Ústav informatizácie, automatizácie a matematiky

doc. Ing. Radoslav Paulen, PhD.

Pracovné zaradenie:
Zástupca vedúceho oddelenia
Pedagogický pracovník
Oddelenie:
Oddelenie informatizácie a riadenia procesov (OIaRP)
Miestnosť:
NB 695
eMail:
Webová stránka:
https://www.uiam.sk/~paulen
Telefón:
+421 259 325 730
Skype:
radopaulen
ORCID iD:
0000-0002-1599-2634
WoS ResearcherID:
L-7817-2016
Google Scholar:
JBx2SewAAAAJ
Vedecká činnosť:
Výskumná skupina doc. Paulena sa zameriava na výskum v oblasti odhadu a optimálneho riadenia nelineárnych dynamických systémov s dôrazom na aplikácie v chemickom a biochemickom priemysle.
Hlavnými výskumnými témami sú:
  • garantovaná a štatistická identifikácia systémov a odhad parametrov a stavov
  • dynamická optimalizácia, globálna optimalizácia, prediktívne riadenie
Dostupnosť:

Citácie

  • Celkový počet citácií       506
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Support Vector Machine-based Design of Multi-model Inferential Sensors. Editor(i): Ludovic Montastruc, Stephane Negny, V 32nd European Symposium on Computer Aided Process Engineering, Elsevier, č. 1, zv. 32, str. 1045–1050, 2022.
  • Počet citácií       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.
C. E. Valero – R. Paulen: Set-membership State Estimation for a Continuous Stirred-Tank Reactor. V 9th International Conference on Systems and Control, 2021.
  • Počet citácií       1
  • Orihuela, Luis: Comparison of the Guaranteed State Estimator and the Zonotopic Kalman Filter for Linear Time-Variant Systems. IEEE Control Systems Letters, zv. 7, str. 1512-1517, 2023.
A. R. Gottu Mukkula – M. Mateáš – M. Fikar – R. Paulen: Robust multi-stage model-based design of optimal experiments for nonlinear estimation. Computers & Chemical Engineering, zv. 155, str. 107499, 2021.   arXiv
  • Počet citácií       6
  • Fleitmann,L. – Pyschik,J. – Wolff,L. – Schilling,J. – Bardow,A.: Optimal experimental design of physical property measurements for optimal chemical process simulations. Fluid Phase Equilibria, zv. 557, str. 113420, 2022.
  • Lu, Q.: Molecular structure recognition by blob detection. RSC Advances, č. 57, zv. 11, str. 35879-35886, 2021.
  • Wang,J. – Dowling,A. W.: Pyomo.DOE: An open-source package for model-based design of experiments in Python. AIChE Journal, 2022.
  • Huang, Chunbing – Cattani, Federica – Galvanin, Federico: An optimal experimental design strategy for improving parameter estimation in stochastic models. Computers & Chemical Engineering, zv. 170, str. 108133, 2023.
  • Carlos {de la Calle-Arroyo} – Mariano Amo-Salas – Jesús López-Fidalgo – Licesio J. Rodríguez-Aragón – Weng Kee Wong: A methodology to D-augment experimental designs. Chemometrics and Intelligent Laboratory Systems, zv. 237, str. 104822, 2023.
  • Tian, H. – Cohen, R.Z. – Zhang, C. – Mei, Y.: Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation. Journal of Applied Statistics, č. 14, zv. 50, str. 2951-2969, 2023.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Data-based design of inferential sensors for petrochemical industry. Computers & Chemical Engineering, zv. 153, str. 107437, 2021.   arXiv
  • Počet citácií       2
  • 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, zv. 241, str. 104942, 2023.
M. Mojto – K. Ľubušký – M. Fikar – R. Paulen: Data-based Industrial Soft-sensor Design via Optimal Subset Selection. Editor(i): Metin Türkay, Rafiqul Gani, V 31st European Symposium on Computer Aided Process Engineering, Elsevier, zv. 31, str. 1247–1252, 2021.
  • Počet citácií       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, zv. 152, 2023.
S. Subramanian – S. Lucia – R. Paulen – S. Engell: Tube-enhanced multi-stage model predictive control for flexible robust control of constrained linear systems with additive and parametric uncertainties. International Journal of Robust and Nonlinear Control, č. 9, zv. 31, str. 4458–4487, 2021.
