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黄承赓

副教授硕士生导师
性别：男
学历：博士研究生毕业
学位：工学博士学位
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B543
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[1] Men, C., Han, Y., Wang, P., Tao, J., & Huang, C. G. (2025). The interpretable reasoning and intelligent decision-making based on event knowledge graph with LLMs in fault diagnosis scenarios., IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-16,
[2] Huang, C. G., Men, C., Yazdi, M., Han, Y., & Peng, W. (2022). Transfer fault prognostic for rolling bearings across different working conditions: a domain adversarial perspective., The International Journal of Advanced Manufacturing Technology, pp. 1-19,
[3] Li, H., Yazdi, M., Huang, C. G.*, & Peng, W. (2022). A reliable probabilistic risk-based decision-making method: Bayesian Technique for Order of Preference by Similarity to Ideal Solution (B-TOPSIS)., LSoft Computing, vol. 26, no. 22, pp. 12137-12153,
[4] Sirui Ma, Yunxiao Chen, Xinyu Xia, Zhi Wang, Yuhang Wei, Xiang Li, Yuyi Sun, Dan Zhao, Ling Zhang, Wei Huang, Cheng-Geng Huang*, “RISKVLM: Interpretable CRRT Venous-Chamber Thrombus Risk Assessment Using Near-Infrared Temporal Evidence”, 中国仪器仪表学会学术年会 (国内一级学会年会)，优秀学术成果,
[5] Zhu, R., Peng, W., Han, Y., & Huang, C. G.* (2022). Intelligent health monitoring of machine tools using a Bayesian multibranch neural network., IEEE Sensors Journal, vol. 22, no. 12, pp. 12183-12196,
[6] Z. Deng, Y. Yu, Y. Zhou, J. Zhou, M Xie, B. Tao, Y. Lai, J. Wen, Z. Fan, X. Liu, D Zhao, L. W. Feng, Y. Cheng, C. G. Huang*, W Yue*, W Huang*.. (2024). Ternary logic circuit and neural network integration via small molecule‐based antiambipolar vertical electrochemical transistor., Advanced Materials, vol. 36, no. 41, pp. 2405115,
[7] Huang, C. G., Zhu, J., Han, Y., & Peng, W. (2021). A novel Bayesian deep dual network with unsupervised domain adaptation for transfer fault prognosis across different machines., IEEE Sensors Journal, vol. 22, no. 8, pp. 7855-7867,
[8] Huang, C. G., Yin, X., Huang, H. Z., & Li, Y. F. (2019). An enhanced deep learning-based fusion prognostic method for RUL prediction, IEEE Transactions on Reliability, vol. 69, no. 3, pp. 1097-1109,
[9] Huang, C. G., Huang, H. Z., Li, Y. F., & Peng, W. (2021). A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing, Journal of Manufacturing Systems, vol. 61, pp. 757-772,
[10] Huang, C. G., Huang, H. Z., & Li, Y. F. (2019). A bidirectional LSTM prognostics method under multiple operational conditions, IEEE Transactions on Industrial Electronics, vol. 66, no. 11, pp. 8792-8802,
[11] Zhu, J., Wang, Y., Huang, C. G.*, Shen, C., & Chen, B. (2023). A new incremental learning for bearing fault diagnosis under noisy conditions using classification and feature-level information, IEEE Transactions on Instrumentation and Measurement, vol. 73, no. 1-14,
[12] Zhu, R., Peng, W., Wang, D., & Huang, C. G.* (2023). Bayesian transfer learning with active querying for intelligent cross-machine fault prognosis under limited data, Mechanical Systems and Signal Processing, vol. 183, no. 109628,
[13] Li, H., Liang, Q., Du, J., Huang, C. G.*, Wang, L., Li, Y., Huang, H. Z. (2026). Uncertainty-Aware and Reliable Remaining Useful Life Prediction Using a Dual-View Graph Transformer with KAN-Based Multi-Feature Fusion., Reliability Engineering & System Safety, vol. 113349, Jul 2026.
[14] Liang, Q., Li, H. N., Zhu, J., Feng, K., Hu, J. W., Cheng, Y. H., Huang, C. G.* Hierarchical domain-physical topology enhanced Fully-Connected Spatial-Temporal Graph Neural Network for remaining useful life prediction., Reliability Engineering & System Safety, vol. 112848, Jul 2026.
[15] Huang, C. G., Li, H., Peng, W.*, Tang, L. C., Ye, Z. S. Personalized federated transfer learning for cycle-life prediction of lithium-ion batteries in heterogeneous clients with data privacy protection., IEEE Internet of Things Journal,, vol. 11, no. 22, pp. 36895-36906, Sep 2024.

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