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张遨 讲师

计算机学院

通讯地址:江苏省镇江市江苏科技大学计算机学院

个人邮箱:azh@just.edu.cn

邮政编码:212003

办公地点:计算机学院511

传真:

  • 个人简介

  • 研究方向

  • 科研团队

  • 获奖动态

  • 教学随笔

  • 教育经历

  • 课程教学

  • 论文著作

  • 科研论文

  • 科研横向项目

  • 科研纵向项目

  • 科研专利

  • 科研动物专利

  • 张遨,男,博士,讲师,硕士研究生导师。主要研究方向为模式识别、图像处理、故障诊断等。主持江苏省产学研合作项目1项、横向项目2项,参与横向项目4项。以第一作者发表SCI论文5篇;授权软著、发明专利多项。



  • 模式识别、图像处理、故障诊断、字典学习等

    • 科研项目

      [1] 基于无人机的森林火灾智能预警系统,江苏省产学研合作项目
      [2] 某训练数字资源服务平台,横向项目
      [3] 持证上岗理论考试系统开发,横向项目


    • 论文著作

      [1] Ao Zhang, Liang Shi, Tensor Feature Extraction Method Based on Matrixed Label and Inpainting, IEEE Access, 2024,12: 153664-153675.
      [2] Linhao Li, Ao Zhang*, Supervised data-dependent kernel low-rank preserving projection for image recognition, in Proceedings of 5th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), 2024
      [3] 周毅岩;石亮;张遨*;岳晓宇, 基于解纠缠表示学习的人脸反欺诈算法, 计算机应用研究,2024,41(8):2502-2507
      [4] Shuaishuai Zhang; Keyu Liu; Taihua Xu; Xibei Yang; Ao Zhang, A meta-heuristic feature selection algorithm combining random sampling accelerator and ensemble using data perturbation, Applied Intelligence, 2023, 53: 29781-29798
      [5] Ao Zhang; Xianwen Gao, Supervised dictionary-based transfer subspace learning and applications for fault diagnosis of sucker rod pumping systems, Neurocomputing, 2019, 338: 293-306
      [6] Ao Zhang; Xianwen Gao, Supervised data-dependent kernel sparsity preserving projection for image recognition, Applied Intelligence, 2018, 48(12): 4923-4936
      [7] Ao Zhang; Xianwen Gao ; Data-dependent kernel sparsity preserving projection and its application for semi-supervised classification, Multimedia Tools & Applications, 2018, 77(18): 24459-24475
      [8] Ao Zhang; Xianwen Gao ; Fault diagnosis of sucker rod pumping systems based on curvelet transform and sparse multi-graph regularized extreme learning machine, International Journal of Computational Intelligence Systems, 2018, 11(1): 428-437 


    • 专利成果

      授权专利:

      [1] 高宪文; 张遨; 王明顺; 魏晶亮; 郑博元; 侯延彬; 李书行; 李东玉 ; 一种基于子空间迁移学习的有杆泵抽油井故障诊断方法, 2021-6-11, 中国, 201810628647.7 

      [2] 高宪文; 王明顺; 张遨; 张平; 魏晶亮; 郑博元; 陈星宇; 宋乐 ; 基于曲波变换和核稀疏的抽油井半监督故障诊断方法, 2020-4-24, 中国, 201710326671.0

      [3] 高宪文; 王明顺; 郑博元; 张平; 张遨; 魏晶亮; 刘俊辰; 张佳奇 ; 基于凡尔工作点的有杆泵抽油井井下工况诊断方法, 2020-3-20, 中国, 201710236752.1

      [4] 高宪文; 王明顺; 魏晶亮; 张平; 郑博元; 张遨; 张佳奇; 刘俊辰 ; 一种有杆泵抽油井故障分离方法, 2020-2-11, 中国, 201710260900.3

      软件著作权:

      [1] 路障碍物智能规避应用系统;登记号: 2024SR1338604;2024.9.10

      [2] 基于视频的运动目标检测跟踪算法系统;登记号: 2024SR1555850;2024.10.18

      [3] 基于深度学习的船型分类研究系统;登记号:2024SR1782574;2024.11.14

      [4] 农作物疾病诊断智能分析系统;登记号:2024SR1782652;2024.11.14


        


  • 《计算机基础》《高级程序设计语言》等


    近年主要论文:

    [1] Ao Zhang, Liang Shi, Tensor Feature Extraction Method Based on Matrixed Label and Inpainting, IEEE Access, 2024,12: 153664-153675.
    [2] Linhao Li, Ao Zhang*, Supervised data-dependent kernel low-rank preserving projection for image recognition, in Proceedings of 5th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), 2024
    [3] 周毅岩;石亮;张遨*;岳晓宇, 基于解纠缠表示学习的人脸反欺诈算法, 计算机应用研究,2024,41(8):2502-2507
    [4] Shuaishuai Zhang; Keyu Liu; Taihua Xu; Xibei Yang; Ao Zhang, A meta-heuristic feature selection algorithm combining random sampling accelerator and ensemble using data perturbation, Applied Intelligence, 2023, 53: 29781-29798
    [5] Ao Zhang; Xianwen Gao, Supervised dictionary-based transfer subspace learning and applications for fault diagnosis of sucker rod pumping systems, Neurocomputing, 2019, 338: 293-306
    [6] Ao Zhang; Xianwen Gao, Supervised data-dependent kernel sparsity preserving projection for image recognition, Applied Intelligence, 2018, 48(12): 4923-4936
    [7] Ao Zhang; Xianwen Gao ; Data-dependent kernel sparsity preserving projection and its application for semi-supervised classification, Multimedia Tools & Applications, 2018, 77(18): 24459-24475
    [8] Ao Zhang; Xianwen Gao ; Fault diagnosis of sucker rod pumping systems based on curvelet transform and sparse multi-graph regularized extreme learning machine, International Journal of Computational Intelligence Systems, 2018, 11(1): 428-437