dc.contributor.author | Subačiūtė-Žemaitienė, Jurga | |
dc.contributor.author | Dzedzickis, Andrius | |
dc.contributor.author | Zinovičius, Antanas | |
dc.contributor.author | Ivinskij, Vadimas | |
dc.contributor.author | Rožėnė, Justė | |
dc.contributor.author | Bagdonas, Rokas | |
dc.contributor.author | Bučinskas, Vytautas | |
dc.contributor.author | Morkvėnaitė-Vilkončienė, Inga | |
dc.date.accessioned | 2023-09-18T16:34:48Z | |
dc.date.available | 2023-09-18T16:34:48Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/115165 | |
dc.description.abstract | Scanning electrochemical microscopy is an advanced tool for studying electrochemically active surfaces, including biological ones. Experiments with biological systems must be performed fast since their reactions and states change very fast. SECM can be easily equipped with a top-mounted light microscope with a known distance between the probe and the camera. This hardware solution, in combination with machine learning algorithms, would allow for the automatic finding of target locations, selecting exact positions for measurements, and compensating for positioning inaccuracies. This article demonstrates a newly constructed SECM setup. In addition, it allows faster user adaptation to unknown topography and shortened scanning times. | eng |
dc.format | PDF | |
dc.format.extent | p. 155-162 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.ispartofseries | Lecture Notes in Networks and Systems vol. 630 2367-3370 2367-3389 | |
dc.relation.isreferencedby | Scopus | |
dc.title | Scanning electrochemical microscope based on visual recognition and machine learning | |
dc.type | Straipsnis konferencijos darbų leidinyje Scopus DB / Paper in conference publication in Scopus DB | |
dcterms.references | 21 | |
dc.type.pubtype | P1b - Straipsnis konferencijos darbų leidinyje Scopus DB / Article in conference proceedings Scopus DB | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Mechanikos fakultetas / Faculty of Mechanics | |
dc.contributor.faculty | Kūrybiškumo ir inovacijų centras „Linkmenų fabrikas“ / Creativity and Innovation Centre "Linkmenų fabrikas" | |
dc.subject.researchfield | T 009 - Mechanikos inžinerija / Mechanical enginering | |
dc.subject.studydirection | E06 - Mechanikos inžinerija / Mechanical engineering | |
dc.subject.vgtuprioritizedfields | MC0505 - Inovatyvios elektroninės sistemos / Innovative Electronic Systems | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | scanning electrochemical microscopy | |
dc.subject.en | machine learning | |
dc.subject.en | visual recognition | |
dcterms.sourcetitle | Automation 2023: Key challenges in automation, robotics and measurement techniques : conference proceedings | |
dc.publisher.name | Springer | |
dc.publisher.city | Cham | |
dc.identifier.doi | 10.1007/978-3-031-25844-2_14 | |
dc.identifier.elaba | 155013685 | |