The Optimum Wavelet Base of Wavelet Analysis in Coal Rock Microseismic Signals

Tang, Shoufeng; Tong, Minman; He, Xiang

doi:10.1155/2014/537415

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…In our study, we set the window size to 21, which is suitable under certain conditions but may not be optimal for other scenarios. On the other hand, the Wavelet Decomposition (WD) method may struggle to select an appropriate Published by Francis Academic Press, UK -20-wavelet basis function that is applicable in all conditions [22] . The performance of the WD and MF methods may improve with a well-selected basis function but may still be limited in their applicability to changing conditions, particularly in complex vehicle-mounted DIS systems.…”

Section: Performance Of the Vmd-sfa Methods In Dismentioning

confidence: 99%

Digital image stabilization method based on variational mode decomposition and sampling fluctuation analysis

2023

AJCIS

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Unintentional motions often cause cameras to produce shaky images, which is a significant source of inter-frame blur and video quality decline. To ad-dress this issue, we present a digital image stabilization approach based on variational mode decomposition (VMD) and sampling fluctuation analysis (SFA) to generate stable video sequences. Our method first estimates the global motion vector (GMV) from a video sequence using the speeded up robust features (SURF) algorithm. We then decompose the GMV into various modes using VMD to separate jitter motions from intentional ones. Here, SFA is applied to distinguish different modes based on their unique structural characteristics. We evaluate our proposed method in complex scenarios by comparing it with several existing methods. Our experimental results demonstrate that VMD outperforms other stabilization techniques under comparable conditions.

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Section: Performance Of the Vmd-sfa Methods In Dismentioning

confidence: 99%

Digital image stabilization method based on variational mode decomposition and sampling fluctuation analysis

2023

AJCIS

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“…Lu et al [5] used a Fourier transform to analyze the power spectrum and amplitude-frequency characteristics of different types of microseismic signal, which provided a basis for the preliminary identification of different types of microseismic signals in mines. Wavelet analysis and wavelet packet analysis fuse the time-frequency domain information of microseismic signals, to enhance the discrimination of microseismic signals [6]. Empirical mode decomposition (EMD) decomposes the original signal into different frequency bands, to better handle random non-smooth signals [7].…”

Section: Introductionmentioning

confidence: 99%

CNN-Transformer for Microseismic Signal Classification

et al. 2023

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The microseismic signals of coal and rock fractures collected by underground sensors contain masses of blasting vibration signals generated by coal mine blasting, and the waveforms of the two signals are highly similar. In order to identify the true microseismic signals with a microseismic monitoring system quickly and accurately, this paper proposes a lightweight network model that combines a convolutional neural network (CNN) and transformer, named CCViT. Of these, the CNN is used to extract shallow features locally, and the transformer is used to extract deep features globally. Moreover, a modified channel attention module provides important channel information for the model and suppresses useless information. The experimental results on the dataset used in this paper show that the proposed CCViT model has significant advantages for floating point operations (FLOPs), parameter quantity, and accuracy compared to many advanced network models.

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“…Therefore, each index must be comprehensively considered in the course of the experiment, to assess its suitability for the signal to be processed. The main characteristics [13] of several commonly used wavelet bases are shown in Table 4. In this study, when we processed infrasonic signals, we sought to ensure the local characteristics of the signal and to reduce distortion.…”

mentioning

confidence: 99%

“…We found the sym and db wavelet bases to be most in line with our requirements. Both are compactly supported and symmetrical; however, the sym wavelet offers better symmetry and stronger localization ability in the time and frequency domain, and can provide more practical and more specific digital filters of finite length in the process of wavelet decomposition of signals [13]. For these reasons, and after a thorough review of the characteristics of infrasound signals and the principles of wavelet denoising, we chose to use the sym wavelet, which is similar to the infrasound signal waveform, for threshold denoising purposes.…”

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confidence: 99%

Debris Flow Infrasound Recognition Method Based on Improved LeNet-5 Network

Leng

Feng

et al. 2022

Sustainability

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To distinguish debris flow infrasound from other infrasound sources, previous works have used one-dimensional infrasound shapes and parameters. In this study, we converted infrasound signals into two-dimensional signal time–frequency graphs and created a time–frequency graph dataset containing five common kinds of infrasound. We used deep learning to distinguish debris flow infrasound from other infrasound and improved the deep learning model to enhance the accuracy of debris flow infrasound identification. By improving the LeNet-5 network, we obtained an infrasound signal recognition method for debris flows based on deep learning. After signal preprocessing and model training, this method was able to differentiate target infrasound from environmental infrasound, and a debris flow infrasound recognition accuracy of 84.1% was achieved. The method described in this paper can effectively recognize debris flow infrasound and distinguish it from other environmental infrasound. By such means, more accurate and more timely debris flow disaster warnings may be obtained.

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The Optimum Wavelet Base of Wavelet Analysis in Coal Rock Microseismic Signals

Cited by 4 publications

References 16 publications

Digital image stabilization method based on variational mode decomposition and sampling fluctuation analysis

Digital image stabilization method based on variational mode decomposition and sampling fluctuation analysis

CNN-Transformer for Microseismic Signal Classification

Debris Flow Infrasound Recognition Method Based on Improved LeNet-5 Network

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