Prediction of surface roughness of various machining processes by a hybrid algorithm including time series analysis, wavelet transform and multi view embedding

Nouhi, Sepehr; Pour, Masoud

doi:10.1016/j.measurement.2021.109904

Cited by 28 publications

(9 citation statements)

References 28 publications

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“…(3) From a quantitative point of view, the index values of different evaluation methods are different, among which the corresponding index of D tw is the largest, the corresponding index of M se is relatively low, and the corresponding index of T di is the smallest in the whole evaluation index. However, there are also some problems in the development process [32][33][34]:…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

An Intelligent Prediction for Sports Industry Scale Based on Time Series Algorithm and Deep Learning

Liang

2022

Computational Intelligence and Neuroscience

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Aiming at some existing issues in the sports industry, the existing model is optimized by deep learning and time series theory based on the relevant algorithm, and the scale of the sports industry is analyzed and predicted by the model. The results show the following: (1) Based on the single-step prediction of time series, MNTS structural algorithm can be used to describe and study the sports industry scale with the single factor, and the correlation fitting degree is high. (2) Curves of different evaluation methods can include parts linear stage and nonlinear stage according to the magnitude of change. (3) Seen from the optimization model in this paper, the proposed method can describe both global and local trends of data. (4) It can be seen from the prediction curve that the overall state of fluctuation indicates that time will have a great impact on the relevant scale of the sports industry. Compared with single-step prediction, the accuracy of multistep prediction is higher, and the multistep prediction model based on time series can well characterize and predict the scale of the sports industry. By using the relevant time algorithm, the sports industry scale can be predicted and analyzed so as to provide theoretical support for the formulation and implementation of relevant policies.

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Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

An Intelligent Prediction for Sports Industry Scale Based on Time Series Algorithm and Deep Learning

Liang

2022

Computational Intelligence and Neuroscience

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“…Numerous studies have been conducted on WA, based on data decomposition and time-series prediction. 15,[21][22][23][24][25][26] In 1998, Huang et al proposed empirical mode decomposition (EMD). 27 The purpose of EMD is to decompose the original signal into several intrinsic mode functions (IMFs), and to determine the intrinsic properties of the effective signal in the data based on experience, that is, to smooth the time series.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, these characteristics can be used to analyze the signal. Numerous studies have been conducted on WA, based on data decomposition and time‐series prediction 15,21–26 . In 1998, Huang et al proposed empirical mode decomposition (EMD) 27 .…”

Section: Introductionmentioning

confidence: 99%

SAVE‐AS: Accelerating convergence in network traffic prediction through adaptive optimized variational mode decomposition and an integrated extreme learning machine

Shi

Leau

2022

Int J Communication

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Nonlinearity and nonstationary data affect the prediction accuracy of network traffic. A practical solution is to use an integrated modeling method based on time-series decomposition and an extreme learning machine (ELM). The original network traffic data series are decomposed in this paper using variational mode decomposition (VMD), and then each subdata series is subjected to phase space reconstruction (PSR). Finally, ELM trains a model for predicting network traffic. We used scalable artificial bee colony (SABC) algorithms to optimize parameter selection throughout the modeling process. However, several processes, such as VMD decomposition, PSR, and ELM training, will be repeated during iterative model optimization, resulting in a significant increase in computational complexity. This article proposes an adaptive selection mechanism for decomposing time series, which named is SABC-VMD-ELM prediction method with an adaptive selection (SAVE-AS) method. From gray correlation degree and approximate entropy, an adaptive selection operator is constructed that can adaptively select the number of data sets after VMD optimization decomposition. The ELM model is trained using several subdata sequences that satisfy the conditions rather than all subdata sequences to minimize computational complexity in modeling. Two publicly available data sets of Mackey-Glass and Lorenz chaotic time series were used; in addition, actual network traffic data from the MAWI Working Group's WIDE Backbone was simulated. It is demonstrated that the prediction model can reduce computational complexity and accelerate convergence speed while maintaining the model's predictive accuracy. In three data sets, training time was reduced by 25.25%, 23.87%, and 41.36%, respectively.

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“…Wang [15] used a numerical simulation to predict the surface morphology of blade machining, through grinding experiments, the simulated value differed by 4% from the experimental value. Nouhi and Pour [16] combined the discrete wavelet transform of the acquired images with time series analysis to predict future grinding roughness surfaces. Nguyen [17] used adaptive neural fuzzy inference system -Gaussian process regression and Taguchi analysis predict wheel wear and surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Non-contact inspection method for surface roughness on small samples

Yan

Zhao

et al. 2023

Surf. Topogr.: Metrol. Prop.

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This study aims to improve the non-contact measurement accuracy of roughness of small samples. Therefore, machine vision and virtual sample generation technology are used to detect the roughness of small sample bearing steel (GCr15) in this study. The surface roughness of different specimens is tested with a contact roughness detector. Image acquisition is carried out on the specimen, histogram equalization image enhancement preprocessing is carried out on the image, and sample capacity expansion is carried out on the basis of data enhancement. On this basis, the gray-level co-occurrence matrix (GLCM) is used to extract the image texture features, and a linear regression model of roughness and Energy, Entropy, Homogeneity, Contrast and Correlation is established. Artificial neural networks (ANNs) is used to classify and predict their surface roughness, train the original samples and virtual samples respectively, and compare their prediction accuracy. The results show that the precision of the virtual samples based on resampling and singular value decomposition(SVD) are 10.13% and 21.21% higher than that of the original samples, respectively, and the average error between the predicted value and the measured value of visual roughness is 3.4%. Therefore, the machine vision and virtual sample generation technology are combined to achieve acceptable surface roughness detection of small samples, which provides a theoretical basis for online detection in gear processing.

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Prediction of surface roughness of various machining processes by a hybrid algorithm including time series analysis, wavelet transform and multi view embedding

Cited by 28 publications

References 28 publications

An Intelligent Prediction for Sports Industry Scale Based on Time Series Algorithm and Deep Learning

An Intelligent Prediction for Sports Industry Scale Based on Time Series Algorithm and Deep Learning

SAVE‐AS: Accelerating convergence in network traffic prediction through adaptive optimized variational mode decomposition and an integrated extreme learning machine

Non-contact inspection method for surface roughness on small samples

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