  • Počet citácií       13
  • Alvarado,I. – Krupa,P. – Limon,D. – Alamo,T.: Tractable robust MPC design based on nominal predictions. Journal of Process Control, zv. 111, str. 75-85, 2022.
  • Shi, Y. – Zhang, K.: Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives. Annual Reviews in Control, zv. 52, str. 170-196, 2021.
  • Mowbray, M. – Petsagkourakis, P. – del Rio-Chanona, E.A. – Zhang, D.: Safe chance constrained reinforcement learning for batch process control. Computers and Chemical Engineering, zv. 157, str. 107630, 2022.
  • Farajzadeh Devin,M. G. – Hosseini Sani,S. K.: Two-loop robust model predictive control with improved tube for industrial applications. International Journal of Systems Science, 2022.
  • Parihar, Sushma – Shah, Pritesh – Sekhar, Ravi – Lagoo, Jui: Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review. Applied System Innovation, č. 6, zv. 5, str. 118, 2022.
  • Bastos,G., Jr. – Franco,E.: Dynamic tube model predictive control for a class of soft manipulators with fluidic actuation. International Journal of Robust and Nonlinear Control, 2023.
  • Schwenkel, Lukas – Koehler, Johannes – Mueller, Matthias A. A. – Allgoewer, Frank: Model Predictive Control for Linear Uncertain Systems Using Integral Quadratic Constraints. IEEE Transactions on Automatic Control, č. 1, zv. 68, str. 355-368, 2023.
  • Sebghati, Ashkan – Esfahani, Mahyar Madani – Shamaghdari, Saeed: On the design of efficient optimal tube-based robust model predictive control: Quasi-H∞ approach. Iet Control Theory and Applications, č. 12, zv. 17, str. 1703-1719, 2023.
  • Bjorvand, Simen – Jaschke, Johannes: Improving Primal Decomposition for Multistage MPC Using an Extended Newton Method. IEEE Control Systems Letters, zv. 7, str. 2677-2682, 2023.
  • Zhang, Qiang – Liu, Ping – Chen, Yu – Deng, Quan – Tong, Angxin: Disturbance Observer-Based Terminal Sliding Mode Tracking Control for a Class of Nonlinear SISO Systems with Input Saturation. Processes, č. 7, zv. 11, 2023.
  • Li, Yang – Vilathgamuwa, D. Mahinda – Quevedo, Daniel E. – Lee, Chih Feng – Zou, Changfu: Ensemble Nonlinear Model Predictive Control for Residential Solar Battery Energy Management. IEEE Transactions on Control Systems Technology, č. 5, zv. 31, str. 2188-2200, 2023.
  • Janatian, Nima – Sharma, Roshan: A robust model predictive control with constraint modification for gas lift allocation optimization. Journal of Process Control, zv. 128, str. 102996, 2023.
  • Zhou, Chengyu – Jia, Li – Zhou, Yang: Tube-based batch model predictive control for polystyrene polymerization reaction process. Asia-pacific Journal of Chemical Engineering, č. e2906, 2023.
K. Kusumo – L. Gomoescu – R. Paulen – S. García Muñoz – C. C. Pantelides – N. Shah – B. Chachuat: Nested Sampling Strategy for Bayesian Design Space Characterization. Editor(i): Sauro Pierucci, Flavio Manenti, Giulia Luisa Bozzano, Davide Manca, V 30th European Symposium on Computer Aided Process Engineering, Elsevier, zv. 30, str. 1957–1962, 2020.
  • Počet citácií       4
  • Kino-Oka,M. – Hayashi,Y. – A. Udugama,I. – Hirono,K. – Sugiyama,H.: A Dynamic and Probabilistic Design Space Determination Method for Mesenchymal Stem Cell Cultivation Processes. Industrial and Engineering Chemistry Research, č. 20, zv. 61, str. 7009 - 7019, 2022.
  • Kaiya,Y. – Tamura,R. – Tsuda,K.: Understanding Chemical Processes with Entropic Sampling. Organic Process Research and Development, č. 12, zv. 26, str. 3276-3282, 2022.
  • Udugama, Isuru A. – Badr, Sara – Hirono, Keita – Scholz, Benedikt X. – Hayashi, Yusuke – Kino-oka, Masahiro – Sugiyama, Hirokazu: The role of process systems engineering in applying quality by design (QbD) in mesenchymal stem cell production. Computers & Chemical Engineering, zv. 172, str. 108144, 2023.
