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杨云(授课,博士生导师)

时间:2018年04月24日 09:07 浏览次数:[ ]

教师姓名:杨云

职称:授课

有关所:软件工程系

研究领域:人工智能、机器学习、数据挖掘、模式识别、多媒体数据分析/目录/追寻、时间序列数据分析、聪慧医疗,聪慧交通

电子邮件:yangyan19@hotmail.com;yangyun@ynu.edu.cn

私主页:http://www.kkldi.ynu.edu.cn/blog/post/5b615d67c5aedf747d507cb4

杨云,大发棋牌游戏平台教授,博士生导师,CCF尖端会员,入选云南省“远处高层次人才引进计划”,云南省“中青年学术和技术带头人后备人才”,云南大学“东陆家”支持计划,云南大学“青年英才计划”。当中国信息经济学会理事,全国高校人工智能与很数量创新联盟理事,云南省大学数据是和智能计算重点实验室主任,昆明市数据是和智能计算重点实验室主任,《Neural Networks》(中科院JCR同区期刊)编委,《云南大学学报(自然科学版)》编委,多只国际学术会议的程序委员会委员。2011年取得英国曼彻斯特大学计算机科学博士学位。学习博士学位期间,那个入选英国政府资助的海外研究生奖励计划(UK Overseas Research Students Awards Scheme–ORSAS)。博士毕业后,那个在英国萨里大学从事研究员工作,参与由欧共体第七只框架计划资助下的国际合作项目。

2014年3月杨讲话博士以引进人才形式进入云南大学工作,以后分别主持国家自然科学基金项目3起(对上,青年,地方),参与国家自然科学基金项目1起,主办省部级科研项目5起。杨云博士的研究方向包括:人工智能,机器学习,数据挖掘,模式识别,异常数据处理与分析等,那个代表性研究收获分别发表于《IEEE Transactions on Cybernetics》,《IEEE Transactions on Neural Networks and Learning Systems》,《IEEE Transactions on Knowledge and Data Engineering》,《IEEE Transactions on Systems, Man, and Cybernetics》《IEEE Transactions on Industrial Informatics》《Pattern Recognition》《Information Fusion》相当人工智能领域的著名学术期刊。并且,在国内外权威学术出版社出版著作2管辖,2018年取得云南省自然科学奖二等奖1起(行第二),2019年取得全国高校人工智能与很数量学术创新奖1起(私奖项)。此外,杨云博士进一步把理论研究和实际应用相结合,和多下大型企业进行了全面的产学研合作,研究收获被广泛使用于智能视频安全预警系统,交通十分数据分析系统,面识别系统,多边贸易交易系统,公路养护管理平台的桥梁修护等级评估系统,有关产学研课题积累数据1.4PB,取得直接或间接的经济效益达1800余万元以上,连申请与获准多件发明专利,软件著作权。

教育背景

2006 - 2011,博士

计算机科学(机器学习与数据挖掘)

英国曼彻斯特大学(Manchester University)

2005–2006,硕士(研究型)

信息学(机器学习与数据挖掘)

英国曼彻斯特大学(Manchester University)

2004–2005,硕士(授课型)

尖端计算机科学

英国布里斯托大学(Bristol University)

2001–2004,知识分子

信息技术与电子通信,一等学位.

英国兰卡斯特大学(Lancaster University)

重要研究领域

•人工智能

•机器学习

•数据挖掘

•模式识别

•多媒体数据分析/目录/追寻

•时间序列数据分析

•智能软件工程

•深度上算法研究和使用

•异常数据处理

代表性学术论著

报论文:

1.Yang P., Qi J., Newcombe L., Peng X., Yang Y., Zhao Z.(2020): Examining Data Fusion Techniques for Internet of Things enabled Physical Activity Recognition and Measure: A Systematic Survey, Information Fusion,55:268-280(SCI JCR同区, ESI top报IF:10.716)

2.Yang Y. and Jiang J. (2019): Adaptive Bi-weighting towards Automatic Initialization and Model Selection for HMM-based Hybrid Meta-clustering Ensembles, IEEE Transactions on Cybernetics, 49(5):1657-1668(SCI JCR同区, ESI top报IF: 10.387)

