This paper presents a modeling method to demonstrate engagement-level military simulation which includes few combat objects, or entities. To this end, the paper, on the basis of the discrete event system specification (DEVS) formalism, centers on two ideas: (1) a combat entity’s model structure at the composition level; and (2) behavioral delineation of the entity’s elementary component. In detail, we classify the combat entity model into platform and weapon models and create six groups of the model categorized by two dimensions: three activities and two abstractions. And the elementary component in the group interprets an engagement scenario as a flow of executable tasks, which are expressed by DEVS semantics. The stated structures and semantics provide intuitive appeal, reducing the effort required to read and understand the model’s behavior. From the combat experiments, we can gain interesting experimental results regarding engagement situations employing underwater weapons and their tactical operations. Finally, we expect that this work will serve an immediate application suited to various engagement situations.

Simulation analysis has been performed for simulation experiments of all possible input combinations as a “what-if” analysis, which causes the simulation to be extremely time-consuming. To resolve this problem, this paper proposes a multi-fidelity modeling framework for enhancing simulation speed while minimizing simulation accuracy loss. A target system for this framework is a discrete event dynamic system. The dynamic property of the system facilitates the development of variable fidelity models for the target system due to its high computational cost; and the discrete event property allows for determining when to change the fidelity within a simulation scenario. For formal representation, the paper defines several key concepts such as an interest region, a fidelity change condition, and a selection model. These concepts are integrated into the framework to allow for the achievement of a condition-based disjunction of high- and low-fidelity simulations within a scenario. The proposed framework is applied to two case studies: unmanned underwater and urban transportation vehicles. The results show that simulation speed increases at least 1.21 times with a 5% accuracy loss. We expect that the proposed framework will resolve a computationally expensive problem in the simulation analysis of discrete event dynamic systems.

This paper discusses two modeling and simulation (M&S) issues for defense cyber-physical systems (CPSs): 1) model-driven development via model interoperation and 2) simulation analysis between interoperated models. To clearly describe the network capabilities between the CPSs within network-centric warfare (NCW), we first modeled communication factors separately from the overall CPSs. The CPS models and the network model were considered discrete event systems, which were realized with DEVSim++ and ns-3, respectively. For simulation of the two types of models, we interoperated them with high-level architecture-based middleware to preserve the independence of each one. The goal for NCW analysis is to find a balance between the computational, physical, and communication factors of the defense CPSs to achieve their missions within a networked environment. In the case study of network-centric ground warfare, the CPS models generate mission effectiveness to be analyzed according to realistic network conditions; at the same time, the network model enables the analysis of the network performance given validated combat scenarios. We expect this study will help simulation experts interactively analyze computational and physical capabilities of the CPSs as well as communication effects between the CPSs.

Command and control (C2) and communication are at the heart of successful military operations in network-centric warfare. Interoperable simulation of a C2 system model and a communication (C) system model may be employed to interactively analyze their detailed behaviors. However, such simulation would be inefficient in simulation time for analysis of combat effectiveness of the C2 model against possible input combinations while considering the communication effect in combat operations. This study proposes a discrete event dynamic surrogate model (DEDSM) for the C model, which would be integrated with the C2 model and simulated. The proposed integrated simulation reduces execution time markedly in analysis of combat effectiveness without sacrificing the accuracy reflecting the communication effect. We hypothesize the DEDSM as a probabilistic priority queuing model whose semantics is expressed in a discrete event systems specification model with some characteristic functions unknown. The unknown functions are identified by machine learning with a data set generated by interoperable simulation of the C2 and C models. The case study with the command, control, and communication system of systems first validates the proposed approach through an equivalence test between the interoperable simulation and the proposed one. It then compares the simulation execution times and the number of events exchanged between the two simulations.

This study utilizes modeling and simulation to analyze coronavirus (COVID-19) infection trends depending on government policies. Two modeling requirements are considered for infection simulation: (1) the implementation of social distancing policies and (2) the representation of population movements. To this end, we propose an extended infection model to combine analytical models with discrete event-based simulation models in a hybrid form. Simulation parameters for social distancing policies are identified and embedded in the analytical models. Administrative districts are modeled as a fundamental simulation agent, which facilitates representing the population movements between the cities. The proposed infection model utilizes real-world data regarding suspected, infected, recovered, and deceased people in South Korea. As an application, we simulate the COVID-19 epidemic in South Korea. We use real-world data for 160 days, containing meaningful days that begin the distancing policy and adjust the distancing policy to the next stage. We expect that the proposed work plays a principal role in analyzing how social distancing effectively affects virus prevention and provides a simulation environment for the biochemical field.