Time-Frequency Characteristics and the Influence Mechanism of the EMR from Coal with Different Joint Angles

It is vital to understand the electromagnetic radiation’s time-frequency characteristics in the process of coal and rock failure with different joint angles in order to reveal the generation mechanism of the electromagnetic radiation (EMR) and improve the accuracy of EMR early warning. We studied the time-frequency characteristics of EMR signals of coal samples with different joint angles. The study finds that, (1) with the increase of joint angle, the failure time and peak load of samples decrease first and increase later, and the postpeak failure time decreases gradually. The EMR counts’ peak value showed a slow rise, a sharp rise, and a slow rise in the three intervals of α = 0° to 45°, 45° to 60°, and 60° to 90°, respectively. The accumulated EMR counts showed a steady upward trend. The duration of the EMR waveform, the dominant frequency of the EMR, and the peak number of the frequency spectrum of coal samples are on the rise. (2) As the joint angle increases, the samples’ failure mode changes from the stage fracture dominated by tension cracks to the rapid fracture with the coexistence of shear and tension cracks and finally to the burst fracture which produces a large number of fragments. This is also the main reason for the difference of the EMR generation mechanism and the signal of samples with different joint angles. (3) According to the experimental results, we established the modified formulas for calculating the EMR threshold value and deviation of coal and rock with joints under different stress environments and revealed that the longer the EMR waveform duration, the higher the dominant frequency, and the more the number of spectrum peaks, the greater the burst risk of coal and rock.

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Experimental Investigation on Time-Frequency Characteristics of Microseismic Signals in the Damage Evolution Process of Coal and Rock

Energies ◽

10.3390/en13040809 ◽

2020 ◽

Vol 13 (4) ◽

pp. 809

Author(s):

Wei Yang ◽

Chengwu Li ◽

Rui Xu ◽

Xunchang Li

Keyword(s):

Damage Evolution ◽

Resonance Effect ◽

Low Frequency ◽

Dominant Frequency ◽

Frequency Characteristics ◽

Evolution Process ◽

Deformation And Failure ◽

Time Frequency ◽

Rock Materials ◽

Damage Degree

The deformation and failure of coal and rock materials is the primary cause of many engineering disasters. How to accurately and effectively monitor and forecast the damage evolution process of coal and rock mass, and form a set of prediction methods and prediction indicators is an urgent engineering problems to be solved in the field of rock mechanics and engineering. As a form of energy dissipation in the deformation process of coal and rock, microseismic (MS) can indirectly reflect the damage of coal and rock. In order to analyze the relationship between the damage degree of coal and rock and time-frequency characteristics of MS, the deformation and fracture process of coal and rock materials under different loading modes was tested. The time-frequency characteristics and generation mechanism of MS were analyzed under different loading stages. Meanwhile, the influences of properties of coal and rock materials on MS signals were studied. Results show that there is an evident mode cutoff point between high-frequency and low-frequency MS signals. The properties of coal and rock, such as the development degree of the original fracture, particle size and dense degree have a decisive influence on the amplitude, frequency, energy and other characteristic parameters of MS signals. The change of MS parameters is closely related to material damage, but has no strong relation with the loading rate. The richness of MS signals before the main fracture depends on the homogeneity of materials. With the increase of damage, the energy release rate increases, which can lead to the widening of MS signals spectrum. The stiffness and natural frequency of specimens decreases correspondingly. Meanwhile, the main reason that the dominant frequency of MS detected by sensors installed on the surface of coal and rock materials is mainly low-frequency is friction loss and the resonance effect. In addition, the spectrum and energy evolution of MS can be used as a characterization method of the damage degree of coal and rock materials. Furthermore, the results can provide important reference for prediction and early warning of some rock engineering disasters.

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Analysis about Effect of Hollow on Time-Frequency Characteristic of Surface Vibration Signal

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.533.181 ◽

2014 ◽

Vol 533 ◽

pp. 181-186

Author(s):

Ming Sheng Zhao ◽

En An Chi ◽

Qiang Kang ◽

Tie Jun Tao

Keyword(s):

Vibration Signal ◽

Dominant Frequency ◽

Frequency Characteristics ◽

Distribution Method ◽

Vibration Signals ◽

Band Energy ◽

Surface Vibration ◽

Time Frequency ◽

Working Face ◽

Blasting Excavation

In blasting excavation of shallow tunnel, the surface vibration of excavated tunnel can be amplified due to effect of hollow. This effect is an important factor for safety of surface buildings. Based on the measured data of one tunnel excavation project, combining wavelet analysis and AOK time-frequency distribution method, the surface vibration signals in front and rear position of working face are processed into different frequency bands. Taking PPV, dominant frequency, d7 (7.8125-15.625 Hz) band energy ratio and d7 (7.8125-15.625 Hz) band energy duration as indexes, the effect of hollow on time-frequency characteristics of surface vibration signal is studied in this article. The results show that, affected by the hollow in excavated region, the PPV and dominant frequency increase, and the d7 (7.8125-15.625 Hz) band energy shows fluctuant ratio of total energy and an increase of band energy duration. The results show that the hollow influence on the frequency characteristics of the surface vibration signals comprehensively, and also provide an analytical basis for anti-vibration and vibration reduction study from the angle of energy.