3.Yang P., Yang G., Qi J., Liu J., Wang T., Yang Y., Wang X.(2019):DUAPM: An Effective Dynamic Micro-blogging User Activity Prediction Model towards Cyber-Physical-Social Systems,IEEE Transactions on Industrial Informatics,To Appear (SCI JCR同区IF: 7.377)

4.Lee S., Tseng C., Lin G., Yang Y.*, Yang P., Muhammad K., Pandeye H. (2019): A Dimension-Reduction Based Multilayer Perception Method for Supporting the Medical Decision Making, Pattern Recognition Letter, To Appear (SCI JCR其三区IF: 2.81)

5.Yang P., Liu J., Qi J., Yang Y., Wang Q. and Lv Z.,(2019): Comparison and Modelling of Country-Level Micro-blog User Behaviour and Activity in Cyber-Physical-Social Systems using Weibo and Twitter Data, ACM Transactions on Intelligent Systems and Technology, To Appear (SCI JCR同区IF: 2.861)

6.He Z., Nan F., Li X., Lee S., Yang Y.* (2019): Traffic Sign Recognition by Combining Global and Local Features Based on Semi-supervised Classification, IET Intelligent Transport Systems, To Appear (SCI JCR其三区IF: 2.05)

7.Yang Y., Nan F., Yang P., Meng Q., Xie Y., Zhang D., Muhammad K.(2019): GAN-based Semi-supervised Learning Approach for Clinical Decision Support in Health-IoT Platform, IEEE Access,7:8048-8057.(SCI JCR同区IF: 4.098)

8.Yang Y., Cao L., Liu Q., Yang P.(2019): A Stacked Multi-Granularity Convolution Denoising Auto-Encoder, IEEE Access,7:83888 - 83899.(SCI JCR同区IF: 4.098)

9.Zhang D., Cui M., Yang Y., Yang P., Xie C., Liu D., Yu B., Chen Z.(2019): Knowledge Graph-Based Image Classification Refinement, IEEE Access,7:57678 - 57690(SCI JCR同区IF: 4.098)

10.Yang Y., Liu X., Ye Q., Tao D. (2018): Ensemble Learning based Person Re-Identification with Multiple Feature Representations, Complexity,2018:1-12.(SCI JCR同区IF: 2.591)

11.Qi J., Yang P., Deng Z., Zhao Y., Waraich A., Yang Y., (2018): Examining Sensor-based Physical Activity Recognition and Monitoring for Healthcare Using Internet of Things: A Systematic Review, Journal of Biomedical Informatics,87:138-153.(SCI JCR第二区IF: 2.95)

12.Xie C.,Cai H., Yang Y.*,Jiang L., Yang P.(2018): User Profiling in Elderly Healthcare Services in China: Scalper Detection, IEEE Journal of Biomedical and Health Informatics,22(6):1796 - 1806. (SCI JCR同区IF: 4.217)

13.Li J., Yang Y.*, Wang X., Zhao Z., Li T. (2018): A Novel Parallel Distance Metric-based Approach for Diversified Ranking on Large Graphs, Future Generation Computer Systems,88:79-91.(SCI JCR同区IF: 5.768)

14.Xie C.; Yang P.; Yang Y.* (2018): Open Knowledge Accessing Method in IoT-based Hospital Information System for Medical Record Enrichment, IEEE Access,6(1): 15202-15211(SCI JCR同区IF: 4.098)

15.Lee S.; Xu Z.; Li T.; Yang Y.* (2018): A Novel Bagging C4.5 Algorithm Based on Wrapper Feature Selection for Supporting Wise Clinical Decision Making, Journal of Biomedical Informatics,78:144-155(SCI JCR第二区IF: 2.95)

16.Yang Y. and Jiang J. (2018): Bi-weighted ensemble via HMM-based approaches for temporal data clustering, Pattern Recognition,76:391-403.(SCI JCR同区IF: 5.898)

17.Yang Y., Li Z., Wang W. and Tao D. (2017): An adaptive semi-supervised clustering approach via multiple density-based information, Neurocomputing,257:193-205.(SCI JCR同区IF: 4.072)

