2019
DOI: 10.1155/2019/4049765
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A Cooperative Denoising Algorithm with Interactive Dynamic Adjustment Function for Security of Stacker in Industrial Internet of Things

Abstract: In order to more effectively eliminate the disturbance of vibration signal to ensure the security monitoring of stacker be more accurate in Industrial Internet of Things (IIoT), a cooperative denoising algorithm with interactive dynamic adjustment function was constructed and proposed. First, some basic theories such as EMD, EEMD, LMS, and VSLMS were introduced in detail according the characteristics of stacker in IIoT. Meanwhile, the advantages and disadvantages of varieties of algorithms have been analyzed. … Show more

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Cited by 5 publications
(3 citation statements)
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“…After that, thresholding of the obtained wavelet coefficients (subbands) is performed. In [16], the DWT [17,18] was applied to the speech signal to simply conserve the obtained approximation portion, which simultaneously attains data compression and noise robustness in recognition. In [1], the DWT was employed for analyzing the spectrogram of a noisy utterance along the temporal axis, and then the resulting detail portion was devalued with an expect of reducing noise effect in order to promote speech quality.…”
Section: Introductionmentioning
confidence: 99%
“…After that, thresholding of the obtained wavelet coefficients (subbands) is performed. In [16], the DWT [17,18] was applied to the speech signal to simply conserve the obtained approximation portion, which simultaneously attains data compression and noise robustness in recognition. In [1], the DWT was employed for analyzing the spectrogram of a noisy utterance along the temporal axis, and then the resulting detail portion was devalued with an expect of reducing noise effect in order to promote speech quality.…”
Section: Introductionmentioning
confidence: 99%
“…At the beginning of the fault diagnosis, the first step is to reduce the noise of the acceleration signal, and then the extracted feature would correctly represent the characteristics of fault. However, some de-noising methods have limitations on the type of noise, such as least mean squares (LMS) [1]. Also, in the actual environment, the type of noise is complex and variable [2].…”
Section: Introductionmentioning
confidence: 99%
“…To solve this problem, Wu and Huang et al proposed ensemble EMD (EEMD) based on the EMD method [11]. EEMD has been widely used in the field of fault diagnosis in recent years because it maintains the advantage of EMD adaptive decomposition and overcomes the endpoint effect and mode aliasing effect of EMD [12]- [15]. Although EEMD has overcome the shortcomings of EMD to some extent, there is still the problem of IMF component selection.…”
Section: Introductionmentioning
confidence: 99%