Text

Learning and Optimisation

The group aims to explore the synergy between machine learning and optimization to achieve collaborative effects in building highly efficient and smart systems.

Contact

Professor

Ning Xiong

+4621151716

ning.xiong@mdh.se

The methodological research concerns: metaheuristics for learning, data driven learning in optimization, real-time learning, data reduction and feature mining, learning and optimization under uncertainty.

We are also actively engaged in practical applications, to test and apply the new developed methods and algorithms in current challenging scenarios such as industrial or biomedical ones. The interesting application areas include (yet are not limited to) the following:

  • Machine learning and optimization in power devices and power systems
  • Real-time process monitoring (both in industry and health care)
  • Complex data analysis in biofeedback systems
  • Process automation and intelligent control systems
  • Behavior learning and control for autonomous robots
  • Cyber attack identification
  • Software testing

Ongoing research projects

The aim of the project is to develop a new methodology for adaptive, distributed learning and information fusion from evolving data streams, based on the MapReduce paradigm.


Project manager at MDH: Ning Xiong

Main financing: The Swedish Research Council

DIGEST will develop new methods and algorithms to dynamically transform data streams continuously generated from HVDC grid into a smart case base, which will be utilized in real-time analysis of the network leading to fast fault identification and protection of the HVDC grid.


Project manager at MDH: Ning Xiong

Main financing: Vinnova

The objective of RECOG is to develop new technology for innovative brain-training for people with cognitive deficits. The ability to focus our attention on relevant information, maintain and manipulate this information during a short period of time (our Working Memory, WM), is central for human cognition.


Project manager at MDH: Elaine Åstrand

Main financing: The Knowledge Foundation