副高级

 姓名：黄昊杰
 性别：男
 职称：副教授，硕导
 学位：博士
 电子邮件： haojie.huang@fzu.edu.cn
 研究方向：流程工业故障诊断与监测，概率机器学习，可解释机器学习，污水处理智能诊断，储能系统仿真

教育工作经历

2026.9至今 福州大学，电气工程与自动化学院，副教授

2025      福建省级高层次人才（C类）

2024.9    入选福州大学旗山学者（国内引进项目）

2023.9~2026.8 福州大学，电气工程与自动化学院，讲师

2021.10~2022.10 University Of Duisburg-Essen，Automatic control and complex systems，联合培养博士生

2017.9~2023.7 华东理工大学，控制科学与工程专业，博士（硕博连读）

2013.9~2017.6 华东理工大学，自动化专业，学士

科研简介

1. 国家自然科学基金青年基金项目,（基于可迁移高斯过程回归方法的可解释建模及其应用研究），30万, 主持

2. 华能西安热工院科研项目，（储能系统仿真相关），218万，主持

3. 福州大学科研启动项目，（基于概率机器学习的污水处理过程的故障诊断及软测量建模），15万，主持

4. 福州大学旗山学者项目，（基于概率机器学习与可解释AI的故障诊断及其工业应用），15万，主持

代表性论文

[1] H. Huang, X. Peng, W. Du, S. X. Ding and W. Zhong, "Nitrogen Oxides Concentration Estimation of Diesel Engines Based on a Sparse Nonstationary Trigonometric Gaussian Process Regression with Maximizing the Composite Likelihood," in IEEE Transactions on Industrial Electronics, 2022

[2] H. Huang, Y. Song, X. Peng, S. Ding, W. Zhong, and W. Du, "A Sparse Nonstationary Trigonometric Gaussian Process Regression and its Application on Nitrogen Oxides Prediction of the Diesel Engine," IEEE Transactions on Industrial Informatics, 2021.

[3] H. Huang, Z. Li, X. Peng, S. X. Ding, and W. Zhong, "Gaussian Process Regression with Maximizing the Composite Conditional Likelihood," IEEE Transactions on Instrumentation and Measurement, 2021.

[4] H. Huang, X. Peng, C. Jiang, Z. Li, and W. Zhong, "Variable-Scale Probabilistic Just-in-Time Learning for Soft Sensor Development with Missing Data," Industrial & Engineering Chemistry Research, vol. 59, no. 11, 2020.

[5] H. Huang, X. Peng, W. Du, and W. Zhong, "Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition," IEEE Transactions on Instrumentation and Measurement, 2024.

[6] Yang D, Huang H, Wang J, et al. Incremental Contrastive Learning With Dual Distilling for Source-Free Domain Adaptation in Industrial Process Fault Diagnosis[J]. IEEE Transactions on Industrial Informatics, 2026.

[6] Su, C., Peng, X., Yang, D., Lu, R., Huang, H., & Zhong, W. A Transferable Ensemble Additive Network for Interpretable Prediction of Key Performance Indicators[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 1-14.

[7] Zhu, Y., Zhang, Y., Xu, Z., Lin, Y., Wang, R., Huang, H., & Yuan, Y. An Efficient Wood Defect Segmentation Method for Seen and Unseen Wood Species Based on Domain Generalization[J]. IEEE Transactions on Instrumentation and Measurement, 2026.

学术兼职

IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics等期刊审稿人

所属团队

福州大学先进控制技术研究中心