Shanjing Zhang


  1. [1] S. Chikkam and S. Singh, Condition monitoring and faultdiagnosis of induction motor using DWT and ANN, ArabianJournal for Science and Engineering, 48(5), 2023, 6237–6252.
  2. [2] C. Liu, P. Zheng, and X. Xu, Digitalisation and servitisation ofmachine tools in the era of industry 4.0: a review, InternationalJournal of Production Research, 61(12), 2023, 4069–4101.
  3. [3] W. Zhang and X. Li, Federated transfer learning for intelligentfault diagnostics using deep adversarial networks with dataprivacy, IEEE/ASME Transactions on Mechatronics, 27(1),2021, 430–439.
  4. [4] H. Wang, J. Xu, and R. Yan, Intelligent fault diagnosisfor planetary gearbox using transferable deep q networkunder variable conditions with small training data, Journal ofDynamics, Monitoring and Diagnostics, 2(1), 2023, 30–41.
  5. [5] T. Xie, X. Huang, and S. K. Choi, Intelligent mechanicalfault diagnosis using multisensor fusion and convolution neuralnetwork, IEEE Transactions on Industrial Informatics, 18(5),2021, 3213–3223.
  6. [6] H. Shao, M. Xia, J. Wan, and C. W. Silva, Modified stackedautoencoder using adaptive morlet wavelet for intelligent faultdiagnosis of rotating machinery, IEEE/ASME Transactions onMechatronics, 27(1), 2021, 24–33.
  7. [7] K. Huang, S. Wu, and F.Li, Fault diagnosis of hydraulic systemsbased on deep learning model with multirate data samples,IEEE Transactions on Neural Networks and Learning Systems,33(11), 6789–6801.
  8. [8] X. Zhang, K. P. Rane, I. Kakaravada, and M. Shabaz,Research on vibration monitoring and fault diagnosis of rotatingmachinery based on internet of things technology, NonlinearEngineering, 10(1), 2021, 245–254.
  9. [9] H. Chen, Z. Chai, O. Dogru, B. Jiang, and B. Huang, Data-driven designs of fault detection systems via neural network-aided learning, IEEE Transactions on Neural Networks andLearning Systems, 33(10), 2021, 5694–5705.
  10. [10] Y. Zou, Y. Zhang, and H. Mao, Fault diagnosis on the bearingof traction motor in high-speed trains based on deep learning,Alexandria Engineering Journal, 60(1), 2021, 1209–1219.
  11. [11] J. M. Kudari, A. Jebakumari, S. Kumar, S. Adlin Jebakumari,and B. S. Sushma, Image classifier using the adam optimizerand the relu activation function, International Journal ofAdvanced Research in Engineering and Technology, 12(3), 2021,56–60.
  12. [12] J. Kwon, Z. Li, and S. Zhao, O. M.AI-Qershi, Predictingcrowdfunding success with visuals and speech in video adsand text ads, European Journal of Marketing, 56(6), 2022,1610–1649.
  13. [13] J. Du and Y. Xu, Performance optimization analysis forship central cooling system based on variable frequencycontrol, Mechatronic Systems and Control, 50(10), 2022, 1–6.DOI:10.2316/J.2022.201-0283.
  14. [14] S. Tiwari and A. Jain, A lightweight capsule networkarchitecture for detection of COVID-19 from lung CT scans,International Journal of Imaging Systems and Technology,32(2), 2022, 419–434.
  15. [15] G. Buttazzo, G. Carlier, and M.Laborde, On the wassersteindistance between mutually singular measures, Advances inCalculus of Variations, 13(2), 2020, 141–154.
  16. [16] S. Zhao, X. Yue, S. Zhang, H. Zhao, and B. Wu, A reviewof single-source deep unsupervised visual domain adaptation,IEEE Transactions on Neural Networks and Learning Systems,33(2), 2022, 473–493.
  17. [17] R. Saouli, K. Djemal, A. Abdelli, and I. Youkana, Multipleinstance learning for classifying histopathological images ofthe breast cancer using residual neural network, InternationalJournal of Imaging Systems and Technology, 32(3), 2022,1015–1029.
  18. [18] Z. Li, Z. Dong, W. Chen, and Z. Ding, On the game-theoreticanalysis of distributed generative adversarial networks,International Journal of Intelligent Systems, 37(1), 2022,516–534.
  19. [19] Y. Fang, B. Luo, T. Zhao, D. He, B. B. Jiang, and Q.Liu, ST-SIGMA: Spatio-temporal semantics and interactiongraph aggregation for multi-agent perception and trajectory11forecasting, CAAI Transactions on Intelligence Technology,7(4), 2022, 744–757.
  20. [20] B. Ada, Bayesian reliability analysis based on the weibullmodel under weighted general entropy loss function, AlexandriaEngineering Journal, 61(1), 2022, 247–255.
  21. [21] Y. Zhou, X. Xu, F. Shen, X. Zhou, and H. T. Shen, Flow-edge guided unsupervised video object segmentation, IEEETransactions on Circuits and Systems for Video Technology,32(12), 2021, 8116–8127.
  22. [22] Y. Guo, Z. Mustafaoglu, and D.Koundal,Spam detection usingbidirectional transformers and machine learning classifier algo-rithms, Journal of Computational and Cognitive Engineering,2(1), 2023, 5–9.
  23. [23] L. Falaschetti, L. Manoni, and C. Turchetti, A low-rank CNNarchitecture for real-time semantic segmentation in visualSLAM applications, IEEE Open Journal of Circuits andSystems, 3, 2022, 115–133.
  24. [24] D. Cui, Y. Wu, and Z. Xiang, Finite-time adaptive fault-tolerant tracking control for nonlinear switched systems withdynamic uncertainties, International Journal of Robust andNonlinear Control, 31(8), 2021, 2976–2992.
  25. [25] D. Cui, W. Zou, and J. Guo, Adaptive fault-tolerantdecentralized tracking control of switched stochastic uncertainnonlinear systems with time-varying delay, InternationalJournal of Adaptive Control and Signal Processing, 36(12),2022, 2971–2987.

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