Thesis supervisor: Zoltán Süle
co-supervisor: János Abonyi
Location of studies (in Hungarian): University of Pannonia, Veszprém, Egyetem str. 10. Abbreviation of location of studies: PE
Description of the research topic:
The research aims to integrate the tools of data science, process simulation, and machine learning, taking the industry 4.0 approach into account in order to develop solutions suitable for increasing the efficiency of production systems.
Depending on the applicant's interests and potential field of application, the research may focus on the following tasks:
• development of solutions to support the work of operators, i.e., development of Operator 4.0 strategies;
• development of solutions to improve overall asset utilization and/or energy efficiency;
• development of solutions to support production-related logistics processes;
• development of solutions to monitor and assess processes;
• development of self-organizing production systems;
• support for quality control and quality improvement processes.
The research work needs to focus on the development and application of the following topics:
• machine-learning algorithms (neural networks, deep learning algorithms, event analysis);
• optimization strategies and methodologies (mixed-integer linear and non-linear optimization techniques);
• procedures of state estimation of complex systems and system identification toolkit;
• elements of artificial intelligence;
• process mining algorithms;
• process simulation tools;
• process and production informatics solutions (standards and ontologies);
• IoT toolbar;
• machine vision, pattern recognition algorithms.
The preliminaries of the research topic can be found in the following papers of the supervisors:
[1] Sule Z, Baumgartner J, Dorgo G, Abonyi J, P-graph-based multi-objective risk analysis and redundancy allocation in safety-critical energy systems, Energy 179, 989-1003. (2019)
[2] Baumgartner J, Sule Z, Bertok B, Abonyi J, Test-sequence optimisation by survival analysis, Central European Journal of Operations Research 27, 357-375. (2019)
[3] Ruppert T, Dorgo G, Abonyi J, Fuzzy activity time-based model predictive control of open-station assembly lines, Journal of Manufacturing Systems 54, 12-23. (2020)
[4] Honti GM, Abonyi J, A review of semantic sensor technologies in internet of things architectures, Complexity, 1-21. (2019)
Our further results: Related R&D projects of the University of Pannonia, related research of the Department of Computer Science and Systems Technology and Department of Process Engineering, results of the Complex systems monitoring research group.
Number of students who can be accepted: 1
Deadline for application: 2020-09-30
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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