18.Yang Y. and Jiang J. (2016): Hybrid sampling-based clustering ensemble with global and local constitutions, IEEE Transactions on Neural Networks and Learning Systems,27(5):952-965.(SCI JCR同区,ESI top报IF: 11.683)

19.Yang Y. and Liu X. (2015): A Robust Semi-supervised Learning Approach via Mixture of Label Information,Pattern Recognition Letters,68:15-21.(SCI JCR同区IF: 2.81)

20.Yang Y. and Jiang J. (2014): HMM-based hybrid meta-clustering ensemble for temporal data, Knowledge-based systems, 56:299-310.(SCI JCR同区IF: 5.101)

21.Yang Y. and Chen K. (2011): Temporal data clustering via weighted clustering ensemble with different representations, IEEE Transactions on Knowledge and Data Engineering, 23(2): 317-320.(SCI JCR同区,CCF引进期刊A接近IF: 3.857)

22.Yang Y. and Chen K. (2011): Time Series Clustering via RPCL Ensemble Networks with Different Representations, IEEE Transactions on Systems, Man, and Cybernetics-Part C,41(2): 190–199.(SCI JCR同区 )

23.代飞,赵文卓,杨云*,莫启,李彤,周华(2018):BPMN 2.0编辑的样式语义和分析,软件学报,29(4):1094-1114(EI,CCF引进中文杂志A接近)

24.杜飞,杨云*,胡媛媛,曹丽娟(2019):深度共享集成网络:同种简单的共享式多层梯度补给方法,软件学报(EI,CCF引进中文杂志A接近)

25.徐继伟,杨云*(2018):合并学习方法:研究综述,云南大学学报(自然科学版),19(6):1082-1092(中文核刊)

26.向鸿鑫,杨云*(2019):不平衡数据挖掘方法综述,计算机工程和使用,55(4):1-16(中文核刊,CCF引进中文杂志C接近)

27.白扬;曹丽娟;胡媛媛;杨云*(2019):根据特征补偿的只目标跟踪算法,计算机工程和规划(中文核刊,CCF引进中文杂志C接近)

国际会议论文:

1.1.Li X., Yang Y.*, Yang P.(2019), Multi-source ensemble transfer approach for medical text auxiliary diagnosis, The 19th IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2019).(EI)

2.Shao W., Yang P., Yang Y*. (2019): Ensemble of Receptive Fields for Training Central-Focused Convolutional Neural Networks, 2019 IEEE International Conference on Industrial Informatics. (EI)

3.Wang X., Qi J., Yang Y., Yang P. (2019): A Survey of Disease Progression Modelling Techniques for Alzheimer's Diseases, 2019 IEEE International Conference on Industrial Informatics. (EI)

4.Xie C., Liu D., Yang Y.*, Yang P., Yu B., Chen Z., Feng Q. and Peng J. (2019): Knowledge Graph based Internet of Things Middleware, 2019 IEEE International Conference on Industrial Informatics. (EI)

5.Qi J., Yang Y., Peng X., Newcombe L., Simpson A., Yang P. (2019): Experimental Analysis of Artificial Neural Networks Performance for Physical Activity Recognition Using Belt and Wristband Devices, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS).(EI)

6.Cao L., Liu Q., Yang Y.* (2019), An Unsupervised Feature Extraction Method based on Multi-granularity Convolution Denoising Autoencoder, 2019 IEEE/ACIS 18th International Conference On computer and Information Science.(EI)

7.Zhao H., Liu Q. and Yang Y.* (2018), Transfer Learning with Ensemble of Multiple Feature Representations, 16th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications.(EI)

8.Jia X., Wang S. and Yang Y.* (2018), Least-Squares Support Vector Machine for Semi-Supervised Multi-Tasking, 16th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications.(EI)

9.Liu Q., Wang J, Zhang D., Yang Y. and Wang N.(2018), Text features extraction based on TF-IDF associating semantic, 2018 4th IEEE International Conference on Computer and Communications (EI)

10.Wang Y., Zhang D., Yuan Y., Liu Q., Yang Y. (2018), Improvement of tf-idf algorithm based on knowledge graph, 16th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications.(EI)

