MIN Faculty
Department of Informatics
Knowledge Technology

Warning!

This webpage is outdated and has moved. Please find the official Knowledge Technology page at:

https://www.inf.uni-hamburg.de/en/inst/ab/wtm/

Dipl.-Ing. Wenjie Yan

Research Associate of Knowledge Technology Group

Contact Info

Address: University of Hamburg
Department of Informatics
Vogt Koelln Str. 30
22527 Hamburg, Germany
Office: F-229
Email: yan at informatik.uni-hamburg.de


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Projects


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Short Curriculum Vitae

Since May 2010 Research Associate of Knowledge Technology Research Group, Department of Computer Science, University of Hamburg, Germany
November 2009 Master degree in mechatronics engineer (German Diplom-Ingenieur), Karlsruhe Institut of Technology, Germany
June 2006 Bachelor degree in mechanical engineer, University of Shanghai for Science and Technology, China


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Thesis

Nachbildung eines menschlichen Reflexbogens mittels künstlicher Neuronen Modelle
Diploma Thesis, Supervisor: Prof. Dr. Georg Bretthauer, Institut für Angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT), Germany, November 2009
 


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Publications

Albers, A., Yan, W., Frietsch, M. Application of Reinforcement Learning to a two DOF Robot Arm Control. In Katalinic, B., editors, Annals of DAAAM for 2009 & Proceedings of the 20th International DAAAM Symposium, Volume 20, No. 1, ISSN 1726-9679, Vienna, Austria, 2009.
 


Bauer, C., Milighetti, G., Yan, W., Mikut, R., Human-like Reflexes for Robotic Manipulation using Leaky Integrate-and-Fire Neurons. The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, October 2010.


Yan, W., Weber, C., Wermter, S. Person tracking based on a hybrid neural probabilistic model. In Honkela, T., Duch, W., Girolami, M., Kaski, S., editors, Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN 2011), Part II, pp. 365-372, Espoo, Finland, June 2011.
 


Yan, W., Weber, C., Wermter, S. A hybrid probabilistic neural model for person tracking based on a ceiling-mounted camera. Ambient Intelligence and Smart Environments, Vol. 3(3), pp. 237-252, 2011.
 


Yan, W., Weber, C., Wermter, S. A Neural Approach for Robot Navigation based on Cognitive Map Learning. 2012 International Joint Conference on Neural Networks (IJCNN2012), pp. 1146-1153, Brisbane, Australia, 2012. IEEE
 


Yan, W. Robot navigation for assisted living. Gerontechnology, 11, p. 356, 2012.
 


Yan, W., Torta, E., van der Pol, D., Meins, N., Weber, C., Cuipers, R.H., Wermter, S. Learning Robot Vision for Assisted Living. In Garcia-Rodriguez, J., Cazorla, M., editors, Robotic Vision: Technologies for Machine Leaning and Vision Applications, ch. 15, pp. 257-280, IGI Global, 2013.

 

Yan, W., Weber, C., Wermter, S. Learning indoor robot navigation using visual and sensorimotor map information. Frontiers in Neurorobotics, Vol. 7(15), 2013. http://www.frontiersin.org/neurorobotics/10.3389/fnbot.2013.00015/abstract
 


Johnson, D.O., Cuijpers, R., Juola, J.F., Torta, E., Simonov, M., Frisiello, A., Bazzani, M., Yan, W., Weber, C., Wermter, S., Meins, N., Oberzaucher, J., Panek, P., Edelmayer, G., Mayer, P., Beck. C., Socially Assistive Robots: A comprehensive approach to extending independent living. International Journal of Social Robotics, 2013. http://link.springer.com/article/10.1007%2Fs12369-013-0217-8