guzman ◽  
Brody Teare ◽  
Tyler Adams ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Carbon Capture ◽  
Large Scale ◽  
Agricultural Practices ◽  
Signal Amplitude ◽  
Belowground Biomass ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Non Invasive

Abstract AimsMany governments and companies have committed to moving to net-zero emissions by 2030 or 2050 to tackle climate change, which require the development of new carbon capture and sequestration/storage (CCS) techniques. A proposed method of sequestration is to deposit carbon in soils as plant matter including root mass and root exudates. Adding perennial traits such as rhizomes to crops as part of a sequestration strategy would result in annual crop regrowth from rhizome meristems rather than requiring replanting from seeds which would in turn encourage no-till agricultural practices. Integrating these traits into productive agriculture requires a belowground phenotyping method compatible with high throughput breeding and selection methods (i.e., is rapid, inexpensive, reliable, and non-invasive), however none currently exist. MethodsGround penetrating radar (GPR) is a non-invasive subsurface sensing technology that shows potential as a phenotyping technique. In this study, a prototype GPR antenna array was used to scan roots of the perennial sorghum hybrid, PSH09TX15. A-scan level time-domain analyses and B-scan level time/frequency analyses using the continuous wavelet transform were utilized to extract features of interest from the acquired radargrams. ResultsOf six A-scan diagnostic indices examined, the standard deviation of signal amplitude correlated most significantly with belowground biomass. Time frequency analysis using the continuous wavelet transform yielded high correlations of B-scan features with belowground biomass. ConclusionThese results demonstrate that continued refinement of GPR data analysis workflows should yield a highly applicable phenotyping tool for breeding efforts in environments where selection is otherwise impractical on a large scale.

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