TY - GEN
T1 - A multi-paradigm simulation for the implementation of digital twins in surveillance applications
AU - Lee, Seunghan
AU - Jain, Saurabh
AU - Zhang, Yinwei
AU - Liu, Jian
AU - Son, Young Jun
N1 - Funding Information:
This work is supported by the Air Force Office of Scientific Research Dynamic Data Driven Application Systems Program under FA9550-17-1-0075.
Publisher Copyright:
© Proceedings of the 2020 IISE Annual. All Rights Reserved.
PY - 2020
Y1 - 2020
N2 - Physics-based simulation (PBS) is now widely utilized to maximize the usage of real-time sensory information in surveillance applications. Since agent-based simulation (ABS) helps in analyzing human behaviors under different scenarios, the combination of PBS and ABS can provide a better situational awareness capability by considering both the sensory inputs and human decisions. Furthermore, advances in sensory detection and tracking technologies allow for real-time planning and control of the surveillance system in the broader area. This paper aims to devise an optimal planning and control policy for surveillance systems, which will process different types of sensory data including videos, seismic data, as well as behavior models. To consider different scenarios in the surveillance area, we formulate this problem as a Markov Decision Process (MDP) by utilizing various sensory data for parameter selection. We then develop a Digital Twin (DT) of the surveillance using both PBS and ABS to calibrate and validate our proposed MDP framework. The resulting multi-paradigm simulation framework with DT can be an attractive approach to handle uncertainties in a system caused by the heterogeneity and velocity of the sensory data.
AB - Physics-based simulation (PBS) is now widely utilized to maximize the usage of real-time sensory information in surveillance applications. Since agent-based simulation (ABS) helps in analyzing human behaviors under different scenarios, the combination of PBS and ABS can provide a better situational awareness capability by considering both the sensory inputs and human decisions. Furthermore, advances in sensory detection and tracking technologies allow for real-time planning and control of the surveillance system in the broader area. This paper aims to devise an optimal planning and control policy for surveillance systems, which will process different types of sensory data including videos, seismic data, as well as behavior models. To consider different scenarios in the surveillance area, we formulate this problem as a Markov Decision Process (MDP) by utilizing various sensory data for parameter selection. We then develop a Digital Twin (DT) of the surveillance using both PBS and ABS to calibrate and validate our proposed MDP framework. The resulting multi-paradigm simulation framework with DT can be an attractive approach to handle uncertainties in a system caused by the heterogeneity and velocity of the sensory data.
KW - Agent-based simulation
KW - Digital Twins
KW - Markov Decision Process
KW - Physics-based simulation
KW - Surveillance
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M3 - Conference contribution
AN - SCOPUS:85105656321
T3 - Proceedings of the 2020 IISE Annual Conference
SP - 79
EP - 84
BT - Proceedings of the 2020 IISE Annual Conference
A2 - Cromarty, L.
A2 - Shirwaiker, R.
A2 - Wang, P.
PB - Institute of Industrial and Systems Engineers, IISE
T2 - 2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020
Y2 - 1 November 2020 through 3 November 2020
ER -