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彭 勇 副教授

硕士生导师

计算机科学与技术

机器学习、模式识别和脑-机接口

yongpeng@hdu.edu.cn
杭州电子科技大学下沙校区信仁楼516
+86-571-86878578
  • 彭勇,男,1985年10月生,安徽巢湖人,分别于中国人民解放军陆军军官学院、中国科学院大学、上海交通大学获工学学士、硕士和博士学位。曾受国家留学基金委资助,于2012年9月至2014年8月在美国密歇根大学安娜堡分校电气工程与计算机系交流。2015年6月加入杭州电子科技大学,现任计算机学院认知与智能计算研究所副研究员。

            研究领域:主要从事机器学习、模式识别与脑机交互算法及应用研究,在国内外期刊与会议发表论文20余篇,其中在Information Sciences, Neural Networks, Applied Soft Computing等SCI期刊以第一作者发表论文9篇(含二区论文3篇,ESI高被引论文1篇),累计影响因子超过22。现主持国家自然科学基金青年项目、浙江省科技计划、江苏省重点实验室创新基金、广西高校重点实验室开放基金和杭州电子科技大学人才引进启动基金等。曾获2009年度中国科学院院长奖。

    希望招收数学基础好或者具有较好编程经验的学生攻读硕士学位研究生。

  • [1] Natural Science Foundation of China(61971173), Research on the knowledge transfer strategy for cross-subject and cross-session emotion recognition from EEG signals, 01/2020-12/2023, RMB708,000, PI
    [2] Natural Science Foundation of China(61602140), Research on joint feature learning and recognition algorithms based on low-rank modeling,01/2017-12/2019,RMB238,000, PI
    [3] Zhejiang Science and Technology Program (2017C33049), Research on the multi-modal driving fatigue detection algorithm and simulation platform development, 01/2017-12/2019, RMB200,000, PI
    [4] Postdoctoral Science Foundation of China (2017M620470), Low-rank learning based data analytics, 01/2018-12/2019, RMB80,000, PI
    [5] Funding of Sochoow University (KJS1841), Research on the rank-constrained joint structured graph learning and recognition algorithms, 04/2019-03/2021,RMB50,000, PI
    [6] Funding of Anhui University (ADXXBZ201704), Research on joint structured graph learning and clustering based on self-representation, 01/2018-12/2019, RMB50,000, PI
    [7] Funding of Anhui Polytechnical University (GDSC202015), Research on driving fatigue detection from EEG signals based on ensembling regression, 01/2020-06/2020, RMB100,000, PI
    Publications:
    [1] Yong Peng, Yikai Zhang, Feiwei Qin, Wanzeng Kong. Joint non-negative and fuzzy coding with graph regularization for efficient data clustering. Egyptian Informatics Journal, doi: /10.1016/j.eij.2020.05.001, 2020. (SCI三区,影响因子2.306)
    [2] Yong Peng, Leijie Zhang, Wanzeng Kong, Feiwei Qin, Jianhai Zhang. Joint low-rank representation and spectral regression for robust subspace learning. Knowledge-Based Systems, 195, 105723, 2020. (SCI二区,影响因子5.101)
    [3] Yong Peng, Qingxi Li, Wanzeng Kong, Jianhai Zhang, Bao-Liang Lu, Andrzej Cichocki. Joint semi-supervised feature auto-weighting and classification model for EEG-based cross-subject sleep quality evaluation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, May 4-8, pages 946-950, 2020. (CCF推荐B类会议)
    [4] Yong Peng, Leijie Zhang, Wanzeng Kong, Feiping Nie, Andrzej Cichocki. Joint structured graph learning and unsupervised feature selection. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, pages 3572-3576, 2019. (CCF推荐B类会议)
    [5] Yong Peng, Yanfang Long, Feiwei Qin, Wanzeng Kong, Feiping Nie, Andrzej Cichocki. Flexible non-negative matrix factorization with adaptively learned graph regularization. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, pages 3107-3111, 2019. (CCF推荐B类会议,引用2次)
