个人基本信息

牟淼，男，博士，讲师，硕士生导师，于2024年6月兰州理工大学毕业并获工学博士学位。近五年，参与国家自然科学基金1项，甘肃省高等学校青年博士支持项目1项、甘肃省计划创新引导计划1项、兰州市青年科技人才创新项目1项。近年来在《IEEE Transactions on Instrumentation and Measurement》、《Process Safety and Environmental Protection》、《仪器仪表学报》等国内外期刊上发表论文十余篇，先后获得甘肃省优秀硕士学位论文、研究生国家奖学金等荣誉。

邮箱：miaom@nwnu.edu.cn

研究方向

目前主要从事基于机器学习、模式识别、大数据解析与人工智能的工业过程智能监控、时间序列异常检测等方面的研究。

招生意向

  欢迎踏实认真、数学英语与编程（Python、MATLAB均可）基础良好，愿意投入做科研的学生联系。

研究成果：

[1] Mou M, Zhao X. Gated broad learning system based on deep cascaded for soft sensor modeling of industrial process[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-11.

[2] Mou M, Zhao X, Liu K, et al. Variational autoencoder based on distributional semantic embedding and cross-modal reconstruction for generalized zero-shot fault diagnosis of industrial processes[J]. Process Safety and Environmental Protection, 2023, 177: 1154-1167.

[3] Mou M, Zhao X. Industrial process fault diagnosis based on domain adaptive broad echo network[J]. Journal of the Taiwan Institute of Chemical Engineers, 2024, 159: 105453.

[4] Mou M, Zhao X, Liu K, et al. A latent representation dual manifold regularization broad learning system with incremental learning capability for fault diagnosis[J]. Measurement Science and Technology, 2023, 34(7): 075005.

[5] Mou M, Zhao X. Quality‐relevant and process‐relevant fault monitoring based on GNPER and the fault quantification index for industrial processes[J]. The Canadian Journal of Chemical Engineering, 2023, 101(2): 967-983.

[6] Mou M, Zhao X. Incipient fault detection and diagnosis of nonlinear industrial process with missing data[J]. Journal of the Taiwan Institute of Chemical Engineers, 2022, 132: 104115.

[7]牟淼,赵小强.基于RLANPE的工业过程故障诊断算法研究[J/OL].控制与决策,1-9[2024-09-03].https://doi.org/10.13195/j.kzyjc.2023.1455.

[8]赵小强,牟淼.基于变量分块的KDLV-DWSVDD间歇过程故障检测算法研究[J].仪器仪表学报,2021,42(02):244-256.DOI:10.19650/j.cnki.cjsi.J2006827.

[9]赵小强,牟淼.基于GSFA-GNPE的动态-静态联合指标间歇过程监控[J].上海交通大学学报,2021,55(11):1417-1428.

[10]Zhao X, Tuo B, Mou M, et al. Batch process quality monitoring based on temporal convolutional networks with depthwise separable coordinated attention module[J]. Asia‐Pacific Journal of Chemical Engineering, 2024, 19(1): e2968.