11.Chen F., Chai J.,Ren D., Liu X. and Yang Y.* (2017), Semi-Supervised Distance Metric Learning for Person Re-Identification, 2017 IEEE/ACIS 16th International Conference On computer and Information Science.(EI)

12.Gan J.,Li A, Lei Q., Ren H. and Yang Y.* (2017), K-means based on Active Learning for Support Vector Machine, 2017 IEEE/ACIS 16th International Conference On computer and Information Science.(EI)

13.Zhang D., Wang N., Yuan Y., Wang B. and Yang Y.* (2017). Fuzzy ontology induction in the cognitive model of ontology learning, 2017 IEEE/ACIS 16th International Conference On computer and Information Science, pp. 739-744.(EI)

14.Yang Y. and Chen K. (2010): Unsupervised Learning via Iteratively Constructed Clustering Ensemble. Proceedings of International Joint Conference on Neural Networks (IJCNN'10), Barcelona, Spain.(EI)

15.Yang Y. and Chen K. (2006): An ensemble of competitive learning networks with different representations for temporal data clustering. Proceedings of International Joint Conference on Neural Networks (IJCNN'06), pp. 5759-5766, Vancouver, Canada.(EI)

专著:

1.Yang Y.(2016):Temporal Data Mining via Unsupervised Ensemble Learning, Elsevier, ISBN: 9780128116548.

2.Yang Y. and Chen K. (2007): Combining competitive learning networks of various representations for sequential data clustering, Trends in Neural Computation (Chapter 13, pp. 315-336), Springer,ISBN:3540361219

3.杨云,杜飞显得(2018):深度上实战,清华大学出版社,ISBN: 9787302491026.

授权发明专利和软件著作权:

1.同种基于多特征并的旅客再识别方法(表明专利),201710120986.X,首先作者

2.通用多边贸易交易系统[简称:贸易交易系统]V1.0(软著),2016SR129592,首先作者

3.根据迁移学习技术的脸表情识别工具V1.0(软著), 2018SR613652,首先作者

重要科研项目

1.2019.1-2022.12,多来域集成迁移学习的研究 (61876166),国家自然科学基金面上项目,62万(直接经费),主持人

2.2017.1-2020.12,针对时间序列聚类问题的特征学习和集成学习钻研(61663046),国家自然科学基金地区列,40万(直接经费),主持人

3.2015.1-2017.12,混合式聚类集成算法的研究(61402397),国家自然科学基金青年项目,26万,主持人

4.2015.1-2018.12,支持软件可信演化的故障定位,国家自然科学基金地区列,44万,参与人(行第二)

5.2019.10-2022.9,高维空间小样本材料基因数据挖掘的自适应深度集成学习钻研,云南省科技厅—云南大学“双一流”建设一起基金重大项目,65万,主持人

6.2016.10-2019.9,根据聚类集成技术的时间序列数据挖掘研究(2016FB104),云南省科技厅应用基础研究计划面上项目,10万,主持人

7.2016.10-2019.09,时间序列数据挖掘研究,云南“百名海外高层次人才引进计划”项目,100万,主持人

8.2017.1-2021.12,合并学习钻研和使用, 云南省中青年学术和技术带头人后备人才培养项目,10万,主持人

9.2018.6-2021.6,机器学习研究和那个在看领域的使用, 云南大学青年英才项目,30万,主持人

10.2015.1-2017.1,迭代式非监督集成学习模型,教育部项目留学回国人员科研启动资金,3万,主持人

11.2015.1-2016.1,根据半监督学习算法的非常数据挖掘研究,云南省软件工程重点实验室开放基金面上项目,3万,主持人

12.2012.1-2013.12,SCC-Computing: strategic collaboration with China on high performance computing based on Tianhe-1A,欧共体第七只框架计划项目,44万(欧元),参与人

重要获奖成果

1.2018年云南省自然科学奖二等奖,根据智能学习的系统建模及控制研究,行第二

2.2019年取得全国高校人工智能与很数量学术创新奖1起(私奖项)

重要学术任职

  • 华夏信息经济学会理事;

  • 全国高校人工智能与很数量创新联盟理事;

  • 《Neural Networks》编委

  • 《云南大学学报(自然科学版)》编委

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