  • Thomas Oberleitner – Thomas Zahel – Christoph Herwig: A Method for Finding a Design Space as Linear Combinations of Parameter Ranges for Biopharmaceutical Development. V 33rd European Symposium on Computer Aided Process Engineering, str. 909-914, 2023.
C. E. Valero – M. Villanueva – B. Houska – R. Paulen: Set-Based State Estimation: A Polytopic Approach. Editor(i): Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, V Preprints of the 21st IFAC World Congress (Virtual), Berlin, Germany, July 12-17, 2020, zv. 21, str. 11428–11433, 2020.     arXiv
  • Počet citácií       3
  • Mu, Bowen – Yang, Xuejiao – Shen, Kai – Scott, Joseph K.: Improved Interval Reachability Bounds for Nonlinear Discrete-Time Systems using an Efficient One-Dimensional Partitioning Method. V 2021 American Control Conference (ACC), str. 3664-3669, 2021.
  • Ragot, José: Guaranteed consistency between measurements and parameter systems with correlated additive and multiplicative uncertainties. Complex Engineering Systems, zv. 2, str. 10, 2022.
  • Dawoud, Mohammed M. – Liu, Changxin – Alanwar, Amr – Johansson, Karl H.: Differentially Private Set-Based Estimation Using Zonotopes. V 2023 European Control Conference, ECC, 2023.
S. Thangavel – R. Paulen – S. Engell: Dual multi-stage NMPC using sigma point principles. Editor(i): Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, V Preprints of the 21st IFAC World Congress (Virtual), Berlin, Germany, July 12-17, 2020, zv. 21, str. 11394–11401, 2020.     arXiv
  • Počet citácií       1
  • Polcz, P. – Csutak, B. – Szederkényi, G.: Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control. Applied Sciences (Switzerland), č. 3, zv. 12, str. 1113, 2022.
S. Thangavel – R. Paulen – S. Engell: Multi-stage NMPC using sigma point principles. V 6th Conference on Advances in Control and Optimization of Dynamical Systems ACODS 2020, Elsevier, zv. 53, str. 386–391, 2020.     arXiv
  • Počet citácií       2
  • Panda, Atanu – Thirunavukarasu, Narayani – Panda, Rames C.: Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault. Proceedings of the Institution of Mechanical Engineers Part I-journal of Systems and Control Engineering, 2023.
  • Landgraf, Daniel – Voelz, Andreas – Berkel, Felix – Schmidt, Kevin – Specker, Thomas – Graichen, Knut: Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control. Annual Reviews in Control, č. 100905, zv. 56, 2023.
S. Thangavel – R. Paulen – S. Engell: Robust Multi-Stage Nonlinear Model Predictive Control Using Sigma Points. Processes, č. 7, zv. 8, str. 0851, 2020.
  • Počet citácií       5
  • Propson,T. – Jackson,B. E. – Koch,J. – Manchester,Z. – Schuster,D. I.: Robust Quantum Optimal Control with Trajectory Optimization. Physical Review Applied, č. 1, zv. 17, str. 014036, 2022.
  • Polcz, P. – Csutak, B. – Szederkényi, G.: Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control. Applied Sciences (Switzerland), č. 3, zv. 12, str. 1113, 2022.
  • Soloperto, Raffaele – Muller, Matthias A. – Allgower, Frank: Guaranteed Closed-Loop Learning in Model Predictive Control. IEEE Transactions on Automatic Control, č. 2, zv. 68, str. 991-1006, 2023.
  • Casas, Carlos Andres Elorza – Valipour, Mahshad – Sandoval, Luis A. Ricardez: Multi-scenario and multi-stage robust NMPC with state estimation application on the Tennessee-Eastman process. Control Engineering Practice, zv. 139, str. 105635, 2023.
  • Kaplan, Ido – Erew, Muhammad – Piasetzky, Yonatan – Goldstein, Moshe – Oz, Yaron – Suchowski, Haim: Segmented composite design of robust single-qubit quantum gates. Physical Review A, zv. 108, str. 042401, 2023.
S. Thangavel – R. Paulen – S. Engell: Adaptive multi-stage NMPC using sigma point principles. V European Control Conference 2020, str. 196–201, 2020.
  • Počet citácií       3
  • Bonzanini, A.D. – Paulson, J.A. – Makrygiorgos, G. – Mesbah, A.: Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks. Computers and Chemical Engineering, str. 107174, 2021.
