Discrete Event System Specification Research Articles

Discrete event based hybrid framework for petroleum products pipeline activities classification

The importance of timely detection, classification and response to anomalies on petroleum products pipeline (PPP) have attracted pragmatic researches in recent times. There is need for efficient monitoring and detection of activities on PPP to guide leak detections and remedy decisions. This paper develops an intelligent hybrid system, driven by discrete event system specification (DEVS) and adaptive neuro-fuzzy inference system (ANFIS) for detection and classification of activities on PPP. A dataset comprising 330 records was used for training, validation and testing of the system. Result of sensitivity test shows that inlet pressure, inlet temperature, inlet volume and outlet volume have cumulative significance of 71.72% on flowrate of PPP. Hybrid learning algorithm was observed to converge faster than the back propagation algorithm in the detection of pipeline activities. ANFIS hybrid learning algorithm with training and testing errors of 0.11980 and 0.010233 yielded a correlation of 0.916 between the computed and the desired output and produced optimal consequent parameters to boost the intelligence of DEVS. A testing error of 0.0303 was observed in the evaluation of DEVS-ANFIS system on 33 test data sample, 32 precise detections were made with one incorrect detection, this gives 96.97% level of confidence in the DEVS-ANFIS model for detection, classification and localization of PPP activities.

Design and simulation of distributed IMA architectures using TTEthernet: a model-driven approach

Integrated modular avionics architectures complemented with time-triggered ethernet (TTEthernet) provides a strong platform to support the design and deployment of distributed avionic software systems. The complexity of the design and continuous integration of such systems can be managed using a model-based methodology. In this paper, we present an extension for the AADL modeling language to support modeling TTEthernet-based distributed systems. This extension consists essentially in a metamodel of the TTEthernet standard and the implementation of its corresponding concrete syntax. Furthermore, we leverage model transformations to enable undertaking the verification of the system models expressed using our AADL extension. In particular, we propose to transform the system model to a model suitable for a simulation using the discrete event system specification formalism. We illustrate the proposed approach using an example of a navigation and guidance system and we use this example to show the verification of the contention-freed property of TTEthernet schedule.

DEVS for AUTOSAR-based system deployment modeling and simulation

AUTOSAR (AUTomotive Open System ARchitecture) is an open and standardized automotive software architecture, developed by automobile manufacturers, suppliers, and tool developers. Its design is a direct consequence of the increasingly important role played by software in vehicles. As design choices during the software deployment phase have a large impact on the behavior of the system, designers need to explore various trade-offs. Examples of such design choices are the mapping of software components to processors, the priorities of tasks and messages, and buffer allocation. In this paper, we evaluate the appropriateness of DEVS, the Discrete-Event System specification, for modeling and subsequent performance evaluation of AUTOSAR-based systems. Moreover, a DEVS simulation model is constructed for AUTOSAR-based electronic control units connected by a communication bus. To aid developers in evaluating a deployment solution, the simulation model is extended with co-simulation with a plant and environment model, evaluation at different levels of detail, and fault injection. Finally, we examine how the simulation model supports the relationship between the supplier and the original equipment manufacturer in the automotive industry. We demonstrate and validate our work by means of a power window case study.

Reconsidering the performance of DEVS modeling and simulation environments using the DEVStone benchmark

The discrete event system specification formalism, which supports hierarchical and modular model composition, has been widely used to understand, analyze and develop a variety of systems. Discrete event system specification has been implemented in various languages and platforms over the years. The DEVStone benchmark was conceived to generate a set of models with varied structure and behavior, and to automate the evaluation of the performance of discrete event system specification-based simulators. However, DEVStone is still in a preliminary phase and more model analysis is required. In this paper, we revisit DEVStone introducing new equations to compute the number of events triggered. We also introduce a new benchmark with a similar central processing unit and memory requirements to the most complex benchmark in DEVStone, but with an easier implementation and with it being more manageable analytically. Finally, we compare both the performance and memory footprint of five different discrete event system specification simulators in two different hardware platforms.

Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR

In the context of the modeling and simulation of neural nets, we formulate definitions for the behavioral realization of memoryless functions. The definitions of realization are substantively different for deterministic and stochastic systems constructed of neuron-inspired components. In contrast to earlier generations of neural net models, third generation spiking neural nets exhibit important temporal and dynamic properties, and random neural nets provide alternative probabilistic approaches. Our definitions of realization are based on the Discrete Event System Specification (DEVS) formalism that fundamentally include temporal and probabilistic characteristics of neuron system inputs, state, and outputs. The realizations that we construct—in particular for the Exclusive Or (XOR) logic gate—provide insight into the temporal and probabilistic characteristics that real neural systems might display. Our results provide a solid system-theoretical foundation and simulation modeling framework for the high-performance computational support of such applications.

