办 公 室: A622          

联系电话: 022-28117054    

邮 箱: geyang406@tute.edu.com    

研究方向

机器学习、大数据建模、智能检测、工业数字孪生

教育背景

2009.09–2014.07 博士 中国科学院

2006.09–2009.07 硕士 北京大学

2002.09–2006.07 学士 北京大学

工作经历

2014.7–今 天津职业技术师范大学 讲师

教学工作

本科生课程 概率论与数理统计、机器学习原理与方法

研究生课程 机器学习导论

科研项目

2025–2028 天津市自然科学基金面上项目 剐齿刀形-性融合设计理论与精准优化方法研究 参加

2018–2021 国家自然科学基金面上项目 一些复杂数据的统计过程监控与诊断 参加

2018–2021 天津市自然科学基金青年项目 数据流变点的MOSUM型在线监控 主持

论文成果（部分列出）

[1] Yang, Z., Ge, Y., Ji, X., Li X., Jiao D., Hou Y., Zhang Y. and Hao D.* (2025) Investigation of fuel cell stack performance degradation based on 1000 h durability experiments and long short-term memory prediction frameworks under dynamic load conditions. Energy and AI, 22, 100628.

[2] Zhou, Y., Ge, Y.* and Jia, L. (2024) Double robust federated digital twin modeling in smart grid. IEEE Internet of Things Journal (IoT), 11(24), 39913 - 39931.

[3] Ge, Y., Hou, P.*, Chen, J., Lai, Y., Su, S. and Ji, Z. (2024) Type B uncertainty evaluation for real driving emission test based on second-by-second data. Accepted at International Journal of Vehicle Performance (IJVP).

[4] Ge, Y., Zhou, Y.* and Jia, L.* (2024) Adaptive personalized federated learning with one-shot screening. IEEE Internet of Things Journal (IoT), 11(9), 15375-15385.

[5] Ge, Y., Hou, P.*, Lv, T., Lai, Y., Su, S., Luo, W., He, M. and Xiao, L. (2023) Machine learning-aided remote monitoring of NOx emissions from heavy-duty diesel vehicles based on OBD data streams. Atmosphere, 14(4), 651.

[6] Su, S., Hou, P.*, Wang, X.*, Lv, L., Ge, Y., Lv, T., Lai, Y., Luo, W. and Wang, Y. (2023) Evaluating the measurement uncertainty of on-road NOx using a portable emission measurement system (PEMs) based on real testing data in China. Atmosphere, 14(4), 702.

[7] Qiu, S., Liu, Q., Ge, Y. (2023) Confidence intervals of proportion differences for stratified combined unilateral and bilateral data. Communications in Statistics-Simulation and Computation, 52(8), 3839-3862.

[8] Su, S., Ge, Y., Hou, P., Wang, X., Wang, Y.*, Lv, T., Luo, W., Lai, Y., Ge, Y. and Lv, L. (2021) China VI heavy-duty moving average window (MAW) method: Quantitative analysis of the problem, causes, and impacts based on the real driving data. Energy, 225, 120295.

[9] 葛旸, 李纯. (2018) 典型例题在《概率统计》教学中的创新应用. 天津职业技术师范大学学报, 28(1): 52-56.

[10] Lu, S., Li, H., Cui, D. and Ge, Y. (2017) A modified method to calculate the pelvis trajectory of crash dummies during frontal impact. in: Proc. ICMTMA, Changsha, China, 148-149, IEEE, 2017.