| Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: A conceptual framework I Baturynska, O Semeniuta, K Martinsen Procedia Cirp 67, 227-232, 2018 | 173 | 2018 |
| Analysis of camera calibration with respect to measurement accuracy O Semeniuta Procedia Cirp 41, 765-770, 2016 | 51 | 2016 |
| Towards increased intelligence and automatic improvement in industrial vision systems O Semeniuta, S Dransfeld, K Martinsen, P Falkman Procedia cirp 67, 256-261, 2018 | 43 | 2018 |
| Vision-based robotic system for picking and inspection of small automotive components O Semeniuta, S Dransfeld, P Falkman 2016 IEEE International Conference on Automation Science and Engineering …, 2016 | 32 | 2016 |
| Deburring using robot manipulators: A review IF Onstein, O Semeniuta, M Bjerkeng 2020 3rd international symposium on small-scale intelligent manufacturing …, 2020 | 31 | 2020 |
| Application of machine learning methods to improve dimensional accuracy in additive manufacturing I Baturynska, O Semeniuta, K Wang Advanced manufacturing and automation VIII 8, 245-252, 2019 | 27 | 2019 |
| MEML: Resource-aware MQTT-based machine learning for network attacks detection on IoT edge devices A Shalaginov, O Semeniuta, M Alazab Proceedings of the 12th IEEE/ACM International Conference on Utility and …, 2019 | 26 | 2019 |
| Tolerancing from STL data: a legacy challenge TL Leirmo, O Semeniuta, K Martinsen Procedia Cirp 92, 218-223, 2020 | 12 | 2020 |
| Investigating the Dimensional and Geometric Accuracy of Laser-Based Powder Bed Fusion of PA2200 (PA12): Experiment Design and Execution TL Leirmo, O Semeniuta Applied Sciences 11 (5), 2031, 2021 | 9 | 2021 |
| Subset-based stereo calibration method optimizing triangulation accuracy O Semeniuta PeerJ Computer Science 7, e485, 2021 | 5 | 2021 |
| EPypes: a framework for building event-driven data processing pipelines O Semeniuta, P Falkman PeerJ Computer Science 5, e176, 2019 | 5 | 2019 |
| Flexible image acquisition service for distributed robotic systems O Semeniuta, P Falkman 2018 Second IEEE International Conference on Robotic Computing (IRC), 106-112, 2018 | 5 | 2018 |
| Cascading trade‐off studies for robotic deburring systems IF Onstein, C Haskins, O Semeniuta Systems Engineering 25 (5), 475-488, 2022 | 4 | 2022 |
| Minimizing form errors in additive manufacturing with part build orientation: An optimization method for continuous solution spaces TL Leirmo, O Semeniuta Open Engineering 12 (1), 227-244, 2022 | 3 | 2022 |
| Extracting shape features from a surface mesh using geometric reasoning TL Leirmo, O Semeniuta, I Baturynska, K Martinsen Procedia CIRP 93, 544-549, 2020 | 3 | 2020 |
| Event-driven industrial robot control architecture for the Adept V+ platform O Semeniuta, P Falkman PeerJ Computer Science 5, e207, 2019 | 3 | 2019 |
| Discrete event dataflow as a formal approach to specification of industrial vision systems O Semeniuta, P Falkman 2015 IEEE International Conference on Automation Science and Engineering …, 2015 | 3 | 2015 |
| Flexible Composition of Robot Logic with Computer Vision Services O Semeniuta PQDT-Global, 2018 | | 2018 |
| Calibration of robot vision systems for flexible assembly O Semeniuta Høgskolen i Gjøvik, 2014 | | 2014 |
| Розробка засобів оптимізації схем водоспоживання на основі моделей у неявному вигляді ОО Квітка, АМ Шахновський, ОМ Семенюта НТУУ «КПІ», 2012 | | 2012 |
Kristian MartinsenNTNU在 ntnu.no 的电子邮件经过验证
