퍼지와 DEVS를 이용한 선박 충돌 위험 예측 모델 설계

Discrete Event Systems Specification (DEVS) formalism supports the specification of discrete event models in a hierarchical and modular manner. Efforts have been made to develop the simulation environments for the modeling and simulation (M&S) of systems using DEVS formalism, particularly in defense M&S domains. This paper introduces the DEVSim++ toolset and its applications. The Object-Analysis Index (OAI) matrix is a tabular form of objects and analysis indices for requirements analysis. DEVSim++ is a realization of DEVS formalism in C++ for M&S. VeriTool is a DEVS model verification tool. DEVSimHLA is a library to support High-level Architecture (HLA) in DEVSim++. Other tools, including KComLib, FOM2CPPClass, and KHLAAdaptor, are used to develop a smart adaptor that allows for the interoperation of simulators of any kind. PlugSim is a distributed simulation framework using plug-in methods. These tools are utilized in every stage of the M&S development process, as well as in every application of the M&S missions to the military domain. Accordingly, the applications implemented by the toolset are used in the training, analytic, and acquisition missions of the Republic of Korea military branches. We expect the DEVS applications to become more prolific as M&S demands grow, and our toolset is already proven as complete and efficient in the domain of defense M&S.

시뮬레이션 프레임워크는 시뮬레이션 응용 프로그램의 개발을 지원하는 기반 소프트웨어이다. 본 논문은 공학용 프로그래밍 언어로 광범위하게 사용되는 매트랩을 이용하여 개발된 이산사건시뮬레이션 프레임워크의 개발 과정을 기술하고 있다. 매트랩 객체지향프로그래밍을 토대로 새롭게 개발된 프레임워크는 매트랩 언어의 편리성과 이산사건시뮬레이션 형식론(DEVS: Discrete EVent System Specification Formalism)이 가지는 뛰어난 개발 방법론을 결합시킴으로써 무기체계 교전 시뮬레이션 프로그램 개발에서 요구되는 생산성, 유연성, 확장성을 제공한다. 더불어 매트랩의 병렬컴퓨팅 기술을 적용한 배치(Batch) 시뮬레이션 기능을 제공함으로써 몬테카를로 시뮬레이션 수행시 컴퓨터 환경에서 지원되는 CPU 코어의 수에 비례하여 응용 프로그램의 연산성능을 향상시킨다. Simulation Framework is a basic software tool used to develop simulation applications. This paper describes the development of a discrete event simulation framework based on DEVS(Discrete EVent System Specification) formalism, using MATLAB language which is widely used in technical computing and engineering disciplines. The newly developed framework utilizing MATLAB object oriented programming combines the convenience of MATLAB language and the sophisticated architecture of the DEVS formalism. Hence, it supports the productivity, flexibility, extensibility that are required for the simulation application software development of the weapon systems engagement. Moreover, it promises a simulation application the increased the computation speed proportional to the number of CPU of a multi-core processor, providing the batch simulation functionality based on MATLAB parallel computing technology.

In this chapter, we formalize the multi-perspective architecture framework using the system-theoretic paradigm provided by the discrete event system specification (DEVS) formalism. The developed integration approach that allows linking up the isolated perspectives has been formalized through a concept based upon DEVS formalism called parameterized DEVS, whereby concurrent simulation processes cause live update through output-to-parameter integration. This sets up the basis for development of DEVS-based holistic models for efficient management of healthcare systems. Results of real case-based simulations are presented to show how holistic models provide new insights not heretofore attainable with unit and facility specific models.

The Routed Discrete Event System Specification (RDEVS) is a modular and hierarchical Modeling and Simulation (M&S) formalism based on the Discrete Event System Specification (DEVS) formalism that provides a set of design models for dealing with routing problems over DEVS. At the formal level, RDEVS models (as DEVS models themselves) are defined mathematically. However, software implementations of both formalisms are based on an object-oriented paradigm. Furthermore, at the implementation design level, the RDEVS formalism is represented by a conceptual model that uses DEVS simulators as execution engines. Even when RDEVS models can be executed with DEVS simulators, the resulting data (obtained as execution outputs) remains DEVS-based, restricting the study of event flows between models influenced by routing policies. This paper shows how the RDEVS formalism design was enhanced to include event tracking in the models without altering their expected behavior during simulation. Such an improvement is based on adding new features to existing RDEVS components. These features are defined as trackers, which are responsible for getting structured data from events exchanged during RDEVS executions. The proposed solution employs the Decorator pattern as a software engineering option to achieve the required goal. It was deployed as a Java package attached to the RDEVS library, devoted to collecting structured event flow data using JavaScript Object Notation (JSON). The results highlight the modeling benefits of adding event tracking to the original capabilities of the RDEVS formalism. For the M&S community, the novel contribution is an advance in understanding how best modeling practices of software engineering can be used to enhance their software tools in general and the RDEVS formalism in particular.

