钟俊培

基本信息


  

姓 名钟俊培性 别
出生年月1984年3月籍贯广东
民 族汉族政治面貌中共党员
最后学历博士研究生最后学位自然科学博士
技术职称助理教授导师类别博、硕导
行政职务
Email
zhong@junpei.eu
工作单位吴贤铭智能工程学院邮政编码510640
通讯地址吴贤铭智能工程学院
单位电话


个人简介

钟俊培,博士,现任华南理工大学吴贤铭智能工程学院助理教授(特聘研究员)。2010年欧盟“居里夫人”奖获得者,先后参加了三个欧洲项目和一个日本项目,IEEE Member。目前主要研究方向为机器学习、人机交互、智能家居、辅助机器人等。

工作经历


2016.12 – 2019.1

国立产业综合技术研究所 (AIST

日本

 

研究员 (Research Scientist)

2016.3-2016.10

早稻田大学WasedaUniversity

日本

 

初级研究员(Junior Researcher)

2015.5-2015.12

普利茅斯大学(University of   Plymouth)

英国

 

博士后研究员 (Postdoc Research   Assistant)

2014.1-2015.1    

赫特福德大学(University of   Hertfordshire)

英国

 

博士后研究员 (Postdoc   Research Assistant)

2010.7-2013.6

汉堡大学(University of Hamburg)

德国

 

研究助理 (Research   Associate)


教育经历

2010.6-2013.12

汉堡大学(University of Hamburg)

德国

 

研究员 (Research associate)同时攻读博士学位


Artificial Neural Models for Feedback Pathways   for Sensorimotor Integration

(欧盟居里夫人项目RobotDoC) (www.robotdoc.org)

2007.10-2010.4

香港理工大学(The Hong Kong Polytechnic University)

香港

 

哲学硕士(MPhil student)

 

 

Simultaneous localsation and mapping in a multi-robot system.

多机器人系统的同步地图构建和定位

2002.9-2006.7

华南理工大学

广州

 

本科:自动控制,辅修计算机

 

发表论文

期刊

1. J. Zhong, A. Cangelosi, T. Ogata, and Zhang X. Encoding longer-term contextual information with predictive coding and ego-motion (accepted). Complexity, 2018 (SCI, IF=1.829, 2)

2. J. Zhong, M. Peniak, J. Tani, T. Ogata, and A. Cangelosi. Sensorimotor input as a language generalisation tool: A connectionist model for generation and generalisation of noun-verb combinations with sensorimotor inputs. Autonomous Robots, 2018 (SCI, IF=2.244, 3)

3. X. Zhang, J. Zhang, and J. Zhong. Towards navigation ability for autonomous mobile robots with learning from demonstration paradigm: A view of hierarchical temporal memory. International Journal of Advanced Robotic Systems, 2018 (SCI, IF=0.952, 4)

4. Y. Xu, C. Yang, J. Zhong, H. Ma, and L. Zhao. Robot teaching by teleoperation based on visual interaction and extreme learning machine. Neurocomputing, 2017 (SCI, IF=3.241, 2)

5. Y. Jiang, C. Yang, J. Na, G. Li, Y. Li, and J.Zhong. A brief review of neural networks based learning and control and their applications for robots. Complexity, 2017 (SCI, IF=1.829, 2)

6. X. Zhang, J. Zhang, and J. Zhong. Skill learning for intelligent robot by perception-action integration: A view from hierarchical temporal memory. Complexity, 2017 (SCI, IF=1.829, 2)

7. J. Zhong, A. Cangelosi, and S. Wermter. Towards a self-organizing presymbolic neural model representing sensorimotor primitives. Frontiers in Behavioral Neuroscience, 8:22, 2014 (SCI, IF=3.138, 3)

8. J. Zhong, C.Weber, and S.Wermter. A predictive network architecture for a robust and smooth robot docking behavior. Paladyn. Journal of Behavioral Robotics, 3(4):172-180, 2012 (新期刊)

9. J. Zhong and Y. Fung. Case study and proofs of ant colony optimisation improved particle filter algorithm. Control Theory Applications, IET, 6(5):689-697, 15, 2012 (SCI, IF=3.392, 3)

