Research on signals extrapolation in the time domain based on wavelet analysis

In this paper, a novel two-stream convolutional neural network based on gradient image is performed to effectively classify and identify aluminum profiles defects for the first time. Recent feature fusion methods based on two-stream network prove promising performance for defects classification and recognition. In this paper, we use data enhancement methods to obtain a large number of samples to prevent the over fitting phenomenon in deep learning. The image gradient is calculated with the Sobel operator, and normalized to transform the data between zero and one under the same dimension. We design a two-stream convolutional neural network model adopting Wavelet transform fusion strategy to realize feature fusion on the ReLU6 layer, which uses the original RGB image of aluminum profile and the gradient image corresponding to the original RGB image as inputs to extract features through two sub-networks and fuses features on a concatenate layer to be input into SVM classifier for classification and recognition. Using Bayesian Optimization function and computing the cross-validation classification error to optimize the hyperparameters to choose the best performance configuration is performed. A series of experimental data, which include accuracy and estimated generalized classification errors of single-stream and two-stream networks with different feature fusion strategies on different fusion layers, are conducted and show that the current model has good convergence, accuracy, stability and generalization. On this basis, this paper also proposes a series of innovative methods for the future research of other defects. INDEX TERMS Aluminum profile surface defects, two-stream network, gradient image, convolutional neural network, SVM.

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“…The double scale classification formula of twodimensional image wavelet decomposition is shown in formula 8 [36].…”

Section: Feature Fusionmentioning

confidence: 99%

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

Duan

Zhang

2020

IEEE Access

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“…Wavelet transform can perform time-frequency analysis on signals because it has the ability of multi-resolution analysis [14,15]. According to the idea of multi-frequency analysis, the wavelet transform expansion formula:…”

Section: Mallat Algorithmmentioning

confidence: 99%

An Iterative Hybrid Harmonics Detection Method Based on Discrete Wavelet Transform and Bartlett–Hann Window

Wang

Zhao

2020

Applied Sciences

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The current signal harmonic detection method(s) cannot reduce the errors in the analysis and extraction of mixed harmonics in the power grid. This paper designs a harmonic detection method based on discrete Fourier transform (DFT) and discrete wavelet transform (DWT) using Bartlett–Hann window function. It improves the detection accuracy of the existing methods in the low frequency steady-state part. In addition, it also separates the steady harmonics from the attenuation harmonics of the high frequency part. Simulation results show that the proposed harmonic detection method improves the detection accuracy of the steady-state part by 1.5175% compared to the existing method. The average value of low frequency steady-state amplitude detection of the proposed method is about 95.3375%. At the same time, the individual harmonic components of the signal are accurately detected and recovered in the high frequency part, and separation of the steady-state harmonics and the attenuated harmonics is achieved. This method is beneficial to improve the ability of harmonic analysis in the power grid.

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“…[5] At present, the research on CO 2 mainly focuses on carbon capture and storage (CCS) [6,9] and carbon capture and utilization (CCU). [10,14] CCS technology consists of three parts-CO 2 collection/capture, transportation, and storage-focusing on storage after capture. The transportation of CO 2 after capture mainly includes transportation by ship, tank car, and pipeline.…”

Section: Introductionmentioning

confidence: 99%

Industrial Application of Bottom–Blown CO₂ in Basic Oxygen Furnace Steelmaking Process

Feng

Zhu

Liu

et al. 2021

steel research int.

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A significant amount of CO 2 is emitted and utilized in the production processes engaged by iron and steel enterprises. Herein, the feasibility and principles of CO 2 participation in the steelmaking process are analyzed, and an industrial examination of bottom-blown CO 2 smelting in a 120 t converter is performed; this serves to summarize and verify the metallurgical advantages of CO 2 participation in the converter steelmaking process. The injection of CO 2 improves the effects of dephosphorization and denitrification, reduces the end-point oxygen content of molten steel, diminishes the consumption of the slagging agent, and contributes to a reduction in production costs and improvement in product quality. This study analyzes two different recycling paths of CO 2 in iron and steel enterprises, providing a new mechanism for CO 2 emission reduction.

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Research on signals extrapolation in the time domain based on wavelet analysis

Cited by 4 publications

References 1 publication

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

An Iterative Hybrid Harmonics Detection Method Based on Discrete Wavelet Transform and Bartlett–Hann Window

Industrial Application of Bottom–Blown CO₂ in Basic Oxygen Furnace Steelmaking Process

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Research on signals extrapolation in the time domain based on wavelet analysis

Cited by 4 publications

References 1 publication

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

An Iterative Hybrid Harmonics Detection Method Based on Discrete Wavelet Transform and Bartlett–Hann Window

Industrial Application of Bottom–Blown CO2 in Basic Oxygen Furnace Steelmaking Process

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Industrial Application of Bottom–Blown CO₂ in Basic Oxygen Furnace Steelmaking Process