TY - GEN
T1 - A Modeling Framework of Cyber-Physical-Social Systems with Human Behavior Classification Based on Machine Learning
AU - An, Dongdong
AU - Liu, Jing
AU - Chen, Xiaohong
AU - Li, Tengfei
AU - Yin, Ling
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Cyber-Physical-Social Systems (CPSS) is an emerging complicated topic in recent years which focuses on the researches of a combination of cyberspace, physical space and social space. Different from traditional Cyber-Physical-Systems, CPSS contain human who interacts with the cyber and physical part more frequently. So how to capture and analyse human behaviors play a vital role in CPSS performance evaluation. To improve the analysis accuracy of CPSS, the paper proposes a new modelling framework – stohMCharts (stochastic hybrid MARTE statecharts) which is an extension of MARTE statecharts for stochastic hybrid system modelling and analysis. Compared to MARTE statechart, in stohMCharts, we can model the CPSS in a unified way. Also, we associate stohMCharts to NSHA (Networks Stochastic Hybrid Automata) and use statistical model checker UPPAAL-SMC to verify the stohMCharts. We apply an autonomous car as an example to explain the efficiency of our proposed approaches.
AB - Cyber-Physical-Social Systems (CPSS) is an emerging complicated topic in recent years which focuses on the researches of a combination of cyberspace, physical space and social space. Different from traditional Cyber-Physical-Systems, CPSS contain human who interacts with the cyber and physical part more frequently. So how to capture and analyse human behaviors play a vital role in CPSS performance evaluation. To improve the analysis accuracy of CPSS, the paper proposes a new modelling framework – stohMCharts (stochastic hybrid MARTE statecharts) which is an extension of MARTE statecharts for stochastic hybrid system modelling and analysis. Compared to MARTE statechart, in stohMCharts, we can model the CPSS in a unified way. Also, we associate stohMCharts to NSHA (Networks Stochastic Hybrid Automata) and use statistical model checker UPPAAL-SMC to verify the stohMCharts. We apply an autonomous car as an example to explain the efficiency of our proposed approaches.
KW - Cyber-Physical-Social Systems
KW - Statistical model checking
KW - Stochastic Hybrid Automata
KW - Stochastic hybrid MARTE statecharts
UR - https://www.scopus.com/pages/publications/85076147261
U2 - 10.1007/978-3-030-32409-4_37
DO - 10.1007/978-3-030-32409-4_37
M3 - 会议稿件
AN - SCOPUS:85076147261
SN - 9783030324087
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 522
EP - 525
BT - Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Proceedings
A2 - Ait-Ameur, Yamine
A2 - Qin, Shengchao
PB - Springer
T2 - 21st International Conference on Formal Engineering Methods, ICFEM 2019
Y2 - 5 November 2019 through 9 November 2019
ER -