A novel denoising method for vibration signal of hob spindle based on EEMD and grey theory

Jia, Yiwei; Li, Guolong; Dong, Xiaomin; He, Kun

doi:10.1016/j.measurement.2020.108490

Cited by 89 publications

(37 citation statements)

References 35 publications

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“…The signal‐to‐noise ratio (SNR) of output signals is calculated to evaluate the denoising effect. In general, the higher SNR, the more obvious the noise suppression and the better the denoising effect for different denoising algorithms (Jia et al., 2021). The output signal

S N R_{o u t}

calculation formula is shown in Equation (7).…”

Section: Methodsmentioning

confidence: 99%

Research on nondestructive identification of grape varieties based on EEMD‐DWT and hyperspectral image

Sun

Zhou

et al. 2021

Journal of Food Science

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Grape varieties are directly related to the quality and sales price of table grapes and consumed products (raisin, wine, grape juice, etc.). To satisfy the identification requirements of rapid, accurate, and nondestructive detection, an improved denoising algorithm based on ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT) is proposed to couple with the hyperspectral image (HSI) of grape varieties in this study. First, the hyperspectral data of grape varieties are collected by using HSI instrument, and denoised by the proposed EEMD-DWT and other denoising algorithms. CARS-SPA (competitive adaptive reweighed sampling coupled with successive projections algorithm) is introduced to select the effective wavelengths and a discriminative model is constructed by using support vector machine (SVM). Finally, Monte Carlo experiments verified that EEMD-DWT was an effective and powerful spectra denoising method, and the SVM model constructed by combining with CARS-SPA had an excellent identification accuracy (99.3125%). The results suggested that the key wavelengths selected by using CARS-SPA and EEMD-DWT could be an alternative to the deal with HSI, and its potential to become a method for identifying grape varieties. Practical Application: Traditional grape varieties identification methods are destructive and time consuming. Therefore, HSI technology is applied to realize fast and nondestructive identification of grape varieties in this study. The research results indicate that it is feasible to combine HSI technology with machine learning algorithm to discriminate grape varieties. It is of great significance for grape grading and the promotion of excellent varieties, and also provides reference for grape industry producers to identify grape varieties quickly and accurately.

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S N R_{o u t}

calculation formula is shown in Equation (7).…”

Section: Methodsmentioning

confidence: 99%

Research on nondestructive identification of grape varieties based on EEMD‐DWT and hyperspectral image

Sun

Zhou

et al. 2021

Journal of Food Science

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“…We therefore employ the Ensemble Empirical Mode Decomposition (EEMD) [ 56 , 57 ] as a self-adaptive filtering for echo intensity denoising, which could adaptively decompose the nonlinear and nonstationary waveform into the sum of components, the Intrinsic Mode Functions (IMFs) and one residual component, and then distinguish and remove IMF with noise as its main component, eliminating the mode mixing problem by adding finite white noise to the investigated signal. The brief process of EEMD denoising [ 58 , 59 ] is expressed as follows: Add a white noise with the given amplitude to the original signal in echo intensity; Perform EMD [ 60 ] to the signal in echo intensity with the added white noise to obtain

Intrinsic Mode Function (IMF) components and one residual component, whereas the definition and acquisition process of IMF are relegated to Appendix A ; Repeat with the given number of trials.…”

Section: Sand Wave Detection and Morphological Geometrical Topological Characterization With Echo Intensitymentioning

confidence: 99%

“…We therefore employ the Ensemble Empirical Mode Decomposition (EEMD) [56,57] as a self-adaptive filtering for echo intensity denoising, which could adaptively decompose the nonlinear and nonstationary waveform into the sum of components, the Intrinsic Mode Functions (IMFs) and one residual component, and then distinguish and remove IMF with noise as its main component, eliminating the mode mixing problem by adding finite white noise to the investigated signal. The brief process of EEMD denoising [58,59] is expressed as follows:…”

Section: Eemd Denoisingmentioning

confidence: 99%

Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

Nian

Zang

Geng

et al. 2021

Sensors

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Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.

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“…By using the gray fuzzy assessment method to the risk assessment of ancient buildings, the cognitive differences attributed to evaluators could be avoided to a certain extent, and the scientific nature of the assessment results could be improved. e process of the gray theory [33,34] calculation (sample assessment matrix construction ⟶ determination of the whitening weight function ⟶ construction of gray assessment matrix), the whitening weight function, and gray assessment matrix were acquired by the following equations [35].…”

Section: Comprehensive Safety Assessmentmentioning

confidence: 99%

Safety Assessment of Ancient Buildings under Adjacent Subway Blasting Construction Based on the Optimized Fuzzy Optimal Method

Liu

Huang

et al. 2021

Shock and Vibration

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Fragile ancient buildings are recognized as an eloquent testimony to human civilization, and their safety should arouse more attention. According to the special case of adjacent blasting construction, the assessment model should be essentially built to assess the effect of tunnel blasting on the safety of the ancient buildings. To analyze the structural safety of ancient buildings under blasting vibration and to protect the precious ancient buildings, a risk assessment model of ancient buildings with 20 relevant assessment indexes was initiatively built in this study. To be specific, the relative factors of blasting, the factors of ancient buildings, and other factors (e.g., religion) were comprehensively considered in the model. Subsequently, the risk level and weight were calculated more systematically and quantitatively by adopting the optimized optimal comprehensive method integrating the G1 method and the entropy method. Lastly, the overall risk value was determined by applying the fuzzy gray method. Afterward, the value was adopted to assess the safety of the Asoka Temple, the only existing temple named after the Indian King Asoka in China, as an attempt to verify the feasibility of this model. Besides, the Asoka tunnel was around it. As demonstrated from the results, the age of the buildings maximally impacted the safety of ancient buildings, and the safety level of the Asoka Temple was “relatively safe.” The present study built an effective model to assess the safety of ancient buildings under adjacent subway blasting construction, which could help improve the efficiency and accuracy of assessments.

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A novel denoising method for vibration signal of hob spindle based on EEMD and grey theory

Cited by 89 publications

References 35 publications

Research on nondestructive identification of grape varieties based on EEMD‐DWT and hyperspectral image

Research on nondestructive identification of grape varieties based on EEMD‐DWT and hyperspectral image

Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

Safety Assessment of Ancient Buildings under Adjacent Subway Blasting Construction Based on the Optimized Fuzzy Optimal Method

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