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@inproceedings{1863710, author = {Kostka, Petr and Rossi, Bruno and Ge, Mouzhi}, address = {Not specified}, booktitle = {International Conference on Systems, Man, and Cybernetics (SMC'22)}, doi = {http://dx.doi.org/10.1109/SMC53654.2022.9945553}, keywords = {Industry 4.0; Monte Carlo Simulations; MCMC; MC}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Not specified}, isbn = {978-1-6654-5258-8}, pages = {242-247}, publisher = {IEEE}, title = {Monte Carlo Methods for Industry 4.0 Applications}, year = {2022} }
TY - JOUR ID - 1863710 AU - Kostka, Petr - Rossi, Bruno - Ge, Mouzhi PY - 2022 TI - Monte Carlo Methods for Industry 4.0 Applications PB - IEEE CY - Not specified SN - 9781665452588 KW - Industry 4.0 KW - Monte Carlo Simulations KW - MCMC KW - MC N2 - The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluation of the quality of the process in terms of KPIs. Monte Carlo simulations can play an important role to improve the estimations. However, there is still a lack of clear workflow to conduct the Monte Carlo simulations for selecting different Monte Carlo methods. This paper, therefore, proposes a simulation flow for conducting Monte Carlo methods comparison in Industry 4.0 applications. Based on the simulation flow, we compare Cumulative Monte Carlo and Markov Chain Monte Carlo methods. The experimental results show the way to use the Monte Carlo methods in Industry 4.0 and possible limitations of the two simulation methods. ER -
KOSTKA, Petr, Bruno ROSSI a Mouzhi GE. Monte Carlo Methods for Industry 4.0 Applications. Online. In \textit{International Conference on Systems, Man, and Cybernetics (SMC'22)}. Not specified: IEEE, 2022, s.~242-247. ISBN~978-1-6654-5258-8. Dostupné z: https://dx.doi.org/10.1109/SMC53654.2022.9945553.
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