Thesis supervisor: Attila Gilányi
Location of studies (in Hungarian): University of Debrecen Faculty of Informatics Abbreviation of location of studies: ITDI
Description of the research topic:
The requirements of Industry 4.0 keep gaining ground in today’s medical device manufacturing industry. To adapt to modern challenges demands that research and innovation projects related to virtual and extended reality (VR), computer simulation (CS), and artificial intelligence (AI) play a significant role in industry developments. During the elaboration of the PhD topic, it is possible to deal with the solution of many scientific, research and development tasks that are important from a practical point of view, and to face many challenges.
VR topics may contain the development of a broader sense educational methodology related to industry. Properly developed virtual systems can provide an effective help in case of a complex, multi-layered education/training tasks that require high costs and a rather tight schedule in production conditions. Besides the education/training tasks there emerge possibilities of virtual systems development to support day-to-day operations, industrial technology, process development as well as developments related to health rehabilitation.
Innovations related to computer simulations (CS) are especially important in solving automation challenges. After exploring the relevant development areas of the industry, computer modelling and simulation of technologies and processes can take place. By cooperating together with the experts of the field in the first step we can define the exploration of the technical and natural science background and most important functions and networks with scientific thoroughness. After this, the simulation methodologies applicable in the industry can be optimized, and then the simulations of the problems belonging to this can be developed in practice and at the same time scientifically demanding.
With the help of artificial intelligence (AI), the recognition and control of scrap and technological and process level errors can be developed to a more efficient level in many cases than that of the actual methods. During the technological processes and the various production and operating processes at different levels a notable amount of data is generated whose processing is not yet solved. Recognizing patterns in large data sets and comparing them to the scientific and engineering know-how of technologies and manufacturing can increase the supervision of industry processes to a higher level.
Systems and developments achieved within the framework of the PhD work can be integrated into the research and development processes of the industry in a complex way. As end results on one side theoretical on the other side practical results are born at the most modern, scientific level.
References:
1. Tony Parisi, Learning Virtual Reality, O’Reilly Media, 2015.
2. Steven M. LaValle, Virtual Reality, Cambridge University Press, 2017.
3. William R. Sherman, Alan B. Craig, Understanding Virtual Reality: Interface, Application, and Design, Morgan Kaufmann, 2018.
4. M. M. Woolfson, G. J. Pert, Introduction to Computer Simulation, Oxford University Press, 1999.
5. Santner Thomas J, Williams Brian J, Notz William I, The design and analysis of computer experiments, Springer Verlag, 2003.
6. Martin O. Steinhauser, Computer Simulation in Physics and Engineering, de Gruyter, 2012.
7. Edward N. Zalta (Principal editor), Eric Winsberg (Author), Computer Simulations in Science, Stanford Encyclopedia of Philosophy, Stanford University Press, 2019.
8. Jeff Heaton, Artificial intelligence for humans, Vol. 1 Fundamental algorithms, Heaton Research Inc., 2013.
9. David Brown, Machine Learning for Beginners: A Step-By-Step Guide to Understand Deep Learning, Data Science and Analysis, Basic Software and Algorithms for Artificial Intelligence, Kindle edition, 2019.
10. John D. Kelleher, Brian Mac Namee, Aoife D’Arcy, Fundamentals of machine learning for predictive data analytics, Second edition, Algorithms, worked examples, and case studies, MIT Press, 2020.
Deadline for application: 2022-11-15
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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