嘟嘟成人网

讲师

办公地址：职技楼A213西

联系方式：邮箱：[email protected]

【研究方向】

致力于工业信息化背景下的深度学习技术研究，主要面向多元时间序列建模、过程优化、故障诊断与智能决策等实际工业需求，系统开展图神经网络、注意力机制和自编码器等前沿算法的探索，以推动数据驱动与知识引导相结合的工业智能理论发展与工程应用。

【教育背景】

2024年6月获北京化工大学控制科学与工程专业博士学位

【教学情况】

机器学习，人工智能综合实习

【科研成果】

[1] Li Jince, Shi Y, Li H*, and Yang B. TC-GATN: Temporal Causal Graph Attention Networks with Nonlinear Paradigm for Multivariate Time Series Forecasting in Industrial Processes. J. IEEE Transactions on Industrial Informatics, 2023, 19(6): 7592-7601.  

[2] Li Jince, Fan Y, Wang Z, and Wang Y*. A graph neural network with dual-stage feature aggregation for industrial soft sensors. J. IEEE Transactions on Instrumentation and Measurement, 2025, DOI: 10.1109/TIM.2025.3606039.

[3]. Li Jince, Yang B, Li H*, Wang Y, Qi C, and Yi L. DTDR–ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models. J. Knowledge-Based Systems, 2021, 211: 106508.

[4]. Chu Q, Li Jince, Li H*, et al. An attention transfer entropy based causality analysis with applications in rooting short-term disturbances for chemical processes. J. ISA Transactions, 2022, 136:284-296.

[5] Wang Y, Li Jince, Yang B and Li H*. Stream-data-clustering based adaptive alarm threshold setting approaches for industrial processes with multiple operating conditions. J. ISA transactions, 2022, 129: 594-608.

[6] Yin M, Li Jince, Shi Y, Qi C, and Li H*. Fusing logic rule-based hybrid variable graph neural network approaches to fault diagnosis of industrial processes. J. Expert Systems with Applications, 2024, 238: 121753.

[7] Shi Y, Li Jince, Li H, and Yang B. An Imbalanced Data Augmentation and Assessment Method for Industrial Process Fault Classification with Application in Air Compressors. J. IEEE Transactions on Instrumentation and Measurement, 2023, 72:3521510.

[8] Zhao W, Li Jince, Li H. A multi-task learning approach for chemical process abnormity locations and fault classifications. J. Chemometrics and Intelligent Laboratory System, 2023, 232: 104719.

[9] Li Jince, Li H, Wang Y, Yang B, Qi C, and Li H*. Hybrid cycle reservoir with jumps for multivariate time series prediction: industrial application in oil drilling process. J. Measurement Science and Technology, 2020, 31(1): 015103.

[10] Li H, Zhao W, Shi Y, and Li Jince*. MP-CRJ: Multi-parallel cycle reservoirs with jumps for industrial multivariate time series predictions. J. Transactions of the Institute of Measurement and Control, 2022, 44(11):2093-2105.