  • Meng,F. – Shen,X. – Karimi,H. R.: Emerging methodologies in stability and optimization problems of learning-based nonlinear model predictive control: A survey. International Journal of Circuit Theory and Applications, č. 11, zv. 50, str. 4146-4170, 2022.
  • Landgraf, Daniel – Voelz, Andreas – Berkel, Felix – Schmidt, Kevin – Specker, Thomas – Graichen, Knut: Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control. Annual Reviews in Control, č. 100905, zv. 56, 2023.
K. Kusumo – L. Gomoescu – R. Paulen – S. García Muñoz – C. C. Pantelides – N. Shah – B. Chachuat: Bayesian Approach to Probabilistic Design Space Characterization: A Nested Sampling Strategy. Industrial & Engineering Chemistry Research, č. 6, zv. 59, str. 2396–2408, 2020.
  • Počet citácií       10
  • Vandercammen, A. – Dessoy, S. – Sanders, M. – Pysik, A. – Geldhof, G. – Schenkendorf, R. – von Stosch, M. – Mariti, M. – Varsakelis, C.: Working within the design space: Do our static process characterization methods suffice?. Pharmaceutics, č. 6, zv. 12, str. 1-15, 2020.
  • Granados-Ortiz, F.-J. – Ortega-Casanova, J.: Machine learning-aided design optimization of a mechanical micromixer. Physics of Fluids, č. 6, zv. 33, str. 063604, 2021.
  • Demis, P. – Kucherenko, S. – Klymenko, O.V.: Design Space Approximation with Gaussian Processes. Computer Aided Chemical Engineering, zv. 50, str. 905-911, 2021.
  • Jiang,S. -. – Papageorgiou,L. G. – Bogle,I. D. L. – Charitopoulos,V. M.: Investigating the Trade-Off between Design and Operational Flexibility in Continuous Manufacturing of Pharmaceutical Tablets: A Case Study of the Fluid Bed Dryer. Processes, č. 3, zv. 10, str. 454, 2022.
  • Destro,F. – Barolo,M.: A review on the modernization of pharmaceutical development and manufacturing – Trends, perspectives, and the role of mathematical modeling. International journal of pharmaceutics, zv. 620, str. 121715, 2022.
  • Destro, Francesco – Barolo, Massimiliano: A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling. INTERNATIONAL JOURNAL OF PHARMACEUTICS, zv. 620, str. 121715, 2022.
  • Peterson, John J.: Response surfaces, blocking, and split plots: A predictive distribution case study. Quality Engineering, č. 1, zv. 35, str. 172-191, 2023.
  • Saadeh,Q. – Naujok,P. – Wu,M. – Philipsen,V. – Thakare,D. – Scholze,F. – Buchholz,C. – Stadelhoff,C. – Wiesner,T. – Soltwisch,V.: Nested Sampling aided determination of tantalum optical constants in the EUV spectral range. Applied Optics, č. 33, zv. 61, str. 10032-10042, 2022.
  • Huayu Tian – Jnana Sai Jagana – Qi Zhang – Marianthi Ierapetritou: Feasibility/Flexibility-based optimization for process design and operations. Computers & Chemical Engineering, zv. 180, str. 108461, 2024.
  • Steven Sachio – Cleo Kontoravdi – Maria M. Papathanasiou: A model-based approach towards accelerated process development: A case study on chromatography. Chemical Engineering Research and Design, zv. 197, str. 800-820, 2023.
A. R. Gottu Mukkula – R. Paulen: Optimal experiment design in nonlinear parameter estimation with exact confidence regions. Journal of Process Control, zv. 83, str. 187–195, 2019.
  • Počet citácií       3
  • Baltodano, Eleaneth – Hanley, Georgia – Vargas, Rolando – Ramírez, Nils – Castillo, Luis: Design of Experiments Assessment for the Determination of Moisture Content in Five Herbal Raw Materials Contained in Tea Products. Borneo Journal of Pharmacy, č. 1, zv. 3, 2020.
  • Himmelsbach, M. – Kroll, A.: On Optimal Test Signal Design and Parameter Identification Schemes for Dynamic Takagi-Sugeno Fuzzy Models Using the Fisher Information Matrix. International Journal of Fuzzy Systems, 2021.
  • Reddy, R.S. – Arepally, D. – Datta, A.K.: Inverse problems in food engineering: A review. Journal of Food Engineering, zv. 319, str. 110909, 2022.
