Overview of Simulation Architectures Supporting Live Virtual Constructive (LVC) Integrated Training

Gao, Yang; Zhang, Yuanyuan; Zhou, Xiaoguang; Lu, Huan

doi:10.1109/crc52766.2021.9620135

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…The typical application of LVC technology in air combat training and test are listed, similarly knowledge on LVC training can be found in [30][31][32][33][34]. Literatures of LVC methods on scholarly research can be reached at [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50].…”

Section: Applicationsmentioning

confidence: 99%

Review of Live-Virtual-Constructive Simulation Technology

Liang

Wang

Tong

et al. 2023

J. Phys.: Conf. Ser.

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With the improvement of network communication technology, creative computer simulation architectures and today’s cutting-edge hardware, the live-virtual-constructive (LVC) simulation technology gradually matures adequately to synthesize joint heterogeneous systems. Firstly, the development of typical LVC architectures and their key technologies are analysed, such as Advanced Distributed Simulation (ADS), High-Level Architecture (HLA), Test and Train Enabling Architecture (TENA), Joint Simulation Supporting Platform (JoSim) and so force. Then, applications in the field of US and Chinese military are reviewed, as well as cases in air combat training and other scholarly research concept. Finally, a summary of LVC technology is given, and the development trend of military LVC simulation technology is predicted to explore the specific content that needs to be studied.

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Section: Applicationsmentioning

confidence: 99%

Review of Live-Virtual-Constructive Simulation Technology

Liang

Wang

Tong

et al. 2023

J. Phys.: Conf. Ser.

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“…Platform and implementation independence is a major concern in the modeling and simulation community and is essential when combining several distributed simulations in a shared so-called Life, Virtual, Constructive (LVC) environment. 39,40 Various players on various simulation systems can play in person (with instrumentation) or move entities around either as first person players or as leaders of semi-automated entity aggregates. The HLA and BOM format mentioned above are central to this (with the already-mentioned limitations).…”

Section: Related Workmentioning

confidence: 99%

A logic-based event controller for means-end reasoning in simulation environments

2023

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Simulation games are designed to cultivate expertise and rehearse particular skill sets. To yield longitudinal effects, sequences of events must be crafted to yield intended learning outcomes, sometimes by focusing on particularly difficult situations and replaying variants. The present paper develops a logic-based approach for encoding the interrelation between action, events, and objects in a manner that allows the resulting scenario description to immediately be executed in a game development environment. This has the dual effect of decoupling the description of a scenario from the simulation platform itself, as well as supporting iterative and flexible development of learning content. To this end, we provide three interrelated components: First, we develop a scenario description language based on Answer Set Programming. The language is designed to allow an automated reasoner to deduce a schedule of the future events that are caused by an action taken in a given simulation environment. Second, we define a protocol for exchanging actions and computed futures between, respectively, the simulation environment and the external automated reasoner. Finally, as a proof of concept, we develop an Application Programming Interface (API) for the Unity Real-Time Development Platform that implements the protocol and offers a software framework for connecting the computed future events to concrete game objects. This allows the game to evolve coherently from the specification. We argue that the resulting system inherits capabilities for artificial commonsense reasoning from its declarative basis which are useful for reasoning about an evolving emergency incident or training scenario.

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“…As shown in Equation (1), this encoding method makes it easy to ensure that some constraints are satisfied by reasonable crossover and mutation operators, which mainly restrict the properties of variables and the relationships between variables, as formulated in Equations ( 15)- (21). However, it is difficult to satisfy the server resource occupancy constraints, e.g., Equation ( 14), by intuition.…”

Section: Constraint Handling Mechanismmentioning

confidence: 99%

“…We design two crossover operators, which can be understood as vertical and horizontal combined with the encoding method shown as Equation (1).…”

Section: Crossover Operatorsmentioning

confidence: 99%

“…Training simulation is a safe, controllable, and cost-effective training method that can improve the abilities of trainees, becoming a critical method of talent cultivation in various industries. In large-scale geo-distributed training simulations, simulation members, i.e., users, can be categorized into three types [1]: live (L), virtual (V), and constructive (C) members, such as actual aircraft with various sensors, aircraft simulators controlled by simulated cockpits and computer generated aircraft, respectively. These members dispersed across different locations are integrated into a virtual space, where they can see and interact with each other.…”

Section: Introductionmentioning

confidence: 99%

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An Approach for Deployment of Service-Oriented Simulation Run-Time Resources

Zhang,

Peng,

Zhang

et al. 2023

Applied Sciences

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The requirements for low latency and high stability in large-scale geo-distributed training simulations have made cloud-edge collaborative simulation an emerging trend. However, there is currently limited research on how to deploy simulation run-time resources (SRR), including edge servers, simulation services, and simulation members. On one hand, the deployment schemes of these resources are coupled and have mutual impacts. It is difficult to ensure overall optimum by deploying these resources separately. On the other hand, the pursuit of low latency and high system stability is often challenging to achieve simultaneously because high stability implies low server load, while a small number of simulation services implies high response latency. We formulate this problem as a multi-objective optimization problem for the joint deployment of SRR, considering the complex combinatorial relationship between simulation services. Our objective is to minimize the system time cost and resource usage rate of edge servers under constraints such as server resource capacity and the relationship between edge servers and base stations. To address this problem, we propose a learnable genetic algorithm for SRR deployment (LGASRD) where the population can learn from elites and adaptively select evolution operators performing well. Extensive experiments with different settings based on real-world data sets demonstrate that LGASRD outperforms the baseline policies in terms of optimality, feasibility, and convergence rate, verifying the effectiveness and excellence of LGASRD when deploying SRR.

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Overview of Simulation Architectures Supporting Live Virtual Constructive (LVC) Integrated Training

Cited by 6 publications

References 3 publications

Review of Live-Virtual-Constructive Simulation Technology

Review of Live-Virtual-Constructive Simulation Technology

A logic-based event controller for means-end reasoning in simulation environments

An Approach for Deployment of Service-Oriented Simulation Run-Time Resources

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