Application of Probabilistic Process Model for Smart Factory Systems

Junsup Song; Yeongbok Choe; Moonkun Lee

doi:10.1007/978-3-030-29563-9_3

Application of Probabilistic Process Model for Smart Factory Systems

Junsup Song
, Yeongbok Choe
, Moonkun Lee^*

^*Corresponding author for this work

Jeonbuk National University

Research output: Contribution to conference › Conference paper › peer-review

5 Scopus citations

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.

Original language	English
Title of host publication	Knowledge Science, Engineering and Management - 12th International Conference, KSEM 2019, Proceedings
Editors	Christos Douligeris, Dimitris Apostolou, Dimitris Karagiannis
Publisher	Springer
Pages	25-36
Number of pages	12
ISBN (Print)	9783030295622
DOIs	https://doi.org/10.1007/978-3-030-29563-9_3
State	Published - 2019
Event	12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019 - Athens, Greece Duration: 2019.08.28 → 2019.08.30

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11776 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019
Country/Territory	Greece
City	Athens
Period	19.08.28 → 19.08.30

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

ADOxx Meta-Modeling Platform
dTP-Calculus
Formal method
Probability
SAVE
Smart Factory

Quacquarelli Symonds(QS) Subject Topics

Computer Science & Information Systems

Access to Document

10.1007/978-3-030-29563-9_3

Cite this

Song, J., Choe, Y., & Lee, M. (2019). Application of Probabilistic Process Model for Smart Factory Systems. In C. Douligeris, D. Apostolou, & D. Karagiannis (Eds.), Knowledge Science, Engineering and Management - 12th International Conference, KSEM 2019, Proceedings (pp. 25-36). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11776 LNAI). Springer. https://doi.org/10.1007/978-3-030-29563-9_3

Song, Junsup ; Choe, Yeongbok ; Lee, Moonkun. / Application of Probabilistic Process Model for Smart Factory Systems. Knowledge Science, Engineering and Management - 12th International Conference, KSEM 2019, Proceedings. editor / Christos Douligeris ; Dimitris Apostolou ; Dimitris Karagiannis. Springer, 2019. pp. 25-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{858b849cc0c64f8999011b91d17e3a09,

title = "Application of Probabilistic Process Model for Smart Factory Systems",

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.",

keywords = "ADOxx Meta-Modeling Platform, dTP-Calculus, Formal method, Probability, SAVE, Smart Factory",

author = "Junsup Song and Yeongbok Choe and Moonkun Lee",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019 ; Conference date: 28-08-2019 Through 30-08-2019",

year = "2019",

doi = "10.1007/978-3-030-29563-9\_3",

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Song, J, Choe, Y & Lee, M 2019, Application of Probabilistic Process Model for Smart Factory Systems. in C Douligeris, D Apostolou & D Karagiannis (eds), Knowledge Science, Engineering and Management - 12th International Conference, KSEM 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11776 LNAI, Springer, pp. 25-36, 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019, Athens, Greece, 19.08.28. https://doi.org/10.1007/978-3-030-29563-9_3

Application of Probabilistic Process Model for Smart Factory Systems. / Song, Junsup; Choe, Yeongbok; Lee, Moonkun.
Knowledge Science, Engineering and Management - 12th International Conference, KSEM 2019, Proceedings. ed. / Christos Douligeris; Dimitris Apostolou; Dimitris Karagiannis. Springer, 2019. p. 25-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11776 LNAI).

Research output: Contribution to conference › Conference paper › peer-review

TY - GEN

T1 - Application of Probabilistic Process Model for Smart Factory Systems

AU - Song, Junsup

AU - Choe, Yeongbok

AU - Lee, Moonkun

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - ADOxx Meta-Modeling Platform

KW - dTP-Calculus

KW - Formal method

KW - Probability

KW - SAVE

KW - Smart Factory

UR - https://www.scopus.com/pages/publications/85076688632

U2 - 10.1007/978-3-030-29563-9_3

DO - 10.1007/978-3-030-29563-9_3

M3 - Conference paper

AN - SCOPUS:85076688632

SN - 9783030295622

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 25

EP - 36

BT - Knowledge Science, Engineering and Management - 12th International Conference, KSEM 2019, Proceedings

A2 - Douligeris, Christos

A2 - Apostolou, Dimitris

A2 - Karagiannis, Dimitris

PB - Springer

T2 - 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019

Y2 - 28 August 2019 through 30 August 2019

ER -

Song J, Choe Y, Lee M. Application of Probabilistic Process Model for Smart Factory Systems. In Douligeris C, Apostolou D, Karagiannis D, editors, Knowledge Science, Engineering and Management - 12th International Conference, KSEM 2019, Proceedings. Springer. 2019. p. 25-36. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-29563-9_3

Application of Probabilistic Process Model for Smart Factory Systems

Abstract

Publication series

Conference

UN SDGs

Keywords

Quacquarelli Symonds(QS) Subject Topics

Access to Document

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