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    讲师
  • 姓 名:杨冲 职 称:高聘副研究员 导师类别:硕导 E-mail:996827@hainanu.edu.cn

  • 一、个人基本情况
  • 杨冲,工学博士,现为hy5900cc海洋之神“智能制造研究团队”成员,毕业于浙江大学控制科学与工程学院。主要研究方向为机器学习与人工智能技术在复杂工业环境中的前沿探索,聚焦软测量建模、故障检测及多模态智能感知等领域。迄今已发表15篇SCI论文,获得3项国家发明专利和3项软件著作权,并参与多项国家自然科学基金项目。现为中国人工智能学会和中国自动化学会会员,担任多个国际学术期刊审稿人。
  • 二、主要研究方向
  • 多模态融合、智能感知、故障检测、智能制造
  • 三、开设课程
  • 《专业前沿研讨与研究方法论》
  • 四、近年的代表性成果

  • 主持或参与的主要科研项目:

    [1] 国家自然科学基金重点项目: 基于混合增强智能的大型高炉故障诊断与自愈控制基础理论与关键技术,参与。

    [2] 国家工业和信息化部工业互联网创新发展工程项目: 基于工业互联网平台的******数字孪生系统,参与。

     

     

    代表性论文:

    [1] Chong Yang, Chunjie Yang. Deep Fusion of Time Series and Visual Data through Temporal Features: A Soft-sensor Model for FeO Content in Sintering Process [J]. Expert Systems With Applications, 2025, In Press.

    [2] Chong Yang, Chunjie Yang, Xinmin Zhang. Multi-source information fusion for Autoformer: Soft sensor modeling of FeO content in iron ore sintering process [J]. IEEE Transactions on Industrial Informatics, 2023, 19, (12). 11584-11595.

    [3] Chong Yang, Chunjie Yang, Junfang Li, Yuxuan Li, Feng Yan. Forecasting of iron ore sintering quality index: A latent variable method with deep inner structure [J]. Computers in Industry, 2022, 141, 103713.

    [4] Chong Yang, Yuchen Zhang, Mingzhi Huang, Hongbin Liu. Adaptive dynamic prediction of effluent quality in wastewater treatment processes using partial least squares embedded with relevance vector machine [J]. Journal of Cleaner Production, 2021, 314, 128076.

    [5] Feng Yan, Chunjie Yang, Xinmin Zhang, Chong Yang, Zhiyong Ruan. BTPNet: A Probabilistic Spatial-Temporal Aware Network for Burn-Through Point Multistep Prediction in Sintering Process [J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, Early Access.

    [6] Feng Yan, Chunjie Yang, Liyuan Kong, Chong Yang. DAMPNN: Dynamic Adaptive Message Passing Neural Network for Industrial Soft Sensor [J]. IEEE Transactions on Industrial Informatics, 2024, Early Access.

    [7] Wensi Liu, Xiao-Yu Tang, Chong Yang, Chunjie Yang. "RWMS: Reliable weighted multiphase for semi-supervised segmentation", in the 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024).

    [8] Xiongzhuo Zhu, Dali Gao, Chong Yang, Chunjie Yang. A blast furnace fault monitoring algorithm with low false alarm rate: Ensemble of greedy dynamic principal component analysis-Gaussian mixture model[J]. Chinese Journal of Chemical Engineering, 2023, 57: 151-161.

    [9] Xiongzhuo Zhu, Chunjie Yang, Chong Yang, Dali Gao, Siwei Lou. An unsupervised fault monitoring framework for blast furnace: Information extraction enhanced GRU-GMM-autoencoder[J]. Journal of Process Control, 2023, 130: 103087.

    [10] Liu Hongbin, Yang Chong*, Huang Mingzhi, Yoo Changkyoo. Soft sensor modeling of industrial process data using kernel latent variables-based relevance vector machine. Applied Soft Computing, 2020, 90, 106149.

    [11] Hongbin Liu, Jie Yang, Yuchen Zhang, Chong Yang*. Monitoring of wastewater treatment processes using dynamic concurrent kernel partial least squares [J]. Process Safety and Environmental Protection, 2021, 147, 274-282.

     

     

    专利:

    [1] 杨冲, 杨春节, 王文海. RVM烧结矿FeO含量软测量模型的构建及应用, ZL202110497744.9. 2022.04.19.

    [2] 刘鸿斌, 杨冲. 基于偏最小二乘的高斯回归软测量建模方法, 中国: ZL201711476291.1. 2021.11.30.

    [3] 刘鸿斌, 杨冲. 基于高斯过程回归的动态非线性PLS软测量建模方法, 中国: ZL201811212785.3. 2022.04.22.

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