Abstract
Process algebra is one of the best suitable formal methods to model smart systems based on IoT, especially Smart Factory. However, because of some uncertainty, it is necessary to model predictability of the systems, based on the uncertainty. There have been several process algebras with probability, such as, PAROMA, PACSR, etc. However they are not well suitable for the smart systems, since they are based only on discrete model or exponential model. Consequently, only simple or targeted probability can be specified and analyzed. In order to handle such limitations, the paper presents a new formal method, called dTP-Calculus, extended from the existing dT-Calculus with discrete, normal, exponential, and uniform probability models. It provides all the possible probability features for Smart Factory with complex uncertainty. The specification of the modeling will be simulated statistically for Smart Factory, and further the simulation results will be analyzed for probabilistic properties of the systems. For implementation, a tool set for the calculus has been developed in the SAVE tool suite on the ADOxx Meta-Modeling Platform, including Specifier, Analyzer and Verifier. A Smart Factory example from Audi Cell Production System has been selected as an example to demonstrate the feasibility of the approach.
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Acknowledgment
This work was supported by Basic Science Research Programs through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2010-0023787), Space Core Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2014M1A3A3A02034792), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A3A01019282), and Hyundai NGV, Korea.
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Song, J., Choe, Y., Lee, M. (2019). Application of Probabilistic Process Model for Smart Factory Systems. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_3
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DOI: https://doi.org/10.1007/978-3-030-29563-9_3
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