ACROSS–DataScience

Znanstveni centar izvrsnosti za znanost o podatcima i kooperativne sustave

Centar ACROSS-DataScience djeluje kao središte suradnje akademske zajednice s poslovnim i javnim sektorom u istraživanju i razvoju novih metodologija i naprednih inženjerskih pristupa za znanost o podatcima i kooperativne sustave.


Poziv na predavanje: New hybrid...

Istraživačka jedinica za znanost o podatcima Centra izvrsnosti za znanost o podatcima i napredne kooperativne sustave, u okviru projekta "DATACROSS – Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima", organizira predavanje istraživačkog seminara

New hybrid system of machine learning and statistical pattern recognition for a 3D visibility network

koje će održati dr. sc. Matej Babič s Fakulteta za informacijske študije, Novo mesto, Slovenija. Predavanje će se održati u srijedu 8. 5. 2019. u 14:00 sati na Institutu Ruđer Bošković, dvorana u krilu Ivan Supek.

Više o predavaču i predavanju možete pročitati u opširnijem sadržaju obavijesti.

Biography: Matej Babič received his Ph. D. degree in Computer Science in 2014 from the Faculty of Electrical Engineering and Computer Science of the University of Maribor, Slovenia. He studied Mathematics at the Faculty of Education in Maribor. He concluded postdoc at Jožef Stefan Institute, Ljubljana, Slovenia. His research interest is in fractal geometry, graph theory, intelligent systems, hybrid machine learning and topography of materials after hardening. Now is employed at Faculty of information studies Novo mesto, Slovenia.

Abstract: Intelligent systems are an excellent tool to use for solving complex problems in the field of industrial applications. We use the mathematical method of fractal geometry and network theory when laser hardening techniques are applied. The microstructure of the robot-laser-hardened specimens is very complex; however, we can present it by using a 3D visibility network. We convert the scanning electron microscope (SEM) images of the microstructure to a 3D graph and calculate the density of the visibility network of these 3D networks. The topographical properties of the hardened specimens are analysed using the algorithm for the construction of a visibility network in a 3D space. We develop a new hybrid system of machine learning for predicting carbide content of the hardened specimens by using multiple regression, neural networks, and a genetic algorithm. We find the statistical significance of the relationship between attributes of the hardened specimens, the topological properties of visibility graphs, and carbide content of the hardened specimens.

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