彭勇

彭勇，博士，教授，认知与智能计算研究所副所长，主要从事机器学习、模式识别与脑机接口相关研究。主持国家自然科学基金青年项目、浙江省科技计划、中国博士后科学基金、CCF-腾讯犀牛鸟创意基金等，参与国家重点研发计划、国家自然科学基金面上项目等。第一作者发表SCI期刊论文11篇（其中1篇入选ESI高被引论文）。获2009年度中国科学院院长奖。

2010.09-2015.06   工学博士   上海交通大学计算机科学与工程系

2012.09-2014.08   Visiting PhD Student   密西根大学安娜堡分校电子工程与计算机科学系

2007.09-2010.07   工学硕士   中国科学院研究生院

2002.09-2006.07   工学学士   解放军炮兵学院

2022.01-至今         杭州电子科技大学   教授

2016.01-2021.12   杭州电子科技大学   副研究员、硕士生导师

2015.06-2015.12   杭州电子科技大学   讲师

12/2018，中国电子学会三等奖(6/6)

03/2018，校优秀骨干教师

12/2009，中国科学院院长优秀奖(1/1)

[1] 国家自然科学基金-面上项目（61971173），面向跨被试与跨时段脑电情感识别的知识迁移方法研究，01/2020-12/2023，70.8万，主持

[2] 国家自然科学基金-青年项目（61602140）：基于低秩模型的联合特征学习与识别算法研究，23.8万，01/2017-12/2019，主持已结题

[3] 浙江省科技计划-公益技术应用研究（2017C33049）：面向主动交通安全的驾驶员疲劳检测关键技术研究与系统开发，20万，01/2017-12/2019，主持已结题

[4] 浙江省自然科学基金（LY21F030005），面向结构化图构造的自适应特征权重学习与邻域选择方法研究，01/2021-12/2023，10万，主持

[5] 中国博士后科学基金第62批面上一等资助（2017M620470）：低秩数据分析及应用，8万，01/2018-07/2019，主持

[6] 浙江省属高校基本科研业务费（GK209907299001-008），脑电情感识别研究，06/2020-06/2022，20万，主持

[7] CCF-腾讯犀牛鸟创意基金（IAGR20170105）：面向低质量数据特征提取的无监督与半监督低秩子空间学习算法研究，3万，10/2017-12/2018，主持已结题

[8] 国防科技重点实验室开放基金（SSKF2018001），****检测与识别，01/2019-06/2020，5万，主持已结题

[9] 其他，集成学习框架下的脑电疲劳回归检测算法研究，01/2020-06/2021，10万，主持已结题

[10] 其他，基于脑电的飞行员疲劳状态检测关键技术研究，01/2022-12/2023，10万，主持

[11] 其他，联合脑电特征迁移与情绪状态估计方法研究，09/2021-08/2024，5万，主持

[12] 其他：基于自表示模型的联合结构化图学习与聚类算法研究，5万，01/2018-12/2019，主持已结题

[1] ****开发，01/2022-08/2022,30万

[1] 全国高等院校计算机基础教育研究会计算机基础教育教学研究项目（2021-AFCEC-195），面向信息类专业卓越拔尖本科人才培养的科教产协同育人模式探索与实践，立项无经费，05/2021-04/2023，主持

[2] 教育部产学协同育人项目（201802164016）：《机器学习与模式识别》创新实践课程的研究性教学改革研究与实践，立项无经费，09/2019-03/2020，主持已结题

[1] Jin Cao, Ran Xu, Xinnan Lin, Feiwei Qin, Yong Peng, Yanli Shao. Adaptive receptive field U-shaped temporal convolutional network for vulgar action detection. Neural Computing & Applications, accepted, 2022.

[2] Yong Peng, Wanzeng Kong, Feiwei Qin, Feiping Nie, Jinglong Fang, Bao-Liang Lu, Andrzej Cichocki. Self-weighted Semi-supervised Classification for Joint EEG-based Emotion Recognition and Affective Activation Patterns Mining, IEEE Transactions on Instrumentation and Measurement, DOI: 10.1109/TIM.2021.3124056, 2021.

[3] Bing Yang, Xueqin Xiang, Wanzeng Kong, Yong Peng, Jinliang Yao. Adaptive multi-task learning using lagrange multiplier for automatic art analysis. Multimedia Tools and Applications, DOI: 10.1007/s11042-021-11360-7, 2021.

[4] Senwei Xu（研究生）, Li Zhu, Wanzeng Kong, Yong Peng, Hua Hu, Jianting Cao. A novel classification method for EEG-based motor imagery with narrow band spatial filters and deep convolutional neural network. Cognitive Neurodynamics, DOI: 10.1007/s11571-021-09721-x, September 2021.

[5] Haowei Jiang（本科生）, Feiwei Qin, Jin Cao, Yong Peng, Yanli Shao. Recurrent Neural Network from Adder’s Perspective: Carry-lookahead RNN. Neural Networks, 144, 297-306, 2021.