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

Naval ships are assigned many and varied missions. Their performance is critical for mission success, and depends on the specifications of the components. This is why performance analyses of naval ships are required at the initial design stage. Since the design and construction of naval ships take a very long time and incurs a huge cost, Modeling and Simulation (M & S) is an effective method for performance analyses. Thus in this study, a simulation core is proposed to analyze the performance of naval ships considering their specifications. This simulation core can perform the engineering level of simulations, considering the mathematical models for naval ships, such as maneuvering equations and passive sonar equations. Also, the simulation models of the simulation core follow Discrete EVent system Specification (DEVS) and Discrete Time System Specification (DTSS) formalisms, so that simulations can progress over discrete events and discrete times. In addition, applying DEVS and DTSS formalisms makes the structure of simulation models flexible and reusable. To verify the applicability of this simulation core, such a simulation core was applied to simulations for the performance analyses of a submarine in an Anti-SUrface Warfare (ASUW) mission. These simulations were composed of two scenarios. The first scenario of submarine diving carried out maneuvering performance analysis by analyzing the pitch angle variation and depth variation of the submarine over time. The second scenario of submarine detection carried out detection performance analysis by analyzing how well the sonar of the submarine resolves adjacent targets. The results of these simulations ensure that the simulation core of this study could be applied to the performance analyses of naval ships considering their specifications.

Combining DEVS and model-checking: concepts and tools for integrating simulation and analysis

Our objectives here are to discuss the development of a formal framework that exploits the advantages of the discrete event system specification (DEVS) formalism and builds upon recent extensive work on verification combining DEVS and model checking for hybrid systems. DEVS offers the ability, via mathematical transformations called system morphisms, to map a system expressed in a formalism suitable for analysis (e.g., timed automata or hybrid automata) into the DEVS formalism for the purpose of simulation. We discuss a probabilistic extension of the FD-DEVS formalism that enables a set of model classes and tools derived from Markov-type models. The MS4 modelling environment provides a suite of tools that support this extension, called FP-DEVS. In this paper, we describe these tools and the concepts underlying them. We also provide examples of application of these concepts and discuss the open opportunities for research in this direction.

퍼지와 DEVS를 이용한 선박 충돌 위험 예측 모델 설계

선박에 현대화된 다양한 항해장비들이 설치됨에도 불구하고 여전히 해양사고가 자주 일어나는데, 이런 사고의 주요 형태 중 하나가 충돌 사고이다. 우리나라 해양사고의 약 1/4이 충돌에 의한 사고이고, 이 중 대부분이 인적오류가 원인인 것으로 알려져 있다. 따라서, 항해사의 의사결정을 도울 수 있는 지능적인 지원 도구가 필요한데, 이와 관련하여 충돌위험을 추정하는 다양한 방식들이 꾸준히 소개되어 왔으며 충돌위험 상황에 대해 사람에게 친숙한 언어적 표현을 반영하여 추론하기 위해 퍼지를 활용한 연구 결과들이 많다. 이런 기존 연구들의 충돌위험도는 현재시점에서 선박들의 속도나 방향 상태가 유지되는 것을 기준으로 충돌위험도를 추정한다. 그러나, 실제 선박에서는 충분히 피항 가능 상황임에도 불구하고 충돌 위험으로 판단되어 잦은 경고를 울리는 시스템들에 대해 항해사들이 느끼는 불편함이 적지 않아 보조 장치들의 알람 기능을 꺼놓은 경우도 많은 것으로 알려져 있다. 이 연구는 선박들의 일반적인 피항 패턴을 반영한 가까운 미래 시점의 충돌위험도 예측에 관한 것으로서, 퍼지추론과 DEVS 형식론에 기반한 충돌 위험 예측 모델을 제안한다. Even thought modernized marine navigation devices help navigators, marine accidents has been often occurred and ship collision is one of the main types of the accidents. Various studies on the assessment method of collision risk have been reported, and studies using fuzzy theory are remarkable for the reason that reflect linguistic and ambiguous criteria for real situations. In these studies, collision risks were assessed on the assumption that the current state of navigation ship would be maintained. However, navigators ignore or turn off frequent alarms caused by the devices predicting collision risk, because they think that they can avoid the collisions in the most of situations. This paper proposes a model of predicting ship collision risk considering the general patterns of collision avoidance, and the approach is based on fuzzy inference and discrete event system specification (DEVS) formalism.