R. Paulen – M. Fikar: Dynamic real-time optimization of batch processes using Pontryagin’s minimum principle and set-membership adaptation. Computers & Chemical Engineering, zv. 128, str. 488–495, 2019.
  • Počet citácií       3
  • Zhang,L. – Li,P. – Chen,L. – Xia,S. – Kong,R. – Ge,Y. – Feng,H.: Entropy generation rate minimization for steam methane reforming reactor heated by molten salt. Energy Reports, zv. 6, str. 685-697, 2020.
  • You, F. – Zhang, S. – Jia, R.: Transfer learning for end-product quality prediction of batch processes using domain-adaption joint-Y PLS. Computers and Chemical Engineering, č. 106943, zv. 140, 2020.
  • Palma-Flores, Oscar – Andres-Martinez, Oswaldo – Ricardez-Sandoval, Luis A.: Optimal control and the Pontryagin's principle in chemical engineering: History, theory, and challenges. AICHE JOURNAL, č. 8, zv. 68, str. e17777, 2022.
C. E. Valero – R. Paulen: Effective Recursive Set-membership State Estimation for Robust Linear MPC. V 12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2019, Elsevier, str. 486–491, 2019.
  • Počet citácií       3
  • Wang,Z. -. – Li,X. – Wang,Y. – Ji,Z. -.: Hyperparallel space set-membership filtering based state estimation algorithm for nonlinear system. Kongzhi yu Juece/Control and Decision, č. 9, zv. 37, str. 2287-2295, 2022.
  • Wang,Z. -. – Cheng,L. – Wang,Y. – Ji,Z. -.: Orthometric hyperparallel spatial directional expansion filtering based fault diagnosis method. Kongzhi yu Juece/Control and Decision, č. 12, zv. 37, str. 3223-3232, 2022.
  • Wang, Zi-Yun – Zhang, Zi-Meng – Wang, Yan – Zhan, Ya-Cong – Ji, Zhi-Cheng: Nonlinear system state estimation based on axisymmetric box space filter under uncertain noise; [不确定噪声下基于轴对称盒空间滤波的非线性系统状态估计]. Kongzhi Lilun Yu Yingyong/control Theory and Applications, č. 5, zv. 40, str. 883 – 890, 2023.
P. Valiauga – R. Paulen: Moving-horizon Guaranteed Parameter Estimation. V 12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2019, Elsevier, str. 112–117, 2019.
  • Počet citácií       1
  • Bernardi, E. – Morato, M.M. – Mendes, P.R.C. – Normey-Rico, J.E. – Adam, E.J.: Fault-tolerant energy management for an industrial microgrid: A compact optimization method. International Journal of Electrical Power and Energy Systems, zv. 124, str. 106342, 2021.
M. Villanueva – X. Feng – R. Paulen – B. Chachuat – B. Houska: Convex Enclosures for Constrained Reachability Tubes. V 12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2019, Elsevier, str. 118–123, 2019.
  • Počet citácií       2
  • Scott,J. K. – Shen,K.: Exploiting nonlinear invariants and path constraints to achieve tighter reachable set enclosures using differential inequalities. Mathematics of Control, Signals, and Systems, č. 1, zv. 32, str. 101-127, 2020.
  • Mishra, P.K. – Wang, T. – Gazzola, M. – Chowdhary, G.: Centralized model predictive control with distributed adaptation. V Proceedings of the IEEE Conference on Decision and Control, str. 697-703, 2020.
C. E. Valero – R. Paulen: Set-Theoretic State Estimation for Multi-output Systems using Block and Sequential Approaches. Editor(i): M. Fikar and M. Kvasnica, V Proceedings of the 22nd International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, str. 268–273, 2019.
  • Počet citácií       1
  • Rauh, A. – Bourgois, A. – Jaulin, L.: Union and intersection operators for thick ellipsoid state enclosures: Application to bounded-error discrete-time state observer design. Algorithms, č. 3, zv. 14, str. 88, 2021.
W. Daosud – P. Kittisupakorn – M. Fikar – S. Lucia – R. Paulen: Efficient robust nonlinear model predictive control via approximate multi-stage programming: A neural networks based approach. Editor(i): Anton A. Kiss, Edwin Zondervan, Richard Lakerveld, Leyla Özkan, V 29th European Symposium on Computer Aided Process Engineering, Elsevier, zv. 29, str. 571–576, 2019.
