Artificial Intelligence

The aim of the Artificial Intelligence Department at the IMI is to create and study intelligent human-centered systems in reality-like environments. 

Forschungsfelder

We perform fundamental research on theory and algorithms for complex interacting systems, and apply our results to design smart systems and to understand processes in diverse fields such as advanced manufacturing, materials science, biology, neuroscience, or politics. We develop cutting-edge novel methods for Artificial Intelligence, including tools for statistical learning, unsupervised machine learning, data science, and applied mathematics. 
Examples of applications of our research include the new generation of Smart Energy Networks in Europe, the Internet of Things, AI for advanced manufacturing, systems of autonomous robots, Cyber-Physical Systems, Deep Learning, systems in virtual and augmented reality, and automated analysis of social, industrial, and political networks.

We demonstrate the positive impact of our results on economy and society by actively supporting the transfer of our innovations and technologies to practice. We have a number of industry partnerships, and collaborations with businesses and organizations, both national and global. 
 

Laufende Projekte
Titel Kurzbeschreibung Ansprechpartner

Deutsch-Chinesische Industrie 4.0 Fabrikautomatisierungsplattform

Unified demand response interoperability framework enabling market participation of active energy consumers

Publikationen


2022
Wind turbine failure prediction and health assessment based on adaptive maximum mean discrepancy.
Peng, J.; Kimmig, A.; Niu, Z.; Wang, J.; Liu, X.; Wang, D.; Ovtcharova, J.
2022. International journal of electrical power & energy systems, 134, 107391. doi:10.1016/j.ijepes.2021.107391
2021
Dual-stage attention-based long-short-term memory neural networks for energy demand prediction.
Peng, J.; Kimmig, A.; Wang, J.; Liu, X.; Niu, Z.; Ovtcharova, J.
2021. Energy and buildings, 249, Art.-Nr.: 111211. doi:10.1016/j.enbuild.2021.111211
Human-Centered Referential Process Models for AI Application.
Elstermann, M.; Bönsch, J.; Kimmig, A.; Ovtcharova, J.
2021. Human Centred Intelligent Systems: Proceedings of KES-HCIS 2021 Conference. Ed.: A. Zimmermann, 56–65, Springer. doi:10.1007/978-981-16-3264-8_6
A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework.
Peng, J.; Kimmig, A.; Niu, Z.; Wang, J.; Liu, X.; Ovtcharova, J.
2021. Applied Energy, 299, Art.-Nr.: 117321. doi:10.1016/j.apenergy.2021.117321
2020
The Research of Flexible Scheduling of Workshop Based on Artificial Fish Swarm Algorithm and Knowledge Mining.
Peng, J.; Wang, J.; Wang, D.; Kimmig, A.; Ovtcharova, J.
2020. Advances in Swarm Intelligence – 11th International Conference, ICSI 2020, Belgrade, Serbia, July 14–20, 2020, Proceedings. Ed.: Y. Tan, 104–116, Springer International Publishing. doi:10.1007/978-3-030-53956-6_10
2019
Methodical approach for the development of a platform for the configuration and operation of turnkey production systems.
Gönnheimer, P.; Kimmig, A.; Mandel, C.; Stürmlinger, T.; Yang, S.; Schade, F.; Ehrmann, C.; Klee, B.; Behrendt, M.; Schlechtendahl, J.; Fischer, M.; Trautmann, K.; Fleischer, J.; Lanza, G.; Ovtcharova, J.; Becker, J.; Albers, A.
2019. Procedia CIRP, 84, 880–885. doi:10.1016/j.procir.2019.04.260