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Genome-Wide Association Studies Based on Equine Joint Angle Measurements Reveal New QTL Affecting the Conformation of Horses

Genes ◽

10.3390/genes10050370 ◽

2019 ◽

Vol 10 (5) ◽

pp. 370 ◽

Cited By ~ 5

Author(s):

Annik Imogen Gmel ◽

Thomas Druml ◽

Rudolf von Niederhäusern ◽

Tosso Leeb ◽

Markus Neuditschko

Keyword(s):

Joint Angle ◽

Association Studies ◽

Genome Wide Association ◽

Cranial Morphology ◽

Genome Wide Association Studies ◽

Joint Angles ◽

Mineral Density ◽

Mixed Effect ◽

Stifle Joint ◽

Genome Wide

The evaluation of conformation traits is an important part of selection for breeding stallions and mares. Some of these judged conformation traits involve joint angles that are associated with performance, health, and longevity. To improve our understanding of the genetic background of joint angles in horses, we have objectively measured the angles of the poll, elbow, carpal, fetlock (front and hind), hip, stifle, and hock joints based on one photograph of each of the 300 Franches-Montagnes (FM) and 224 Lipizzan (LIP) horses. After quality control, genome-wide association studies (GWASs) for these traits were performed on 495 horses, using 374,070 genome-wide single nucleotide polymorphisms (SNPs) in a mixed-effect model. We identified two significant quantitative trait loci (QTL) for the poll angle on ECA28 (p = 1.36 × 10−7), 50 kb downstream of the ALX1 gene, involved in cranial morphology, and for the elbow joint on ECA29 (p = 1.69 × 10−7), 49 kb downstream of the RSU1 gene, and 75 kb upstream of the PTER gene. Both genes are associated with bone mineral density in humans. Furthermore, we identified other suggestive QTL associated with the stifle joint on ECA8 (p = 3.10 × 10−7); the poll on ECA1 (p = 6.83 × 10−7); the fetlock joint of the hind limb on ECA27 (p = 5.42 × 10−7); and the carpal joint angle on ECA3 (p = 6.24 × 10−7), ECA4 (p = 6.07 × 10−7), and ECA7 (p = 8.83 × 10−7). The application of angular measurements in genetic studies may increase our understanding of the underlying genetic effects of important traits in equine breeding.

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Experimental and Numerical Study of Fracture Behavior of Rock-Like Material Specimens with Single Pre-Set Joint under Dynamic Loading

Materials ◽

10.3390/ma14102690 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2690

Author(s):

Bo Pan ◽

Xuguang Wang ◽

Zhenyang Xu ◽

Lianjun Guo ◽

Xuesong Wang

Keyword(s):

Crack Initiation ◽

Crack Propagation ◽

Joint Angle ◽

Simulation Software ◽

Dissipated Energy ◽

Energy Structure ◽

Joint Angles ◽

Crack Penetration ◽

Before And After ◽

The Impact

The Split Hopkinson Pressure Bar (SHPB) is an apparatus for testing the dynamic stress-strain response of the cement mortar specimen with pre-set joints at different angles to explore the influence of joint attitudes of underground rock engineering on the failure characteristics of rock mass structure. The nuclear magnetic resonance (NMR) has also been used to measure the pore distribution and internal cracks of the specimen before and after the testing. In combination with numerical analysis, the paper systematically discusses the influence of joint angles on the failure mode of rock-like materials from three aspects of energy dissipation, microscopic damage, and stress field characteristics. The result indicates that the impact energy structure of the SHPB is greatly affected by the pre-set joint angle of the specimen. With the joint angle increasing, the proportion of reflected energy moves in fluctuation, while the ratio of transmitted energy to dissipated energy varies from one to the other. NMR analysis reveals the structural variation of the pores in those cement specimens before and after the impact. Crack propagation direction is correlated with pre-set joint angles of the specimens. With the increase of the pre-set joint angles, the crack initiation angle decreases gradually. When the joint angles are around 30°–75°, the specimens develop obvious cracks. The crushing process of the specimens is simulated by LS-DYNA software. It is concluded that the stresses at the crack initiation time are concentrated between 20 and 40 MPa. The instantaneous stress curve first increases and then decreases with crack propagation, peaking at different times under various joint angles; but most of them occur when the crack penetration ratio reaches 80–90%. With the increment of joint angles in specimens through the simulation software, the changing trend of peak stress is consistent with the test results.

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Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽

10.3390/s21010231 ◽

2021 ◽

Vol 21 (1) ◽

pp. 231

Author(s):

Weiheng Jiang ◽

Xiaogang Wu ◽

Yimou Wang ◽

Bolin Chen ◽

Wenjiang Feng ◽

...

Keyword(s):

Frequency Analysis ◽

Classification Accuracy ◽

Communication Systems ◽

Mimo Systems ◽

Decision Fusion ◽

Frequency Characteristics ◽

Modulation Classification ◽

Time Frequency Analysis ◽

Time Frequency ◽

Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

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Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Sensors ◽

10.3390/s21030692 ◽

2021 ◽

Vol 21 (3) ◽

pp. 692

Author(s):

Jingcheng Chen ◽

Yining Sun ◽

Shaoming Sun

Keyword(s):

Feature Selection ◽

Activity Recognition ◽

Human Activity ◽

Joint Angle ◽

Recognition Performance ◽

Wearable Sensors ◽

Estimation Algorithm ◽

Recognition System ◽

Human Activity Recognition ◽

Time Frequency

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

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