Study on Blasting Vibration Control of Brick-Concrete Structure under Subway Tunnel

Wu, Yangyong; Mu, Chaomin; Qi, Zong; Wu, Jiehao; Zhou, Hui

doi:10.3390/app122110960

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…In the wavelet transform, the functions are crucial and should be selected in a comprehensive manner in terms of support length, symmetry, regularity and similarity. The functions, such as db, sym, coif and fk [11,[17][18][19][20][21][22][23], are the most commonly used. db is short for Daubechies.…”

Section: Wavelet Transformmentioning

confidence: 99%

Research on Image Denoising in Edge Detection Based on Wavelet Transform

2023

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Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The PSNR and MSE of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the PSNR and MSE of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results.

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Section: Wavelet Transformmentioning

confidence: 99%

Research on Image Denoising in Edge Detection Based on Wavelet Transform

2023

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“…In the wavelet transform, the use of wavelet basis functions is very critical and needed to comprehensive considerate the support length, symmetry, regularity and similarity and so on. The most popular ones are db, sym and coif [18][19][20][21][22]. The choice of the number of decomposition layers is also crucial.…”

Section: Figure 1 Transform Denoising General Processmentioning

confidence: 99%

Research on Image Denoising in edge detection Based on Wavelet Transform

Ning¹,

Han²,

Zhu³

et al. 2022

Preprint

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In the process of image feature extraction, noise in the image will greatly affect the accuracy of edge detection. In this paper, the image is filtered to remove noise before edge detection by using the algorithm of wavelet transformation. Different wavelet functions are used to decompose image. Based on the experimental results, the best denoising wavelet function is selected. Canny algorithm is used to detect the edge of the denoised image, and the result of edge detection is evaluated according to the Pratt quality factor. It is proved that wavelet transform can improve the edge detection results.

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“…However, there are difficulties in controlling the stability of surrounding rocks in the blasting mining process. In addition, simultaneous blasting of ore and waste rock increases the workload of washing [3][4][5]. In recent years, the continuous mining of metal mines, which draws on the successful experience of mechanized mining in non-metallic mines, such as coal mines [6][7][8], has attracted the attention of scholars.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Surrounding Rock State on Working Performance of Cutting Head in Metal Mining

Xu,

Wan,

Gao

et al. 2023

Applied Sciences

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Continuous mining is one of the development goals for metal mines, and the application of coal mining equipment represented by shearers provides a reference for continuous mining. However, rock in metal mines is generally harder than coal, making cutting difficult. Improving the surrounding rock conditions is an important way to improve the applicability of the drum for hard rock cutting. Therefore, this article explores the correlation between drum-cutting performance and surrounding rock boundary conditions, aiming to obtain surrounding rock boundary conditions that can help improve drum-cutting performance. To achieve the goal, a model of a shearer drum and hard rock is established using finite element software. With the model, hard rock cutting was simulated and the stress distribution on rock mass, deformation of rock mass, and drum cutting force during the cutting process under different confining pressures were analyzed. Relations between drum cutting force and confining pressure on rock mass were obtained. Then, drum cutting force under different free surfaces of rock mass are studied and the positions of free surface on rock mass that help to reduce the drum cutting force were summarized. According to the research, when the rock mass is under uniaxial compression, drum cutting force increases with the confining pressure on the rock mass; In addition, the free surfaces on the rock mass are proved to be helpful to reduce the drum cutting force. The research content lays the foundation for the boundary conditions required to reduce drum-cutting force in metal mining.

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Study on Blasting Vibration Control of Brick-Concrete Structure under Subway Tunnel

Cited by 5 publications

References 19 publications

Research on Image Denoising in Edge Detection Based on Wavelet Transform

Research on Image Denoising in Edge Detection Based on Wavelet Transform

Research on Image Denoising in edge detection Based on Wavelet Transform

Analysis of the Influence of Surrounding Rock State on Working Performance of Cutting Head in Metal Mining

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