10. J. Zhong, Y. Fung, and M. Dai. A biologically inspired improvement strategy for particle filter: Ant colony optimization assisted particle filter. International Journal of Control, Automation and Systems, 8:519-526, 2010 (SCI, IF=2.338, 3)

会议

1. J. Li, Yang C., Zhong J., and Dai S. Emotion-aroused human behaviors perception using RNNPB. In The 9th International Conference on Modelling, Identification and Control, 2018

 2. J. Zhong and T. Ogata and A. Cangelosi. Encoding longer-term contextual sensorimotor information in a predictive coding model. In IEEE Symposium Series on Computational Intelligence, 2018

3. J. Zhong, A. Cangelosi, X. Zhang, and T. Ogata. Afa-prednet: The action modulation within predictive coding. International Joint Conference on Neural Networks (IJCNN), 2018

4. J. Zhong, A. Cangelosi, and T. Ogata. Toward abstraction from multimodal data: Empirical studies on multiple time-scale recurrent models. In International Joint Conference on Neural Networks (IJCNN), 2017

5. Xingjian Wang, Chenguang Yang, Junpei Zhong, and Rongxin Cui. Teleoperation control for bimanual robots based on RBFNN and wave variable. In Proceedings of the 9th International Conference on Modelling, Identification and Control, 2017 (Best Theory Paper)

6. Y. Xu, C. Yang, J. Zhong, H. Ma, and L. Zhao. Robot teaching by teleoperation based on visual interaction and neural network learning. In Proceedings of the 9th International Conference on Modelling, Identification and Control, 2017

7. J. Zhong, A. Cangelosi, T. Ogata, and C Yang. Understanding natural language sentences with word embedding and multi-modal interaction. Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2017 Joint IEEE International Conferences on, 2017

8. M. Dai, S. Huang, J. Zhong, C. Yang, and S. Yang. Influence of wording noise in text space on transfer learning: an empirical example in Chinese sentiment classification. Proceedings of the 13th International Conference on Natural Computation, 2017

9. J. Zhong, R. Novianto, M. Dai, X. Zhang, and A. Cangelosi. A hierarchical emotion regulated sensorimotor model: Case studies. In The 5th International Conference on Data-Driven Control and Learning Systems, 2016

10. J. Zhong, A. Cangelosi, and T. Ogata. Sentence embeddings with sensorimotor embodiment. In The 34th Annual Conference of the Robotics Society of Japan, 2016

11. J. Zhong and L. Canamero. From continuous affective space to continuous expression space: Non-verbal behaviour recognition and generation. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2014 Joint IEEE International Conferences on, pages 75-80. IEEE, 2014

12. J. Zhong, C. Weber, and S. Wermter. Restricted boltzmann machine with transformation units in a mirror neuron system architecture. In Proceedings of the IROS2011 Workshop on Cognitive Neuroscience Robotics (CNR), pages 23-28, San Francisco, CA, USA, Sep 2011

13. J. Zhong, C. Weber, and S. Wermter. Learning features and predictive transformation encoding based on a horizontal product model. In Artificial Neural Networks and Machine Learning (ICANN) 2012, pages 539-546. Springer, 2012

14. J. Zhong, C. Weber, and S. Wermter. Learning features and transformation encoding based on a generative horizontal product model. In Proceedings of the Sixteenth International Conference on Cognitive and Neural Systems (ICCNS 2012), Boston, MA, USA, May 2012

15. J. Zhong, C. Weber, and S. Wermter. Robot trajectory prediction and recognition based on a computational mirror neurons model.  Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN 2011), volume 2, pages 333-340, Espoo, Finland, June 2011. Springer

16. J. Zhong and Y. Fung. A detailed analysis of the ant colony optimization enhanced particle filters. In Min Zhu, editor, Electrical Engineering and Control, volume 98 of Lecture Notes in Electrical Engineering, pages 641-648. Springer Berlin Heidelberg, 2011

17. S Ren, Y Fung, J Zhong, X Li, and J Bi. Freeway traffic estimation based on improved particle filter. IEEE International Conference on Computer Science and Information Technology, 5:312-317, 6, 2011

18. J. Zhong and Y. Fung. A biological inspired improvement strategy for particle filters. In IEEE International Conference on Industrial Technology, 2009. ICIT 2009. pages1-6, Feb. 2009