[6] Yong Peng, Feiwei Qin, Wanzeng Kong, Feiping Nie, Yuan Ge, Andrzej Cichocki. GFIL: a unified framework for the analysis of features, frequency bands, channels in EEG-based emotion recognition. IEEE Transactions on Cognitive and Developmental Systems, DOI: 10.1109/TCDS.2021.3082803, 2021.

[7] Yong Peng, Xin Zhu, Feiping Nie, Wanzeng Kong, Yuan Ge. Fuzzy graph clustering. Information Sciences, 571: 38-49, 2021.

[8] Yong Peng, Yikai Zhang, Feiwei Qin, Wanzeng Kong. Joint non-negative and fuzzy coding with graph regularization for efficient data clustering. Egyptian Informatics Journal, 22(1): 91-100, 2021.

[9] Wenna Huang（研究生）, Yong Peng, Yuan Ge, Wanzeng Kong. A new kmeans formulation and its generalization achieved by joint spectral embedding and rotation. PeerJ Computer Science, doi: 10.7717/peerj-cs.450, 2021.

[10] Fangyao Shen（研究生）, Yong Peng, Wanzeng Kong, Guojun Dai. Multi-scale frequency bands ensemble learning for EEG-based emotion recognition. Sensors, 21(4), 1262, 2021.

[11] Xuanyu Jin（研究生）, Jiajia Tang（研究生）, Xianghao Kong (本科生), Yong Peng, Jianting Cao, Qibin Zhao, Wanzeng Kong. CTNN: a convolutional tensor-train neural network for multi-task brainprint recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 103-112, 2021.

[12] Xinnan Lin(研究生), Feiwei Qin, Yong Peng, Yanli Shao. Fine-Grained Pornographic Image Recognition with Multiple Feature Fusion Transfer Learning. International Journal of Machine Learning and Cybernetics, 12, 73-86, 2021.

[13] Yikai Zhang (本科生), Yong Peng, Hongyu Bian, Yuan Ge, Feiwei Qin, Wanzeng Kong. Auto-weighted concept factorization for joint feature map and data representation learning. Journal of Intelligent & Fuzzy Systems, 41(1): 69-81, 2021.

[14] Yuxuan Zhu(研究生), Yong Peng, Yang Song, Kenji Ozawa, Wanzeg Kong. RAMST-CNN: A residual and multiscale spatio-temporal convolution neural network for personal identification with EEG. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E104-A, No.2, pp.563-571, 2021.

[15] 秦飞巍，沈希乐，彭勇，邵艳利，袁文强，计忠平，白静。无人驾驶中的场景实时语义分割方法。计算机辅助设计与图形学学报，33 (7), 1026-1037, 2021. （中文一级学报）

[16] Yong Peng, Qingxi Li, Wanzeng Kong, Feiwei Qin, Jianhai Zhang, Andrzej Cichocki. A joint optimization framework to semi-supervised RVFL and ELM networks for efficient data classification. Applied Soft Computing, volume 97, Part A, Article ID 106756, 2020.

[17] 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.

[18] Yong Peng, Leijie Zhang, Wanzeng Kong, Feiwei Qin, Jianhai Zhang. Low rank spectral regression via matrix factorization for efficient subspace learning. Journal of Intelligent & Fuzzy Systems, 39(3): 3401-3412, 2020.

[19] 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.

[20] Qinghao Ye（本科生）, Daijian Tu（本科生）, Feiwei Qin, Zizhao Wu,Yong Peng, Shuying Shen. Dual attention based fine-grained leukocyte recognition for imbalanced microscopic images. Journal of Intelligent & Fuzzy Systems, 37(5): 6971-6982, 2019.

[21] 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.

[22] 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.

[23] Yong Peng, Rixin Tang, Wanzeng Kong, Jianhai Zhang, Feiping Nie, Andrzej Cichocki. Joint structured 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.

[24] 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.

[25] Feiwei Qin, Nannan Gao, Yong Peng, Zizhao Wu, Shuying Shen, Artur Grudtsin. Fine-grained leukocyte classification with deep residual learning for microscopic images. Computer Methods and Programs in Biomedicine, 162: 243-252, 2018.

[26] Feiwei Qin, Haibing Xia, Yong Peng, Zizhao Wu. Integrated modeling, simulation and visualization for nano materials. Complexity, Volume 2018 (2018), Article ID 5083247.

[27] Wanzeng Kong, Shijie Guo, Yanfang Long,Yong Peng, Hong Zeng, Xinyu Zhang, Jianhai Zhang. Weighted extreme learning machine for P300 detection with application to brain computer interface. Journal of Ambient Intelligence and Humanized Computing, 10.1007/s12652-018-0840-1, May 2018.

[28] 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.

