scholarly journals An Analysis Method for Time-, Frequency-, and Energy-Domain Characteristics of Downhole Microseismic Signals and Its Application

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiao-xu Gao ◽  
Xiang-xu Pan ◽  
Guang-an Zhu
Keyword(s):  
Fault Plane ◽  
Middle Part ◽  
Favorable Effect ◽  
Discrete Wavelet ◽  
Analysis Method ◽  
One Dimensional ◽  
Time Frequency ◽  
Working Face ◽  
Transmitted Waves ◽  
Energy Domain

Based on SOS microseismic (MS) monitoring system of a certain mine, time-, frequency-, and energy-domain characteristics of MS signals are analyzed by comprehensively applying one-dimensional discrete Fourier transform (DFT) and one-dimensional discrete wavelet transform (DWT). The technology is employed to explore MS signals from incident and transmitted waves on the fault plane. The research results show that, within the time domain, the transmitted waves are found to have the rapidly reducing velocity and a few wave crests; within the frequency domain, the fault plane presents a low-pass filtering effect on incident waves; within the energy domain, the fault generally shows the vibration-attenuating effect on MS signals. The analysis method is used for the No. 3307 working face of a certain mine to realize the prediction on a buried fault in the middle part of the working face, and it is validated to have a favorable effect.

Research on Goniometer Fault Detection of a Certain Missile Based on Wavelet Analysis

2012 ◽  
Vol 490-495 ◽  
pp. 1600-1604
Author(s):  
Zhu Lin Wang ◽  
Jiang Kun Mao ◽  
Zi Bin Zhang ◽  
Xi Wei Guo
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Discrete Wavelet Transform ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Analysis Method ◽  
Frequency Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Fault Characteristic

Aiming at the problem of existing time-frequency analysis methods was not effective to goniometer keeping fault of a certain missile, combined time -frequency analysis method of CWT and DWT for the fault was put forward based on the fault characteristic. The process of the method proposed was given and the time-frequency method of continuous and discrete wavelet transform was analysed. The signal when goniometer keeping fault occurred was analysed by the method that was put forward. The simulation showed that the method which was effective to the fault detecting could accurately detect the time and location of goniometer fault occurred.

scholarly journals High-order compact LOD methods for solving high-dimensional advection equations

2021 ◽  
Vol 40 (3) ◽  
Author(s):  
Bo Hou ◽  
Yongbin Ge
Keyword(s):  
Truncation Error ◽  
Three Dimensional ◽  
Taylor Series Expansion ◽  
High Order ◽  
High Dimensional ◽  
Analysis Method ◽  
Order Accuracy ◽  
Von Neumann ◽  
One Dimensional ◽  
Von Neumann Analysis

AbstractIn this paper, by using the local one-dimensional (LOD) method, Taylor series expansion and correction for the third derivatives in the truncation error remainder, two high-order compact LOD schemes are established for solving the two- and three- dimensional advection equations, respectively. They have the fourth-order accuracy in both time and space. By the von Neumann analysis method, it shows that the two schemes are unconditionally stable. Besides, the consistency and convergence of them are also proved. Finally, numerical experiments are given to confirm the accuracy and efficiency of the present schemes.

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.

scholarly journals UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

2021 ◽  
Vol 13 (6) ◽  
pp. 1205
Author(s):  
Caidan Zhao ◽  
Gege Luo ◽  
Yilin Wang ◽  
Caiyun Chen ◽  
Zhiqiang Wu
Keyword(s):  
Recognition Performance ◽  
Principal Component ◽  
Texture Features ◽  
Training Model ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Dynamic Feature ◽  
Complex Environments ◽  
Time Frequency ◽  
Model Training

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

scholarly journals MULTIDIMENSIONAL REPRESENTATION OF AUTOMATIC DOSING DEVICE SIGNALS

2018 ◽  
pp. 26-32
Author(s):  
Denis Borisovich Fedosenkov ◽  
Anna Alekseevna Simikova ◽  
Boris Andreevich Fedosenkov ◽  
Stanislav Matveevich Kulakov
Keyword(s):  
Fourier Transform ◽  
Wigner Distribution ◽  
Low Frequency ◽  
Stationary Flow ◽  
Complex Model ◽  
Signal Spectrum ◽  
One Dimensional ◽  
Time Frequency ◽  
Cohen's Class ◽  
Discrete Values

