No abstract

Recently, big data has received greater attention in diverse research fields, including medicine, science, engineering, management, defense, politics, and others. Such research uses big data to predict target systems, thereby constructing a model of the system in two ways: data modeling and simulation modeling. Data modeling is a method in which a model represents correlation relationships between one set of data and the other set of data. On the other hand, physics-based simulation modeling (or simply simulation modeling) is a more classical, but more powerful, method in which a model represents causal relationships between a set of controlled inputs and corresponding outputs. This paper (i) clarifies the difference between the two modeling approaches, (ii) explains their advantages and limitations and compares each characteristic, and (iii) presents a complementary cooperation modeling approach. Then, we apply the proposed modeling to develop a greenhouse control system in the real world. Finally, we expect that this modeling approach will be an alternative modeling approach in the big data era.

No abstract

This article presents an application of the Discrete Event System Specification (DEVS) framework to the design and safety analysis of a real-time embedded control system, a railroad crossing control system. The authors employ an extension of the DEVS formalism, real-time DEVS (RT-DEVS), which has a sound semantics for the specification of real-time systems in a hierarchical modular fashion. The notion of a clock matrix for communicating RT-DEVS models is proposed, which represents a global time between the models. Based on the composition rules and the clock matrix, an algorithm for the generation of a timed reachability tree is developed that can be used for safety analysis at two phases: an untimed and timed analysis phase. A railroad crossing control example demonstrates that the proposed analysis for RT-DEVS models would be effective to verify the safety property of real-time control systems.

No abstract

This paper presents a method for solving the optimization problems that arise in hybrid systems. These systems are characterized by a combination of continuous and discrete event systems. The proposed method aims to find optimal design configurations that satisfy a goal performance. For exploring design parameter space, the proposed method integrates a metamodel and a metaheuristic method. The role of the metamodel is to give good initial candidates and reduced search space to the metaheuristic optimizer. On the other hand, the metaheuristic method improves the quality of the given candidates. This proposal also demonstrates a defense system that illustrates the practical application of the presented method. The optimization objective of the case study is to find the required operational capability configurations of a decoy that meet the desired measure of effectiveness. Through a comparison with a full search method, two metamodeling methods without the aid of metaheuristics and a metaheuristic method without the support of metamodels, we confirmed that the proposed method provides same high-quality solutions as those of the full search method at a small computational cost.

Modeling and simulation (M&S) has long played an important role in developing tactics and evaluating the measure of effectiveness (MOE) for the underwater warfare system. In simulation-based acquisition, M&S technology facilitates decisions about future equipment procurements, such as a mobile decoy or a torpedo. In addition, assessment of submarine tactical development, during an engagement against a torpedo, can be conducted using M&S techniques. This paper presents a case study that applies discrete event systems specification-based M&S technology to develop a simulation of an underwater warfare system, specifically, an anti-torpedo combat system, to analyze the MOE of the system. The entity models required for M&S are divided into three sub-models: controller, maneuver, and sensor model. The developed simulation allows us to conduct a statistical evaluation of the overall underwater warfare system under consideration, an assessment of the anti-torpedo countermeasure's effectiveness, and an assessment of tactics development of the underwater vehicle. Moreover, it can be utilized to support the decision-making process for future equipment procurements. In order to analyze the system effectiveness, we performed extensive combat experiments by varying parameters, such as various tactics and weapon performance. The experimental results show how the factors influence the MOEs of the underwater warfare system.

Communication system in the network-centric warfare (NCW) has been analyzed from the perspective of the system of systems (SoS), which consists of a combat system and a network system so that the two reflect each other’s effects. However, this paradoxically causes a prolonged execution time. To solve this problem, this paper proposes an advanced integrated modeling method for the communication analysis in the NCW via the transformation of the SoS, which reduces the simulation execution time while ensuring the accuracy of the communication effects. The proposed models mainly cover interentity traffic and intraentity mobility developed in the form of feed-forward neural networks to guarantee two-way interactions between the combat system and the network system. Because they are characterized as discrete events, the proposed models are designed with the discrete-event system specification (DEVS) formalism. The experimental results show that the proposed transformation reduced an error by 6.40% compared to the existing method and reduced the execution time 3.78-fold compared to the SoS-based NCW simulation.