Open Access

Research on adaptive artificial intelligence algorithm in signal denoising and enhancement

  
Oct 04, 2024

Cite
Download Cover

Wang, H., Liu, Z., Peng, D., & Cheng, Z. (2022). Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA transactions, 128, 470-484. Search in Google Scholar

Ghorat, M., Gharehpetian, G. B., Latifi, H., & Hejazi, M. A. (2018). A new partial discharge signal denoising algorithm based on adaptive dual-tree complex wavelet transform. IEEE Transactions on Instrumentation and Measurement, 67(10), 2262-2272. Search in Google Scholar

Lastre-Dominguez, C., Shmaliy, Y. S., Ibarra-Manzano, O., Munoz-Minjares, J., & Morales-Mendoza, L. J. (2019). ECG signal denoising and features extraction using unbiased FIR smoothing. BioMed research international, 2019(1), 2608547. Search in Google Scholar

Alyasseri, Z. A. A., Khader, A. T., Al-Betar, M. A., Abasi, A. K., & Makhadmeh, S. N. (2019). EEG signals denoising using optimal wavelet transform hybridized with efficient metaheuristic methods. IEEE Access, 8, 10584-10605. Search in Google Scholar

Li, Y., Li, Y., Chen, X., Yu, J., Yang, H., & Wang, L. (2018). A new underwater acoustic signal denoising technique based on CEEMDAN, mutual information, permutation entropy, and wavelet threshold denoising. Entropy, 20(8), 563. Search in Google Scholar

Pauline, S. H., & Dhanalakshmi, S. (2022). A robust low-cost adaptive filtering technique for phonocardiogram signal denoising. Signal Processing, 201, 108688. Search in Google Scholar

Zhao, M., & Jia, X. (2017). A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery. Mechanical Systems and Signal Processing, 94, 129-147. Search in Google Scholar

Wang, J., Li, J., Yan, S., Shi, W., Yang, X., Guo, Y., & Gulliver, T. A. (2020). A novel underwater acoustic signal denoising algorithm for Gaussian/non-Gaussian impulsive noise. IEEE Transactions on Vehicular Technology, 70(1), 429-445. Search in Google Scholar

Naveed, K., Akhtar, M. T., Siddiqui, M. F., & ur Rehman, N. (2021). A statistical approach to signal denoising based on data-driven multiscale representation. Digital Signal Processing, 108, 102896. Search in Google Scholar

Wang, H., Liu, Z., Song, Y., & Lu, X. (2017). Ensemble EMD-based signal denoising using modified interval thresholding. IET Signal Processing, 11(4), 452-461. Search in Google Scholar

Wang, G., Yang, L., Liu, M., Yuan, X., Xiong, P., Lin, F., & Liu, X. (2020). ECG signal denoising based on deep factor analysis. Biomedical Signal Processing and Control, 57, 101824. Search in Google Scholar

Han, H., Wang, H., Liu, Z., & Wang, J. (2022). Intelligent vibration signal denoising method based on non-local fully convolutional neural network for rolling bearings. ISA transactions, 122, 13-23. Search in Google Scholar

Fan, G., Li, J., & Hao, H. (2020). Vibration signal denoising for structural health monitoring by residual convolutional neural networks. Measurement, 157, 107651. Search in Google Scholar

Arsene, C. T., Hankins, R., & Yin, H. (2019, September). Deep learning models for denoising ECG signals. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5). IEEE. Search in Google Scholar

Bayer, F. M., Kozakevicius, A. J., & Cintra, R. J. (2019). An iterative wavelet threshold for signal denoising. Signal Processing, 162, 10-20. Search in Google Scholar

Kumar, A., Tomar, H., Mehla, V. K., Komaragiri, R., & Kumar, M. (2021). Stationary wavelet transform based ECG signal denoising method. ISA transactions, 114, 251-262. Search in Google Scholar

Han, G., Lin, B., & Xu, Z. (2017). Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview. Journal of Instrumentation, 12(03), P03010. Search in Google Scholar

Zhu, W., Mousavi, S. M., & Beroza, G. C. (2019). Seismic Signal Denoising and Decomposition Using Deep Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9476-9488. Search in Google Scholar

Dautov, C. P., & Ozerdem, M. S. (2018, May). Wavelet transform and signal denoising using Wavelet method. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). Ieee. Search in Google Scholar

Singh, O., & Sunkaria, R. K. (2017). ECG signal denoising via empirical wavelet transform. Australasian physical & engineering sciences in medicine, 40, 219-229. Search in Google Scholar

Crockett, B., Romero Cortes, L., Maram, R., & Azaña, J. (2022). Optical signal denoising through temporal passive amplification. Optica, 9(1), 130-138. Search in Google Scholar

Liu, R., Shu, M., & Chen, C. (2021). ECG signal denoising and reconstruction based on basis pursuit. Applied Sciences, 11(4), 1591. Search in Google Scholar

Ma, Y., Liu, X., Zhao, T., Liu, Y., Tang, J., & Shah, N. (2021, October). A unified view on graph neural networks as graph signal denoising. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 1202-1211). Search in Google Scholar

Ranjan, R., Sahana, B. C., & Bhandari, A. K. (2022). Motion artifacts suppression from EEG signals using an adaptive signal denoising method. IEEE Transactions on Instrumentation and Measurement, 71, 1-10. Search in Google Scholar

Li, F., Zhang, B., Verma, S., & Marfurt, K. J. (2018). Seismic signal denoising using thresholded variational mode decomposition. Exploration Geophysics, 49(4), 450-461. Search in Google Scholar

Yu, S., Ma, J., & Wang, W. (2019). Deep learning for denoising. Geophysics, 84(6), V333-V350. Search in Google Scholar

Jin, T., Li, Q., & Mohamed, M. A. (2019). A novel adaptive EEMD method for switchgear partial discharge signal denoising. IEEE Access, 7, 58139-58147. Search in Google Scholar

Li Yuan,Wang Zhuojian,Li Zhe & Li Hao. (2021). Research on Gear Signal Fault Diagnosis Based on Wavelet Transform Denoising. Journal of Physics: Conference Series(1). Search in Google Scholar

Qingming Kong,Guowen Cui,Sang-Soo Yeo,Zhongbin Su,Jingjing Wang,Fengzhu Hu... & Varshinee Anu Padigala. (2017). DBN wavelet transform denoising method in soybean straw composition based on near-infrared rapid detection.Journal of Real-Time Image Processing(3),613-626. Search in Google Scholar

U. D. Dwivedi & S. N. Singh. (2009). A Wavelet-based Denoising Technique for Improved Monitoring and Characterization of Power Quality Disturbances.Electric Power Components and Systems(7),753-769. Search in Google Scholar

Hyun-Dong Lee & Kwang-Sik Lee. (2001). Characteristics of Partial Discharges Signals Utilizing Method of Wavelet Transform Denoising Process. Journal of the Korean Institute of Illuminating and Electrical Installation Engineers(4),62-68. Search in Google Scholar

Language:
English