1. Research Area
The focus of research is on AI (Artificial Intelligence) and its application to real-world problems. Specific topics of interests include:
- Machine learning and data mining with applications to smart factory development
- Intelligent scheduling and optimization using evolutionary algorithms
- Decision making and reasoning under uncertainty
2. Research Overview
The following smart factory projects apply machine learning techniques to the real-time process data collected via an IoT infrastructure. The models learned are used for predicting defect or for building a simulator for production schedule simulations.
3. Research Achievements
- J. H. Hong and K. R. Ryu, Simulation-based multimodal optimization of decoy system design using an archived noise-tolerant genetic algorithm, Engineering Applications of Artificial Intelligence, 65: 230~239, 2017
- R. Choe, J. Kim, and K. R. Ryu, Online preference learning for adaptive dispatching of AGVs in an automated container terminal, Applied Soft Computing, 38: 647~660, 2016
- T. Park and K. R. Ryu, A Dual-Population Genetic Algorithm for Adaptive Diversity Control, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 6, pp. 865~884, Dec. 2010.
- Scheduling Optimization System and Method in Hot Press Forging Process (2020)