[29] Jianhai Zhang, Shaokai Zhao, Guodong Yang, Jiajia Tang, Tao Zhang, Yong Peng, Wanzeng Kong, Emotional-state brain network analysis revealed by minimum spanning tree using EEG signals. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, December 3-6, pages 1045-1048, 2018.

[30] Jianhai Zhang, Na Zhang, Jiajia Tang, Jianting Cao, Wanzeng Kong, Yong Peng. A new method for brain death diagnosis based on phase synchronization analysis with EEG. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, December 3-6, pages 1135-1138, 2018.

[31] Yong Peng, Bao-Liang Lu. Discriminative extreme learning machine with supervised sparsity preserving for image classification. Neurocomputing, 261: 242-252, 2017.（引用70+）

[32] Yong Peng, Wanzeng Kong, Bing Yang. Orthogonal extreme learning machine for image classification. Neurocomputing, 266: 458-464, 2017.

[33] 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.

[34] Zhi-Jie Wang, Xiao Lin, Mei-E Fang, Bin Yao, Yong Peng, Haibin Guan, MinyiGuo. RE2L: An efficient output-sensitive algorithm for computing boolen operations on circular-arc polygons and its applications. Computer-Aided Design, 83(2):1--14, 2017.

[35] Yong Peng, Bao-Liang Lu. Discriminative manifold extreme learning machine and applications to image and EEG signal classification. Neurocomputing, 174:265--277, 2016.

[36] 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.

[37] Xianzhong Long, Hongtao Lu, Yong Peng, Xianzhong Wang, Shaokun Feng. Image classification based on improved VLAD. Multimedia Tools and Applications,75(10), 5533--5555, 2016.

[38]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.（曾入选ESI高被引论文）

[39]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.

[40] Yong Peng, Bao-Liang Lu. Hybrid learning clonal selection algorithm. Information Sciences, 296: 128--146, 2015.

[41] 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.

[42] Xianzhong Long, Hongtao Lu,Yong Peng, Wenbin Li. Graph regularized discriminative nonnegative matrix factorization for face recognition. Multimedia Tools and Applications, 72(3): 2679--2699, 2014

[43] 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. （引用250+）

[44] 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.

[45] 林浒（导师），彭勇。面向多目标优化的适应度共享免疫克隆算法。控制理论与应用，28（2）：206-214， 2011（中文权威期刊）

[1] 数据处理方法、装置、终端及存储介质。公开号：CN110032704A（实审）

[2] 一种特征权重自学习的睡眠质量检测关键脑区判定方法。公开号：CN111067513A（实审）

[3] 一种基于结构化数据分解的脑电信号分析方法。公开号：CN111265214A（实审）

[4] 一种难样本重训练的深度神经网络声呐目标检测方法。公开号：CN111738081A（实审）

[5] 一种特征权重自适应学习的脑电情绪识别方法。公开号：CN112773378A（实审）

[6] 一种样本与特征质量联合量化评估的脑电疲劳检测方法。公开号：CN113143275A（实审）

[7] 联合特征迁移与图半监督标记传播的脑电情感识别方法。公开号：CN113157094A（实审）

[8] 一种特征权重自学习的在线RVFL脑电疲劳检测方法。申请号：202111212547.4

[9] 一种基于聚类的多任务情感脑电特征提取与识别方法。申请号：202111340210.1

[10] 一种实时估计目标被试者情感状态的脑电特征迁移学习方法。申请号：202111491551.9

[11] 一种基于自适应图学习的半监督脑电情感识别方法。申请号：202111547894.2

[12] 特征贡献度差异化脑电数据重构的情感激活模式发掘方法。申请号：202111608170.4

[13] 一种联合判别子空间发掘与半监督脑电情感识别方法。申请号： 202111578215.8

2021年度中国国际“互联网+”创新创业大赛，银奖（合作）

2021年度中国高校大数据挑战赛，二等奖

2021年度浙江省“挑战杯”大学生课外学术科技作品竞赛，二等奖（合作）

2020年度浙江省“挑战杯”大学生创业计划竞赛，三等奖（合作）

2019年度浙江省“挑战杯”大学生课外学术科技作品竞赛，二等奖（合作）

省级新苗人才计划（2019张怿恺、2020程诺、2021宣欣祎）

省级新苗人才计划（2021李幸、2022张怿恺）

国家奖学金（2020朱鑫、2020李晴熙、2021黄文娜、2021张怿恺）

2021年度浙江省“挑战杯”大学生课外学术科技作品竞赛，二等奖（合作）

华为奖学金（2019朱鑫、2021李幸）

教育厅科研项目（2020黄文娜、2021李幸）

校科研创新基金（2018汤日新、2019朱鑫、2020王文娟、2021李幸）

校优秀硕士论文培育基金（2019汤日新）

校优秀硕士论文（2019张雷杰）

yongpeng@hdu.edu.cn, QQ:491743196