    [6] Yong Peng, Rixin Tang, Wanzeng Kong, Jianhai Zhang, Feiping Nie, Andrzej Cichocki. Joint graph learning and clustering via concept factorization. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, pages 3162-3166, 2019. (CCF推荐B类会议)
    [7] Yong Peng, Wanzeng Kong, Feiwei Qin, Feiping Nie. Manifold adaptive kernelized low-rank representation for semi-supervised image classification. Complexity, Volume 2018 (2018), Article ID 2857594, 2018.(SCI二区,影响因子2.591,引用2次)
    [8] Yong Peng, Rixin Tang, Wanzeng Kong, Feiwei Qin, Feiping Nie. Parallel vector field regularized non-negative matrix factorization for image representation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, April 15-20, pages 2216-2220, 2018.( CCF推荐B类会议,引用3次)
    [9] Yong Peng, Wanzeng Kong, Bing Yang. Orthogonal extreme learning machine for image classification. Neurocomputing, 266: 458-464, 2017.(SCI二区,影响因子4.072,引用16次)
    [10] Yong Peng, Bao-Liang Lu. Discriminative extreme learning machine with supervised sparsity preserving for image classification. Neurocomputing, 261: 242-252, 2017.(SCI二区,影响因子4.072,引用54次)
    [11] Yong Peng, Bao-Liang Lu. Robust structured sparse representation via half-quadratic optimization for face recognition. Multimedia Tools and Applications,76(6): 8859-8880, 2017. (SCI三区,影响因子2.101,引用14次)
    [12] Yong Peng, Bao-Liang Lu. Discriminative manifold extreme learning machine and applications to image and EEG signal classification. Neurocomputing, 174:265--277, 2016.(SCI二区,影响因子4.072,引用30次)
    [13] Yong Peng, Wei-Long Zheng, Bao-Liang Lu. An unsupervised discriminative extreme learning machine and its applications to data clustering. Neurocomputing, 174: 250--264, 2016.(SCI二区,影响因子4.072,引用28次)
    [14] Yong Peng, Xianzhong Long, Bao-Liang Lu. Graph based semi-supervised learning via structure preserving low rank representation. Neural Processing Letters, 41(3): 389--406,2015. (SCI三区,影响因子2.591,引用8次)
    [15] Yong Peng, Bao-Liang Lu, Suhang Wang. Enhanced low rank representation via sparse manifold adaption for semi-supervised learning. Neural Networks, 65: 1--17, 2015.(SCI一区TOP,影响因子5.785,引用34次)
    [16] Yong Peng, Bao-Liang Lu. Hybrid learning clonal selection algorithm. Information Sciences, 296: 128--146, 2015.(SCI一区TOP,影响因子5.524,引用28次)
    [17] Yong Peng, Suhang Wang, Xianzhong Long, Bao-Liang Lu. Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing,149: 340--353, 2015.(SCI二区,影响因子4.072,引用86次,曾入选ESI高被引论文)
    [18] Wei-Long Zheng, Jia-Yi Zhu, Yong Peng, Bao-Liang Lu. EEG-based emotion classification using deep belief networks. IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China, July 14-18, pages 1--6, 2014. (CCF推荐B类会议,引用182次)
    [19] Yong Peng, Bao-Liang Lu. A hierarchical particle swarm optimizer with latin sampling based memetic algorithm for numerical optimization. Applied Soft Computing, 13(5): 2823--2836, 2013.(SCI二区,影响因子4.873,引用22次)
    [20] Yong Peng, Shen Wang, Bao-Liang Lu. Marginalized denoising autoencoder via graph regularization for domain adaptation. International Conference on Neural Information Processing (ICONIP), Daegu, South Korea, Nov 3-7, pages 156--163, 2013.(CCF推荐C类会议,引用9次)

  • 1.   2018年中国电子学会三等奖。
    2.  2009年中国科学院院长奖。