张九思

个人信息Personal Information

副教授（特聘）硕士生导师

性别：男

毕业院校：哈尔滨工业大学

学历：博士研究生毕业

学位：工学博士学位

在职信息：在职人员

所在单位：自动化工程学院

学科：仪器科学与技术

办公地点：电子科技大学清水河校区成电国际创新中心D311

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[1]J. Zhang, K. Qian, H. Luo, Y. Liu, X. Qiao, X. Xu, J. Tian. Process monitoring for tower pumping units under variable operational conditions: From an integrated multitasking perspective.Control Engineering Practice, 2025, 156: 106229.

[2]J. Zhang, K. Zhang, Y. An, H. Luo, S. Yin.An Integrated Multitasking Intelligent Bearing FaultDiagnosisScheme Based on Representation Learning Under Imbalanced Sample Condition.IEEE Transactions onNeural Networks and Learning Systems, 2024, 35(5): 6231-6242.

[3]J. Zhang, C. Huang, M.Y. Chow, X. Li, J. Tian, H. Luo, S. Yin. A Data-model Interactive Remaining UsefulLife Prediction Approach of Lithium-ion Batteries Based on PF-BiGRU-TSAM.IEEE Transactions on IndustrialInformatics, 2024, 20(2): 1144-1154.

[4]J. Zhang, J. Tian, H. Luo, S. Wu, S. Yin, O. Kaynak. Prognostics for the Sustainability of Industrial Cyber-Physical Systems: From an Artificial Intelligence Perspective.IEEE Transactions on Industrial Cyber-Physical Systems, 2024, 2: 495-507.

[5]J. Zhang, J. Tian, P. Yan, S. Wu, H. Luo, S. Yin. Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective.Reliability Engineering& System Safety, 2024, 244: 109950.

[6]J. Zhang, J. Tian, A. M. Alcaide, J. I. Leon, S. Vazquez, L. G. Franquelo, H. Luo, S. Yin. Lifetime ExtensionApproach Based on Levenberg-arquardt Neural Network and Power Routing of DC-DC Converters.IEEE Transactions on Power Electronics, 2023, 38(3): 10280-10291.

[7]J. Zhang, X. Li, J. Tian, H. Luo, S. Yin. An integrated multi-head dual sparse self-attentionnetwork forremaining useful life prediction.Reliability Engineering & System Safety, 2023, 233: 109096.

[8]J. Zhang, X. Li, J. Tian, Y. Jiang, H. Luo, S. Yin. A variational local weighted deep sub-domainadaptationnetwork for remaining useful life prediction facing cross-domain condition.Reliability Engineering &System Safety, 2023, 231: 108986.

[9]J. Zhang, J. Tian, M. Li, J. I. Leon, L. G. Franquelo, H. Luo, S. Yin. A Parallel Hybrid Neural NetworkwithIntegration of Spatial and Temporal Features for Remaining Useful Life Prediction in Prognostics.IEEETransactions on Instrumentation and Measurement, 2023, 72: 1-12.

[10]J. Zhang, Y. Jiang, X. Li, H. Luo, S. Yin, O. Kaynak. Remaining Useful Life Prediction of Lithium-ionBatterywith Adaptive Noise Estimation and Capacity Regeneration Detection.IEEE/ASME Transactions onMechatronics, 2023, 28(2): 632-643.

[11]J. Zhang, Y. Jiang, X. Li, M. Huo, H. Luo, S. Yin. An adaptive remaining useful life prediction approach forsingle battery with unlabeled small sample data and parameter uncertainty.Reliability Engineering & SystemSafety, 2022, 222: 108357.

[12]J. Zhang, Y. Jiang, S. Wu, X. Li, H. Luo, S. Yin. Prediction of remaining useful life based onbidirectionalgated recurrent unit with temporal self-attention mechanism.Reliability Engineering & System Safety,2022, 221: 108297.