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KEDRI: 5 Years of Special Cooperation Between 'AUT' and 'PKU'

27 Oct 2015

05/10/2015 – Auckland University of Technology's 'Knowledge Engineering and Discovery Research Institute' (KEDRI) signed an agreement with Key Laboratory of Machine Perception, Peking University, in 2010. This year, the involved parties celebrate five years of cooperation. 

The Knowledge Engineering and Discovery Research Institute (KEDRI) signed an agreement with Key Laboratory of Machine Perception, Peking University, in 2010. This year, the involved parties celebrate five years of cooperation. This broadly covers exchange of educational materials and academic publications; exchange of faculty members for research, consulting and lecturing; exchange of graduate students for study and research; co-supervision of PhD students; applications for joint research projects funded by China and New Zealand. The collaborative project is currently led at AUT by Professor Nikola Kasabov, Chair of Knowledge Engineering in the School of Computer and Information Sciences.

Several other Chinese tertiary education and research institutions have also joined the project over the period, including: China Academy of Sciences Institute for Automation (joined 2009); Shanghai Jiaotong University and Xinjiang University (joined 2010); Zhejiang University (joined 2014).

Specifically, the objectives of a project of coordination between the above partners included the following: 

Development of novel neurocomputing methods and software and hardware technologies for complex spatio-temporal brain data modelling. 

Based on the neurocomputing technologies, development of a novel methodology and prototype system for brain-machine interfaces and neurorehabilitation robotics.

Development of personalised modelling methods and systems for stroke data analysis with respect to risk evaluation and outcome prognosis. 

2015: Photo Professor Kasabov visits KEDRI partners Dr. Wang and Professor Yang, Shanghai. 2015: Photo Professor Kasabov visits KEDRI partners Dr. Wang and Professor Yang, Shanghai.

It follows that the project has been hugely successful, with large contributions made to joint publications, including the following:


2014 - Zhang, W., Yang, J., Jia, W., Kasabov, N., Jia, Z., & Zhou, L. Unsupervised Segmentation Using Cluster Ensembles. 21st International Conference, ICONIP 2014. Kuching, Malaysia, November 3-6,2014. Neural Information Processing, Lecture Notes in Computer Science, Volume 8836, part III, pp 76-84. doi: 10.1007/978-3-319-12643-2_10. 

2014 - Tu, E., Yang, J., Jia, Z., & Kasabov, N. Posterior Distribution Learning (PDL): A Novel Supervised Learning Framework. 21st International Conference, ICONIP 2014. Kuching, Malaysia, November 3-6,2014. Neural Information Processing, Lecture Notes in Computer Science, Volume 8834, part I, pp 86-94. doi: 10.1007/978-3-319-12637-1_11 

2014- Hartono, R., Pears, R., Kasabov, N., & Worner, S. (2014). Extracting Temporal Knowledge from Time Series: A Case Study in Ecological Data. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 4237-4243). Beijing, China: IEEE. doi:10.1109/IJCNN.2014.6889918 

2014 - Tu, E., Kasabov, N., Othman, M., Li, Y., Worner, S., Yang, J., Jia, Z. NeuCube(ST) for Spatio-Temporal Data Predictive Modelling with a Case Study on Ecological Data. In 2014 International Joint Conference on Neural Networks (pp. 638-645). Beijing, China: IEEE. doi:0.1109/IJCNN.2014.6889717 

2014 - Wubuli, A., Zhen-Hong, J., Xi-Zhong, Q., Jie, Y., & Kasabov, N. Medical image enhancement based on shearlet transform and unsharp masking. Journal of Medical Imaging and Health Informatics, 4(5), 814-818. doi:10.1166/jmihi.2014.1326 

2014 - Ling-Ling, L., Zhen-Hong, J., Xi-Zhong, Q., Jie, Y., & Kasabov, N. White matter lesions change detection in MR images based on fuzzy nearness and non-subsampled shear waves. Journal of Medical Imaging and Health Informatics, 4(6), 953-956. doi:10.1166/jmihi.2014.1348 

2014 - Tu, E., Cao, L., Yang, J., & Kasabov, N. (2014). A novel graph-based k-means for nonlinear manifold clustering and representative selection. Neurocomputing. doi:10.1016/j.neucom.2014.05.067


 


Last updated: 27 Oct 2015 1:16pm

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