Performance analyses of naval ships based on engineering level of simulation at the initial design stage

Employing Modeling and Simulation (M&S) extensively to analyze and develop complex systems is the norm today. The use of robust M&S formalisms and rigorous methodologies is essential to deal with complexity. Among them, the Discrete Event System Specification (DEVS) provides a solid framework for modeling structural, behavior and information aspects of any complex system. This gives several advantages to analyze and design complex systems: completeness, verifiability, extensibility, and maintainability. DEVS formalism has been implemented in many programming languages and executable on multiple platforms. In this paper, we describe the features of an M&S framework called xDEVS that builds upon the prevalent DEVS Application Programming Interface (API) for both modeling and simulation layers, promoting interoperability between the existing platform‐specific (C++, Java, Python) DEVS implementations. Additionally, the framework can simulate the same model using sequential, parallel, or distributed architectures. The M&S engine has been reinforced with several strategies to improve performance, as well as tools to perform model analysis and verification. Finally, xDEVS also facilitates systems engineers to apply the vision of model‐based systems engineering (MBSE), model‐driven engineering (MDE), and model‐driven systems engineering (MDSE) paradigms. We highlight the features of the proposed xDEVS framework with multiple examples and case studies illustrating the rigor and diversity of application domains it can support.

We introduce a generic method based on the Discrete Event System Specification (DEVS) formalism to model and simulate a Controller Area Network (CAN) port connected to a network with other nodes. The models are tested in software and then implemented in hardware. We show a streamlined development process to model, simulate, and test the use of the CAN network for any general application using the Cadmium simulation environment for software simulations and executing the models on hardware using Real-Time (RT-) DEVS. With this method, different scenarios can be tested in the modeling and simulation phase before the models are ported onto embedded hardware.

A hybrid model is a combination of models developed in various simulation development environments. A war game model is a kind of hybrid model and consists of multiple level models. Engineering level models are interested in the change of values via time and process mathematical equations, and most of them are developed in MALTAB/Simulink. Engagement level models are concerned about state transitions by events and modeled using the Discrete Event Systems Specification (DEVS) formalism. This paper proposes a framework for interoperation of simulation tools for engineering and engagement level models simulation. The framework employs the High Level Architecture (HLA) to reuse existing models and suggests specific interfaces for MATLAB/Simulink. The proposed framework is demonstrated through simulation of a simple war game model including heterogenous models.

The industry has experienced significant advancements in recent years, primarily focusing on smart manufacturing, culminating in the Industry 4.0 (I4.0) revolution I4.0 emphasizes interconnectivity, real time data capture and transmission among machines, autonomy, and machine learning, providing manufacturing companies numerous growth opportunities. The Industrial Internet of Things (IIoT) is a core component of this revolution, becoming integral to each system and increasing complexity due to the vast number of interconnected devices and diverse physical components. The variety of virtual services distributed across the architectural layers of industrial systems (cloud, fog, edge) and the various connection types between IIoT devices introduce security and privacy challenges, which are critical issues for any system incorporating IIoT. To fully leverage IIoT’s potential, addressing these security and privacy concerns is essential. Research and design in this domain are challenging, particularly when creating a simulation environment to study a system’s behavior over time. Despite the extensive research in IoT and the significant benefits of simulation based approaches, there remains a challenge in creating detailed representations from the underlying IoT nodes to the application layer in the cloud, along with the underlying networking infrastructure. To assist researchers and practitioners in overcoming these challenges, we propose the Discrete Event System Specification (DEVS) formalism. DEVS provides a mathematical framework for modeling systems, whether discrete or continuous events, allowing for the simulation of these systems within the DEVS environment. Every system, whether real or conceptual, has a time base, inputs, outputs, and functions to determine the next state, as well as outputs that reflect the current state and inputs. Simulating the system within the DEVS environment allows one to study its behavior to predict and optimize performance patterns.

