TY - GEN
T1 - Model-Based Systems Engineering Supporting Integrated Modeling and Optimization of Radar Cabin Layout
AU - She, Shiyan
AU - Lu, Jinzhi
AU - Wang, Guoxin
AU - Ding, Jie
AU - Hu, Zixiang
N1 - Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.
PY - 2021
Y1 - 2021
N2 - The equipment layout optimization of a UAV (Unmanned Aerial Vehicle) radar cabin can decrease cable length in order to promote the quality of radar and UAV system. Model-based Systems Engineering (MBSE) is widely used for UAV development, particularly for the layout design of UAV radar cabin. In this paper, a semantic modeling approach based on KARMA language is proposed to create the system model of the radar cabin layout based on an MBSE approach for formalizing Requirement, Function, Logical and Physical structure (RFLP). Moreover, the KARMA models for UAV radar cabin layout modeling are transformed to the Genetic Algorithm (GA) in MATLAB toolkit for radar cabin layout optimization by code generation. Based on the layout information generated from the KARMA models, the optimized layout solution is generated by the MATLAB toolkit. From the case study, we find the KARMA language enables to formalize the radar cabin design based on nine diagrams of SysML specification. And optimizations can be executed automatically after getting data generated from KARMA models. Thereby, the proposed semantic modeling approach improves design efficiency and quality during radar cabin design.
AB - The equipment layout optimization of a UAV (Unmanned Aerial Vehicle) radar cabin can decrease cable length in order to promote the quality of radar and UAV system. Model-based Systems Engineering (MBSE) is widely used for UAV development, particularly for the layout design of UAV radar cabin. In this paper, a semantic modeling approach based on KARMA language is proposed to create the system model of the radar cabin layout based on an MBSE approach for formalizing Requirement, Function, Logical and Physical structure (RFLP). Moreover, the KARMA models for UAV radar cabin layout modeling are transformed to the Genetic Algorithm (GA) in MATLAB toolkit for radar cabin layout optimization by code generation. Based on the layout information generated from the KARMA models, the optimized layout solution is generated by the MATLAB toolkit. From the case study, we find the KARMA language enables to formalize the radar cabin design based on nine diagrams of SysML specification. And optimizations can be executed automatically after getting data generated from KARMA models. Thereby, the proposed semantic modeling approach improves design efficiency and quality during radar cabin design.
KW - KARMA
KW - MBSE
KW - RFLP
KW - Radar cabin layout
UR - http://www.scopus.com/inward/record.url?scp=85115221473&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-85910-7_23
DO - 10.1007/978-3-030-85910-7_23
M3 - Conference contribution
AN - SCOPUS:85115221473
SN - 9783030859091
T3 - IFIP Advances in Information and Communication Technology
SP - 218
EP - 227
BT - Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings
A2 - Dolgui, Alexandre
A2 - Bernard, Alain
A2 - Lemoine, David
A2 - von Cieminski, Gregor
A2 - Romero, David
PB - Springer Science and Business Media Deutschland GmbH
T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021
Y2 - 5 September 2021 through 9 September 2021
ER -
