Mr. Yintao Liu Profile

Yintao Liu

at Guilin Univ of Technology

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Publications (4)

Proceedings Article | 9 December 2015 Paper

Wavelet analysis deformation monitoring data of high-speed railway bridge

ShiHua Tang, Qing Huang, Conglin Zhou, HongWei Xu, YinTao Liu, FeiDa Li

Proceedings Volume 9808, 98083Y (2015) https://doi.org/10.1117/12.2207906

KEYWORDS: Wavelets, Denoising, Signal to noise ratio, Interference (communication), Error analysis, Bridges, MATLAB, Safety, Pollution, Fourier transforms

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Deformation monitoring data of high-speed railway bridges will inevitably be affected because of noise pollution, A deformation monitoring point of high-speed railway bridge was measurd by using sokkia SDL30 electronic level for a long time,which got a large number of deformation monitoring data. Based on the characteristics of the deformation monitoring data of high-speed railway bridge, which contain lots of noise. Based on the MATLAB software platform, 120 groups of deformation monitoring data were applied to analysis of wavelet denoising.sym6,db6 wavelet basis function were selected to analyze and remove the noise.The original signal was broken into three layers wavelet,which contain high frequency coefficients and low frequency coefficients.However, high frequency coefficient have plenty of noise.Adaptive method of soft and hard threshold were used to handle in the high frequency coefficient.Then,high frequency coefficient that was removed much of noise combined with low frequency coefficient to reconstitute and obtain reconstruction wavelet signal.Root Mean Square Error (RMSE) and Signal-To-Noise Ratio (SNR) were regarded as evaluation index of denoising,The smaller the root mean square error and the greater signal-to-noise ratio indicate that them have a good effect in denoising. We can surely draw some conclusions in the experimental analysis:the db6 wavelet basis function has a good effect in wavelet denoising by using a adaptive soft threshold method,which root mean square error is minimum and signal-to-noise ratio is maximum.Moreover,the reconstructed image are more smooth than original signal denoising after wavelet denoising, which removed noise and useful signal are obtained in the original signal.Compared to the other three methods, this method has a good effect in denoising, which not only retain useful signal in the original signal, but aiso reach the goal of removing noise. So, it has a strong practical value in a actual deformation monitoring project.

Proceedings Article | 9 December 2015 Paper

Application of wavelet neural network model based on genetic algorithm in the prediction of high-speed railway settlement

Shihua Tang, Feida Li, Yintao Liu, Lan Lan, Conglin Zhou, Qing Huang

Proceedings Volume 9808, 98082P (2015) https://doi.org/10.1117/12.2222200

KEYWORDS: Neural networks, Wavelets, Genetics, Data modeling, Genetic algorithms, Wave propagation, Evolutionary algorithms, Geomatics, Wavelet transforms, Local area networks

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Proceedings Article | 9 December 2015 Paper

Based on the wavelet neural network analysis and forecast of deformation monitoring data

Conglin Zhou, Shihua Tang, Changzeng Tang, Qing Huang, Yintao Liu, Xinying Zhong, Feida Li, Hongwei Xu

Proceedings Volume 9808, 98081E (2015) https://doi.org/10.1117/12.2207604

KEYWORDS: Neural networks, Wavelets, Data modeling, Filtering (signal processing), Denoising, Data processing, Safety, Evolutionary algorithms, Analytical research, Neurons

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Proceedings Article | 9 December 2015 Paper

Research on the processing technology of low-altitude unmanned aerial vehicle images

Shihua Tang, Yintao Liu, Feida Li, Conglin Zhou, Qing Huang, Hongwei Xu

Proceedings Volume 9808, 98083O (2015) https://doi.org/10.1117/12.2207583

KEYWORDS: Unmanned aerial vehicles, Data processing, Image processing, Cameras, Orthophoto maps, Control systems, Feature extraction, Associative arrays, Photography, Roads

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