DEVS-C++, a high-performance environment for modeling large-scale systems at high resolution, uses the DEVS (Discrete-EVent system Specification) formalism to represent both continuous and discrete processes. A prototype suggests that the DEVS formalism can be combined with genetic algorithms running in parallel to serve as the basis of a very general, very fast class of simulation environments.

DISCRETE EVENT BASED APPROACH TO THE SIMULATION OF A CONTINUOUS NAVAL SYSTEM

Discrete Event System Specification (DEVS) is a popular formalism for modeling complex dynamic systems using a discrete-event abstraction. At this abstraction level, a timed sequence of pertinent "events" input to a system (or internal timeouts) cause instantaneous changes to the state of the system. Main advantages of DEVS are its rigorous formal definition and its support for modular composition. This tutorial introduces the Classic DEVS formalism in a bottom-up fashion, using a simple traffic light example. The syntax and operational semantics of atomic (i.e., non-hierarchical) models are introduced first. Coupled (i.e., hierarchical) models are introduced to structure and couple Atomic models. We continue to actual applications of DEVS, for example in performance analysis of queueing systems. All examples are presented with the tool PythonPDEVS, though this introduction is equally applicable to other DEVS tools. We conclude with further reading on DEVS theory, DEVS variants, and DEVS tools.

This paper presents a composable cognitive agent meta-model for multi-agent simulation based on the DEVS (Discrete Event System Specification) framework. We describe an attempt to compose a PRS-based cognitive agent by merely combining plug and play DEVS components, show how this DEVS-based cognitive agent meta-model is extensible to serve as a higher-level component for M&S of multi-agent systems, and how the agent meta-model components are reusable to ease cognitive agent modelling development. In addition to an overview of our agent meta-model, we also describe the components of the model specification and services in detail. To test the feasibility of our design, we constructed a simulation based on a Rock-Paper-Scissors game scenario. We also give out comparisons between this agent meta-model and other cognitive agent models. Our agent meta-model is novel in terms of both agent and agent components as these are all abstracted using the DEVS formalism. As different implementations of agent model components are based on the same meta-model components, all the developed agent model components can be reused in the development of other agent models which increases the composability of the agent model, and the whole cognitive agent model can be considered as a coupled model in the DEVS model hierarchy which supports multi-hierarchy modelling.

The continuously increasing demands for higher data rates is a challenge for cellular networks service providers. In recent years, wireless video has been one of the main drivers of wireless data traffic, and this kind of traffic is going to continue growing. The LTE-Advanced standard (LTE-A) introduced new methods to cope with the increasing demands, but performance is still limited. New communication algorithms that exploit Device-to-Device (D2D) communication provide promising solutions to deal with these issues. In D2D, transmission between user equipments (UEs) takes place over direct links, without going through the Base-Stations (BS). Here, we introduce an algorithm for improving the throughput of video transmission in cellular networks called the Cached and Segmented Video Download (CSVD). The algorithm splits video files into pieces that are cached in selected UEs in the cellular network, and employs D2D communication between UEs to exchange files' pieces. We have built different models and simulated the CSVD algorithm with the Discrete EVent System Specification (DEVS) formalism. The models are used to study the improvements achieved by the CSVD in terms of the cell's aggregate data rate and the average data rate per user. Simulation results show that the CSVD achieves significant improvements over the conventional transmission approaches.

Advances in electronics and connectivity have enabled a wide range of applications that can harness data collection for better decision making and an improved lifestyle. The Internet of Things (IoT) provides the communication infrastructure that allows devices with sensing and control capabilities to be connected within a home network. Smart home systems are considered one of the prominent applications in IoT, where it is possible to control home devices to achieve a better usage in terms of cost and comfort. However, smart home networks contain a wide range of devices and finding an optimal schedule for their working hours is an NP-hard problem. Hence, rather than using mathematical optimization to find optimal solutions, this paper proposes a modeling and simulation methods in order to provide good decisions and recommendations for devices’ scheduling. Discrete Event System Specification (DEVS) formalism is used to develop a model of a smart home network. The devices are categorized into two groups: monitoring devices and control devices. Monitoring devices include sensors that capture climate, energy, power, performance, and occupant’s behavioral data. Control devices send signals remotely for setting and controlling different devices in the smart home network. The behavior in terms of power usage and cost is simulated under different scenarios and settings. The simulation results show that less energy consumption can be achieved if users adopt a behavior where the schedule of three devices is changed every week. As a result, the proposed method can be utilized to make better decisions in setting devices parameters and evaluating the performance of the smart devices network under different conditions, scenarios, and settings.