Mobile QR Code QR CODE

2024

Acceptance Ratio

21%

REFERENCES

1 
H. Li, H. Wang, Z. Xie, and M. He, ``Fault diagnosis of railway freight car wheelset based on deep belief network and cuckoo search algorithm,'' Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, vol. 236, no. 5, pp. 501-510, July 2022.DOI
2 
X. Zhong, Q. Mei, X. Gao, and T. Haung, ``Fault diagnosis of rolling bearings based on improved direct fast iterative filtering and spectral amplitude modulation,'' Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 236, no. 9, pp. 5111-5123, January 2022.DOI
3 
A. Zhang, C. Shen, Q. He, F. Hu, F. Liu, and F. Kong, ``Doppler distortion removal based on Dopplerlet transform and re-sampling for wayside fault diagnosis of train bearings,'' Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 235, no. 17, pp. 3407-3423, October 2021.DOI
4 
Z. Mo, H. Zhang, J. Wang, H. Fu, and Q. Miao, ``Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm,'' Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 235, no. 19, pp. 4458-4474, November 2021.DOI
5 
J. Luo, J. Huang, J. Ma, and H. Li, ``An evaluation method of conditional deep convolutional generative adversarial networks for mechanical fault diagnosis,'' Journal of Vibration and Control, vol. 28, no. 11/12, pp. 1379-1389, February 2022.DOI
6 
J. Huang, Y. Liu, and Z. Liang, ``Rapid evaluation of the mechanical fault severity in induction motors using the model‐based diagnosis technique,'' IET Electric Power Applications, vol. 15, no. 3, pp. 145-158, January 2021.DOI
7 
Z. Shang, W. Li, M. Gao, X. Liu, and Y. Yu, ``An intelligent fault diagnosis method of multi-scale deep feature fusion based on information entropy,'' Chinese Journal of Mechanical Engineering, vol. 34, no. 1, pp. 1-16, December 2021.DOI
8 
S. Marmouch, T. Aroui, and Y. Koubaa, ``Statistical neural networks for induction machine fault diagnosis and features processing based on principal component analysis,'' IEEJ Transactions on Electrical and Electronic Engineering, vol. 16, no. 2, pp. 307-314, January 2021.DOI
9 
L. A. Briceno-Mena, J. A. Romagnoli, and C. G. Arges, ``PemNet: A transfer learning-based modeling approach of high-temperature polymer electrolyte membrane electrochemical systems,'' Industrial & Engineering Chemistry Research, vol. 61, no. 9, pp. 3350-3357, March 2022.DOI
10 
B. Kaya and M. Nal, ``A CNN transfer learning‐based approach for segmentation and classification of brain stroke from noncontrast CT images,'' International Journal of Imaging Systems and Technology, vol. 33, no. 1, pp. 1335-1352, February 2023.DOI
11 
J. Adams, Y. Qiu, L. Posadas, K. Eskridge, and G. Graef, ``Phenotypic trait extraction of soybean plants using deep convolutional neural networks with transfer learning,'' Big Data & Information Analytics, vol. 6, no. 2, pp. 26-40, March 2021.DOI
12 
I. Debicha, R. Bauwens, T. Debatty, T. Kenaza, and W. Mees, ``TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems,'' Future Generations Computer Systems: FGCS, vol. 138, no. 2, pp. 185-197, October 2023.DOI
13 
W. Zhu, J. Zhang, and J. Romagnoli, ``General feature extraction for process monitoring using transferlearning approaches,'' Industrial & Engineering Chemistry Research, vol. 61, no. 15, pp. 5202-5214, April 2022.DOI
14 
A. N. Chy, U. A. Siddiqua, and M. Aono, ``Exploiting transfer learning and hand-crafted features in a unified neural model for identifying actionable informative tweets,'' Journal of Information Processing, vol. 29, no. 1, pp. 16-29, January 2021.DOI
15 
P. M. Rajasree, A. Jatti, and D. Santosh, ``An improved transfer learning approach towards breast cancer classification on deep residual network,'' Indian Journal of Computer Science and Engineering, vol. 12, no. 4, pp. 1136-1148, August 2021.DOI
16 
M. Hasanvand, M. Nooshyar, E. Moharamkhani, and A. Selyari, ``Machine learning methodology for identifying vehicles using image processing,'' Artificial Intelligence and Applications, vol. 1, no. 3, pp. 170-178, April 2023.DOI
17 
H. Tang and L. Notash, ``Neural network-based transfer learning of manipulator inverse displacement analysis,'' Journal of Mechanisms and Robotics, vol. 13, no. 3, pp. 1-22, June 2021.DOI
18 
R. Rani and H. Singh, ``Fingerprint presentation attack detection using transfer learning approach,'' International Journal of Intelligent Information Technologies (IJIIT), vol. 17, no. 1, pp. 53-67, March 2021.DOI
19 
X. Yuan, E. Pang, K. Lin, and J. Hu, ``Deep protein subcellular localization predictor enhanced with transfer learning of GO annotation,'' IEEJ Transactions on Electrical and Electronic Engineering, vol. 16, no. 4, pp. 559-567, February 2021.DOI
20 
F. G. Waldamichael, T. G. Debelee, and Y. M. Ayano, ``Coffee disease detection using a robust HSV color-based segmentation and transfer learning for use on smartphones,'' International Journal of Intelligent Systems, vol. 37, no. 8, pp. 4967-4993, November 2021.DOI