The article describes the development of a special approach based on using multidimensional wavelet distributions principle to monitor and control the feed dozing processes in the mix preparation unit. As a key component, this approach uses the multidimensional time-frequency Wigner-Ville distribution, which is the part of Cohen's class distributions. The research focuses on signals characterizing mass transfer processes in the form of material flow measuring signals in relevant points of the unit. Wigner-Ville distribution has been shown in time terms as Fourier transform of products of multiplied parts of the signal under consideration for past and future time moments; corresponding distribution for the frequency spectrum is shown as Fourier transform of the products of signal parts for high-frequency and low-frequency fragments of the signal spectrum. It has been noted that when using a complex model of a dozing signal, discrete values (samples) of the latter are considered as its real values. The description of the signal parameters (amplitude, phase, frequency) has been carried out with the help of Hilbert transform. In Cohen's class distributions which represent one-dimensional non-stationary flow signals, the concept of ‘instantaneous frequency’ has been introduced. A graphical explanation for the transformation of a process flow signal from a one-dimensional time domain to a time-frequency 2 D/ 3 D -space is presented. The technology of developing a multidimensional image in the form of Wigner distribution for one-dimensional signals of continuous spiral or screw-type feeders has been examined in detail. There have been considered the features to support Wigner distribution, which allow to guess the presence or absence of time-frequency distribution elements in the interval of signal recording. There has been demonstrated how Wigner distribution can be obtained for a continuous-intermittent feeding signal. It has been concluded that for a certain types of the signal for zero fragments of the latter, non-zero time-frequency elements (i.e. virtual, anomalous ones) appear on the distribution. In addition to Wigner distribution, two other distributions - of Rihachek and Page - are considered. They display the same signal and also contain virtual elements, but in different domains of the time-frequency space. A generalized multidimensional compound signal distribution with a so-called distribution kernel available in it is presented, which includes a correction parameter that allows controlling the intensity of the virtual signal energy.

scholarly journals The wavelet transforms technique in the computer-aided system for controlling the quarry unmanned vehicles

2021 ◽  
Vol 315 ◽  
pp. 03022
Author(s):  
Ivan Chicherin ◽  
Boris Fedosenkov ◽  
Dmitriy Dubinkin ◽  
Wang Zhenbo
Keyword(s):  
Wavelet Transforms ◽  
Matching Pursuit ◽  
Effective Means ◽  
Unmanned Vehicles ◽  
Open Pit ◽  
Open Pit Mining ◽  
Frequency Distributions ◽  
One Dimensional ◽  
Time Frequency ◽  
Computer Aided

Introduction. Purpose of the work. Within the framework of the computer-aided system, a technology has been formed for the method of controlling the current trajectories (CTs) of unmanned vehicles (UMVs) when they move along routes in a quarry in open pit mining. The purpose of the presented studies is to analyze the application of a wavelet transforms technique to the problem of routing unmanned vehicles when they move along routes within open pit roads. Methodology. The results of modeling certain one-dimensional signals corresponding to the UMV current trajectories when they deviate to the left / right from a nominal axial trajectory (NAT), as well as their time-frequency representations in a wavelet medium are presented. An algorithm of the procedure for displaying scalar UMV CT control signals in a complex medium of time-frequency wavelet transforms has been developed and described. Such a transformation allows for a functionally transparent and information-capacious monitoring of the UMV movement and efficiently manage the processes of trajectory routing dump trucks in an open pit. Research results, analysis. The processes of modifying the UMV movement current trajectories under the control of the computer-aided system are generated using wavelet transforms methods. They are based on algorithms for projecting the trajectory signals with a time-dependent frequency (chirp signals) onto a set of wavelet functions as part of a wavelet thesaurus (wavelet dictionary), executing certain wavelet matching pursuit procedures, and displaying the CT scalar signals in a specific multidimensional medium of Cohen’s class time-frequency distributions. The simulation results in the form of the current trajectory (CT-) signals waveforms and their three-dimensional time-frequency representations as Wigner maps showing the UMV movement in a start-stop mode, as well as the signals of formed continuous deviation trajectories when they leave to the left and to the right from the NAT, are presented. An algorithm for the formation of 3D-representations of UMV current trajectory one-dimensional signals is presented. Conclusion. The conclusion is made that the mathematical technique of wavelet transforms is the most expedient and effective means for computer-aided monitoring and controlling the dynamics of UMV movement along routes within open pit roads.

scholarly journals Orthogonal-based wavelet analysis of wind turbulence and correlation between turbulence and forces

2007 ◽  
Vol 29 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Le Thai Hoa ◽  
Nguyen Dong Anh
Keyword(s):  
Wavelet Transforms ◽  
Discrete Wavelet ◽  
Time Frequency ◽  
Existing Problems ◽  
Physical Measurements ◽  
Force Relation ◽  
Wind Turbulence ◽  
Time Frequency Localization ◽  
Physical Simulations ◽  
Wavelet Decompositions

Recent models of wind turbulence and turbulence-force relation as well still contain uncertainties. Further studies on them are needed to gain the better knowledge to refine the existing problems from analytical computations to wind tunnel's physical simulations in the wind engineering. The continuous and discrete wavelet transforms have been applied as powerful transformation tools to represent time series into the time-frequency localization. This paper will apply the orthogonal-based wavelet decomposition to investigate the intermittency of the turbulence and to detect the turbulence-force correlation in the both temporal-spectral information using proposed cross energy of wavelet decompositions. Analyzing data have been obtained by physical measurements on model from the wind tunnel tests.

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.

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