  • Počet citácií       3
  • Mitsos, A. – Kevrekidis, I.G. – Mhamdi, A. – Caspari, A. – Hamacher, N.C. – Vaupel, Y.: Accelerating nonlinear model predictive control through machine learning. Journal of Process Control, zv. 92, str. 261-270, 2020.
  • Thombre, M. – (Joyce) Yu, Z. – Jäschke, J. – Biegler, L.T.: Sensitivity-Assisted multistage nonlinear model predictive control: Robustness, stability and computational efficiency. Computers and Chemical Engineering, zv. 148, str. 107269, 2021.
  • Yu, Zhou (Joyce) – Biegler, Lorenz T.: Sensitivity-assisted Robust Nonlinear Model Predictive Control with Scenario Generation. IFAC PAPERSONLINE, č. 2, zv. 53, str. 7204-7209, 2020.
T. B. L. Tran – M. Törngren – H. D. Nguyen – R. Paulen – N. W. Gleason – T. H. Duong: Trends in preparing cyber-physical systems engineers. Cyber-Physical Systems, č. 2, zv. 5, str. 65–91, 2019.
  • Počet citácií       15
  • Andreev, A. – Nefedyev, Y. – Demina, N.: The Study of Dynamic Parameters of Corporate Graphic Stations Using Methods of Adaptive Regression Multi-Parameter Modeling. V 2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings, str. 9225025, 2020.
  • Mäkiö, E. – Azmat, F. – Ahmad, B. – Harrison, R. – Colombo, A.W.: T-CHAT educational framework for teaching cyber-physical system engineering. European Journal of Engineering Education, 2021.
  • Hernandez-de-Menendez, M. – Escobar Díaz, C.A. – Morales-Menendez, R.: Engineering education for smart 4.0 technology: a review. International Journal on Interactive Design and Manufacturing, č. 3, zv. 14, str. 789-803, 2020.
  • Francisco, R. – de Freitas Rocha Loures, E. – Santos, E.A.P. – Deschamps, F.: Working in the 4.0 Era: An Ontology for Competence Management in the Fourth Industrial Revolution. V Springer Proceedings in Mathematics and Statistics, str. 491-502, 2020.
  • Anatolijs Zabasta – Nadezda Kunicina – Oksana Nikiforova – Joan Peuteman – Alexander K. Fedotov – Alexander S. Fedotov – Andrii Hnatov: Chapter 11 - Development of industry oriented cross-domain study programs in cyber-physical systems for Belarusian and Ukrainian universities. V Multi-Paradigm Modelling Approaches for Cyber-Physical Systems, Editor(i): Bedir Tekinerdogan and Dominique Blouin and Hans Vangheluwe and Miguel Goulão and Paulo Carreira and Vasco Amaral, Academic Press, str. 271-292, 2021.
  • Nefedyev, Y. – Mubarakshina, R. – Andreev, A. – Demina, N.: The study of geodynamic parameters on the basis of adaptive regression modeling. Studies in Systems, Decision and Control, zv. 338, str. 225-236, 2021.
  • Nefedyev, Y. – Andreev, A. – Mubarakshina, R. – Demina, N. – Andreeva, Z.: The Use of Deterministic Mathematical Modeling for the Prediction of Dynamic Geophysical Processes. V 2021 IEEE East-West Design and Test Symposium, EWDTS 2021 - Proceedings, 2021.
  • Li,Y.: Fractional differential equations in National Sports Training in Colleges and Universities. Applied Mathematics and Nonlinear Sciences, 2021.
  • Kosvyra,A. – Filos,D. – Mountford,N. – Cusack,T. – Isomursu,M. – Chouvarda,I.: PhD courses and the intersectoral experience: A comprehensive survey. V International Conference on Higher Education Advances, str. 1131-1139, 2021.
  • Roy,S. – Dong,Y. – Baber,L. – Ahn,B.: Classroom to Workplace: Knowledge and Skills Learned by Recently Hired Aerospace Engineers. Journal of Aerospace Information Systems, č. 4, zv. 19, str. 317-329, 2022.
  • Virmani,N. – Salve,U. R.: Analysing key social implications of implementation of Industry 4.0 in manufacturing industries. International Journal of Process Management and Benchmarking, č. 3, zv. 12, str. 277-299, 2022.
  • Aydin,H. – Sertbaş,A.: CYBER SECURITY IN INDUSTRIAL CONTROL SYSTEMS (ICS): A SURVEY OF ROWHAMMER VULNERABILITY. Applied Computer Science, č. 2, zv. 18, str. 86-100, 2022.
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