科研项目 |
[1] 云南省科技厅昆明理工大学“双一流”创建联合专项面上项目, 202301BE070001-037, 大型桥梁健康监测异常数据诊断的可解释自动深度学习方法, 2023年12月至2026年11月, 20万元, 在研, 主持 [2] 云南省基础研究计划面上项目, 202301AT070394, 基于深度学习的结构健康监测极端事件自动识别, 2023年6月至2026年5月, 10万元, 在研, 主持 [3] 云南省教育厅科技创新团队建设项目, 基础设施智能运维科技创新团队, 2023年1月至2025年12月, 40万元, 在研, 参与 [4] 昆明理工大学高层次人才平台建设项目, 大型桥梁结构健康监测异常数据清洗的深度学习方法, 2022年1月至2029年12月, 80万元, 在研, 主持 [5] 国家自然科学基金委员会, 面上项目, 51978216, 基于计算机视觉和深度学习的大型桥梁健康监测异常数据诊断, 2020年1月至2023年12月, 60万元, 结题, 参与 [6] 云南交投集团科技研发项目, 基于深度学习的交通AIOT与边缘计算融合集成技术研究与应用项目, 24万元, 在研，主持 |
代表性 学术论文 |
TANG Z Y, GUO J X, WANG Y H, XU W, BAO Y Q, HE J R*, ZHANG Y Q*. Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion [J]. Computer-Aided Civil and Infrastructure Engineering. 2024. (学校在该期刊首篇, 2025年6月上期封面文章) LIU P P, TANG Z Y*, ZHANG C X, HUANG X M, XU W. Nonuniform data loss reconstruction based on time-series-specialized neural networks for structural health monitoring [J]. Structural Health Monitoring. 2025, 14759217251321760. (学院在该期刊首篇) QIAN G C, TANG Z Y*, GUO J X, HUANG X M, ZHANG C X, XU W*. Consistent seismic event detection using multi-input end-to-end neural networks for structural health monitoring [J]. Structural Control and Health Monitoring. 2025, 9966359. (学院在该期刊首篇) TANG Z Y, CHEN Z C, BAO Y Q, LI H. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring [J]. Structural Control and Health Monitoring, 2019, 26(1): e2296. (ESI高被引论文) BAO Y Q, TANG Z Y, LI H, ZHANG Y F. Computer vision and deep learning-based data anomaly detection method for structural health monitoring [J]. Structural Health Monitoring, 2019, 18(2): 401-421. (ESI高被引论文) BAO Y Q, CHEN Z C, WEI S Y, XU Y, TANG Z Y, and LI H. The state of the art of data science and engineering in structural health monitoring [J]. Engineering. 2019, 5(2): 234-242. (ESI高被引论文) TANG Z Y, BAO Y Q, LI H. Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring [J]. Structural Health Monitoring, 2021, 20(4): 1738-1759. GUO J X, TANG Z Y*, ZHANG C X, XU W*, WU Y H. An interpretable deep learning method for identifying extreme events under faulty data interference [J]. Applied Sciences, 2023, 13(9), 5659. BAO Y Q, TANG Z Y, LI H. Compressive-sensing data reconstruction for structural health monitoring: A machine-learning approach [J]. Structural Health Monitoring, 2020, 19(1): 293-304. LIU D W, TANG Z Y, BAO Y Q*, LI H. Machine-learning-based methods for output-only structural modal identification [J]. Structural Control and Health Monitoring, 2021, 28(12): e2843. XIANG Z L, BAO Y Q, TANG Z Y, and LI H. Deep reinforcement learning-based sampling method for structural reliability assessment [J]. Reliability Engineering & System Safety. 2020, 199: 106901. CHEN Z C, BAO Y Q, TANG Z Y, CHEN J H, and LI H. Clarifying and quantifying the geometric correlation for probability distributions of inter-sensor monitoring data: A functional data analytic methodology [J]. Mechanical Systems and Signal Processing. 2020, 138: 106540. HE J R, GAO R F, and Tang Z Y. A data-driven multi-scale constitutive model of concrete material based on polynomial chaos expansion and stochastic damage model [J]. Construction and Building Materials. 2022, 334: 127441. |
专利 |
[1] 鲍跃全, 李惠, 唐志一. 一种基于计算机视觉和深度学习技术的结构健康监测异常数据诊断方法, 2021.07.16, 中国, ZL201810290291.0. [2] 鲍跃全, 吴迪, 唐志一, 李惠. 一种基于计算机视觉人体姿态估计的施工安全帽佩戴监测方法, 2022.05.17, 中国, ZL201810290291.0. [3] 鲍跃全, 刘大伟, 唐志一, 李惠. 一种基于机器学习的结构模态参数识别方法, 2022.08.02, 中国, ZL201910994657.7. [4] 鲍跃全, 邓岳, 潘秋月, 唐志一, 李惠. 一种基于卷积神经网络与迁移学习的结构健康监测多元数据异常诊断方法, 2022.11.08, 中国, ZL202110720189.1. |
