Hybrid Deep Learning Framework for Reduction of Mixed Noise via Low Rank Noise Estimation

Kim, Dai-Gyoung; Ali, Y.; Farooq, Muhammad; Mushtaq, Asif; Rehman, Muhammad Ahmad Abdul; Shamsi, Zahid Hussain

doi:10.1109/access.2022.3170490

Cited by 6 publications

(1 citation statement)

References 56 publications

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“…The WSST approach acts as a filter for better localization of the signal components in both time and frequency domains of multi-sensor data. The deep learning models can be applied directly to the multi-sensor time-series to remove noise [27] [28].…”

Section: B Wavelet Synchro-squeezed Transformmentioning

confidence: 99%

Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

Yakkati,

Yeduri,

Tripathy

et al. 2023

IEEE Access

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In the industry, machinery failure causes catastrophic accidents and destructive damage to the machines. It causes the machinery to stop and reduces production, causing financial losses to the industry. As a result, identifying machine faults at an early stage is critical. With the rapid advancement in artificial intelligence-based methods, developing automated systems that can diagnose machinery faults is necessary and challenging. This paper proposes a multi-channel time-frequency domain deep convolutional neural network (CNN)-based approach for machinery fault diagnosis using multivariate time-series data from multisensors (tachometer, microphone, underhang bearing accelerometer, and overhand bearing accelerometer). The wavelet synchro-squeezed transform (WSST) based technique is used to evaluate the time-frequency images from the multivariate time-series data. The time-frequency images are fed into the multi-channel deep CNN model for automated fault detection. The proposed multi-channel deep CNN model is multi-headed, considering the time-frequency domain information of each channel time-series data for automated fault detection. The proposed model's performance is compared to benchmark models regarding testing accuracy, total parameters, and model size. Experiments have shown that the proposed model outperforms benchmark models regarding classification accuracy. The proposed multi-channel CNN model has obtained the accuracy and F1-score values of 99.48% and 99% for fault classification using timefrequency images of multi-sensor data. Finally, the proposed model's performance is measured regarding inference time when deployed on edge computing devices such as the Raspberry Pi and the Nvidia Jetson AGX Xavier. INDEX TERMS Deep CNN, Machinery failure, Nvidia Jetson AGX Xavier, Raspberry Pi, time-frequency analysis, and wavelet synchro-squeezed transform.

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Section: B Wavelet Synchro-squeezed Transformmentioning

confidence: 99%

Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

Yakkati,

Yeduri,

Tripathy

et al. 2023

IEEE Access

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Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Kusnik

Smołka

2022

Sci Rep

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Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing the Gaussian noise, so that it is able to cope with the impulsive disturbances. Furthermore, the elaborated technique can be applied to enhance the images corrupted by a mixture of strong Gaussian and impulsive noise, severely decreasing the quality of color digital images. By means of our approach, which is based on a novel similarity measure between a pixel and a patch located in the center of the processing block, even heavily disturbed images can be effectively restored, which enables the success of further stages of the image processing pipeline. We evaluate the efficiency of the proposed method using a publicly available database of test color images and compare the restored images applying a set of standard quality metrics with the results delivered by state-of-the-art denoising methods. Additionally, we compare our method with the Medoid and Quick Shift techniques, accelerating the original Mean Shift algorithm, in terms of objective quality criteria and computational complexity. The results of the performed experiments indicate that the proposed technique is superior to the widely used denoising techniques and can be used as a robust extension of the Mean Shift procedure. In the paper, a particular emphasis is placed on the ability of the presented algorithm to preserve and enhance image edges. The performed experiments evaluated with the use of the Pratt’s index, quantitatively confirm the superiority of the proposed design over the Mean Shift and standard denoising methods. The preservation of edges and even their sharpening is a very important feature of our algorithm whereas the final goal is segmentation, playing a crucial role in various computer vision tasks. The proposed algorithm is intended for the mixed noise reduction in color images, but it can be also applied in multispectral imaging and clustering of multidimensional data. To enable the comparison of our method with the standard denoising techniques and to help applying it in other image processing fields, we made its code freely available.

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Echo State Network based Noise Detection in Energy Internet Orienting Justice Blockchain Data

Hongming¹,

Guo²,

Ming

et al. 2022

2022 IEEE International Conference on Energy Internet (ICEI)

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Hybrid Deep Learning Framework for Reduction of Mixed Noise via Low Rank Noise Estimation

Cited by 6 publications

References 56 publications

Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Echo State Network based Noise Detection in Energy Internet Orienting Justice Blockchain Data

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