- Position:
- Researcher
PhD student - Department:
- Department of Information Engineering and Process Control (DIEPC)
- Room:
- NB 647
- eMail:
- Home page:
- https://www.uiam.sk/~mojto
- Phone:
- +421 259 325 349
- ORCID iD:
- 0000-0002-6114-2710
- WoS ResearcherID:
- AAZ-3608-2020
- Google Scholar:
- sjBbi0AAAAAJ
- Availability:
Citations
- Total citations 5
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Support Vector Machine-based Design of Multi-model Inferential Sensors. Editor(s): Ludovic Montastruc, Stephane Negny, In 32nd European Symposium on Computer Aided Process Engineering, Elsevier, no. 1, vol. 32, pp. 1045–1050, 2022.
- Number of citations 1
- Kappatou, Chrysoula D. – Odgers, James – García-Muñoz, Salvador – Misener, Ruth: An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics. Industrial and Engineering Chemistry Research, 2022.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Data-based design of inferential sensors for petrochemical industry. Computers & Chemical Engineering, vol. 153, pp. 107437, 2021. arXiv
- Number of citations 3
- Sanseverinatti, Carlos I. – Perdomo, Mariano M. – Clementi, Luis A. – Vega, Jorge R.: An Adaptive Soft Sensor for On-Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process. Macromolecular Reaction Engineering, 2023.
- Ikonen, Teemu J. – Bergman, Samuli – Corona, Francesco: A Bayesian inferential sensor for predicting the reactant concentration in an exothermic chemical process. Chemometrics and Intelligent Laboratory Systems, vol. 241, pp. 104942, 2023.
- Dai, Siyang – Cao, Deshun – Li, Na – Guo, Yian – Wang, Hao: Multiphysics modeling and experimental analysis of corrosion-assisted degradation in industrial pressure transducer packages under thermomechanical fatigue. Materials Chemistry and Physics, no. 129811, vol. 326, 2024.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Data-based Industrial Soft-sensor Design via Optimal Subset Selection. Editor(s): Metin Türkay, Rafiqul Gani, In 31st European Symposium on Computer Aided Process Engineering, Elsevier, vol. 31, pp. 1247–1252, 2021.
- Number of citations 1
- Shan, Baoming – Ma, Cuncheng – Niu, Chengqun – Xu, Qilei – Zhu, Zhaoyou – Wang, Yinglong – Zhang, Fangkun: Soft sensor model predictive control for azeotropic distillation of the separation of DIPE/IPA/water mixture. Journal of the Taiwan Institute of Chemical Engineers, vol. 152, 2023.
