Adaptive Systems
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filed under:
Adaptive Systems,
Information Engineering Laboratory
Our research is concerned with recognising patterns and detecting events from data, as well as modelling and simulating complex self-organising systems. Techniques we have developed are being applied to the problems of designing adaptive power grids, detection of gas in underground coal mines, structural integrity modelling, optimal sensor placement, and distributed fault detection.
Research
Complex networks and cyber-physical systems
- Graph-theoretic analysis and design of network topology;
- Information-theoretic analysis of distributed systems;
- Modelling/forecasting of dynamical, chaotic and self-organising systems;
- Spatiotemporal coordination;
- System evaluation.
Machine Learning
- Bayesian networks;
- Genetic algorithms;
- Feature selection; clustering and classification;
- Recurrent neural networks;
- Reinforcement learning for multi-agent systems.
Application Areas
Current Projects
- Smart Grids and Intelligent Energy
- Cyber-Security (Immunology Approach to Denial of Service Attacks)
- Fault detection and diagnosis for sensor networks (Subsea gas/liquid separation)
Other possible applications of our research include:
- neural computation: information flow analysis, sensorimotor coordination, pattern recognition and classification of complex spatiotemporal dependencies;
- modern network analysis (scale-free networks, including metabolic networks, gene regulatory networks, signalling networks; brain functional networks); in particular: dynamics, connectivity, closeness/betweenness/centrality, motifs, assortativity, core decomposition, modularity analysis, hub classification, etc.);
- hyperspectral image classification using statistical learning methods.
selected recent publications
- J. Boedecker, O. Obst, N. M. Mayer, and M. Asada. Initialization and Self-Organized Optimization of Recurrent Neural Network Connectivity, HFSP Journal, 3(5), 340–349, 2009.
- Y. Guo, G. Poulton, P. Corke, G. Bishop-Hurley, T. Wark, and D. Swain, Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model, Ecological Modelling, 220(17), 2068-2075, 2009.
- Y. Guo, A. Zeman, and R. Li. A Reinforcement Learning Approach to Setting Multi-Objective Goals for Energy Demand Management, International Journal of Agent Technologies and Systems, 1(2), 55-70, 2009.
- J. Li, G. Poulton, G. James, and Y. Guo. Multiple Energy Resource Agent Coordination Based on Electricity Price, Journal of Distributed Energy Resources, 5(2), 103-120, 2009.
- J. T. Lizier, and M. Prokopenko. Differentiating information transfer and causal effect, European Physical Journal B, 73(4), 605-615, 2010.
- J. T. Lizier, M. Prokopenko, and A. Y. Zomaya. Local information transfer as a spatiotemporal filter for complex systems, Physical Review E 77, 026110, 2008.
- O. Obst. Distributed Fault Detection using a Recurrent Neural Network, In Proceedings of the 8th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2009), 373–374, IEEE Computer Society, April 2009.
- M. Piraveenan, M. Prokopenko, and A. Y. Zomaya. Local Assortativity and Growth of Internet, European Physical Journal B, 70, 275–285, 2009.
- M. Piraveenan, M. Prokopenko, and A. Y. Zomaya. Local assortativeness in scale-free networks, Europhysics Letters, 84, 28002, 2008.
- G. Platt, J. Li, R. Li, G. Poulton, G. James, and J. Wall. Adaptive HVAC Zone Modeling for Sustainable Buildings, Journal of Energy and Buildings, 42(4), 412-421, 2010.
- M. Prokopenko, F. Boschetti, and A. Ryan. An information-theoretic primer on complexity, self-organisation and emergence, Complexity, 15(1), 11-28, Wiley, 2009.
- M. Prokopenko, D. Polani, and M. Chadwick. Stigmergic gene transfer and emergence of universal coding, HFSP Journal, 3(5), 317-327, 2009.
- M. Rubinov, and O. Sporns. Complex network measures of brain connectivity: Uses and interpretations, NeuroImage, DOI: 10.1016/j.neuroimage.2009.10.003, 2010.
- M. Rubinov, O. Sporns, C. van Leeuwen and M. Breakspear, Symbiotic relationship between brain structure and dynamics, BMC Neuroscience, 10:55, 2009.
- X. R. Wang, G. Mathews, D. Price, and M. Prokopenko. Optimising Sensor Layouts for Direct Measurement of Discrete Variables, In Proceedings of The 3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2009), San Francisco, California, September 14-18, 92-102, 2009.
- X. R. Wang, J. Lizier, O. Obst, M. Prokopenko, and P. Wang. Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks. In R. Verdone (ed.), Proceedings of the 5th European Conference on Wireless Sensor Networks (EWSN-2008), January 31 - February 1, 2008, Bologna, Italy, Lecture Notes in Computer Science 4913, 90-105, Springer, 2008.