DIMZAL: A Software Tool to Compute Acceptable Safety Distance

The scope of this work is to present a multidisciplinary study in order to propose a tool called DIMZAL. DIMZAL forecasts fuelbreak safety zone sizes. To evaluate a safety zone and to prevent injury, the Acceptable Safety Distance (ASD) between the fire and firefighters is required. This distance is usually set thanks to a general rule-of-thumb: it should be at least 4 times the maximum flame length. A common assumption considers an empirical relationship between fireline intensity and flame length. In the current work which follows on from an oral presentation held at the VII International Conference on Forest Fire Research in Coimbra in 2014, an alternative way is proposed: a closed physical model is applied in order to quantize the ASD. This model is integrated in a software tool, which uses a simulation framework based on Discrete EVent system Specification formalism (DEVS), a 3D physical real-time model of surface fires developed at the University of Corsica and a mobile application based on a Google SDK to display the results

Emergence at the Fundamental Systems Level: Existence Conditions for Iterative Specifications

Conditions under which compositions of component systems form a well-defined system-of-systems are here formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: (1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state; and (2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and, if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

Multi-resolution modeling based on quotient space and DEVS

As simulation systems get more and more complex, the study of multi-resolution modeling (MRM) remains an exciting and fertile area of research. Contrasting its abundant successful use cases, a rigorous mathematical foundation is still lacking in MRM. In this paper, we propose a quotient space based multi-resolution modeling (QMRM) theory based on granular computing in artificial intelligence and on discrete-event system specification (DEVS) in modeling and simulation. Based on quotient sets, resolution, multi-resolution modeling and other related concepts are defined and a general concept framework is constructed. Based on the concepts of quotient set and natural projection, several MRM principles are derived. The internal consistency principle guarantees consistency among different perspectives of an atomic model, whereas the external consistency principle guarantees that different components in a coupled model are consistent. The false-preserving principle indicates that if a construction relation or state transformation relation of a component does not exist in a low resolution model, then the corresponding relations should not exist in its high resolution model. The true-preserving principle tells us that a high resolution model can be simplified by choosing the proper low resolution model. QMRM is not only a formal specification, but also a fundamental framework to understand MRM concepts, a guiding ideology to design specific MRM methods, and a modeling methodology to develop MRM systems. QMRM is created from a general simulation perspective, not limited by any specific application or problem domain aspects. The results of this paper can serve as a starting point for further study of multi-resolution problems in different domains.

Operational Semantics of a Discrete Event System Specification Subclass for Reachability Analysis

Operational Semantics of a Discrete Event System Specification Subclass for Reachability Analysis

MODELING AND SIMULATION OF THRESHOLD ANALYSIS FOR PVFS IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) suffer serious damage from false positive and negative attacks due to their hardware restrictions. The sensor network causes both unnecessary energy consumption and information loss through false reports and normal reports, which include false message authentication codes (MACs). A probabilistic voting-based filtering scheme (PVFS) effectively detects the two types of attacks through a pre-defined threshold, which is the number of detected false MACs in a report. Since the threshold significantly influences the ability to detect attacks, the sensor network should be simulated to ensure proper function. In this paper, we describe the development and simulation of a PVFS-based WSN using a discrete event system specification. The experimental results showed that PVFS with a threshold of 2 reduced energy usage by about 16% and improved the detected false reports as compared with a PVFS with a threshold of 3.

System entity structure extension to integrate abstraction hierarchies and time granularity into DEVS modeling and simulation

The modeling and simulation (M&S) of complex systems often requires models described at different levels of detail characterized by differences in abstraction hierarchies and/or time granularity. The discrete-event system specification (DEVS) is a framework based on mathematical systems theory that offers a computational basis for application of M&S to systems engineering and that has become widely adopted for its support of discrete-event, continuous, and hybrid applications. A fundamental representation of DEVS hierarchical modular model structures is the system entity structure (SES), which represents a design space via the elements of a system and their relationships in a hierarchical and axiomatic manner. As has been described in a number of publications, the SES supports development, pruning, and generation of DEVS simulation models. The goal of this paper is to propose an extension of SES in order to integrate both the concepts of abstraction hierarchies and time granularity into DEVS. This paper explains in detail: (i) the concepts of abstraction hierarchies and time granularity; (ii) the extension of SES in order to take into account these concepts; (iii) DEVS M&S of complex systems according to different levels of detail (abstraction hierarchies and time granularity); (iv) the use of a Python DEVS simulator (DEVSimPy) to implement the management of abstraction hierarchies and time granularity. A real case study is given to illustrate the proposed approach, and follow-on research needed to implement the concepts is discussed.

Emergency crowd evacuation modeling and simulation framework with cellular discrete event systems

First responders and security personnel face many challenges to safely evacuate crowded environments. Conducting frequent physical and table-top exercises are neither feasible nor economical. This is where modeling and simulation comes into play in providing a risk-free and economical method to practice various evacuation strategies, train first responders, and provide accurate decision-support and emergency guidance. With the aid of formal methods, here we present a suite of various egress strategies build on top of an open-source modeling and simulation environment. We use Cellular Discrete-Event System Specification (Cell-DEVS) formalism to explore emergency evacuation scenarios by building 12 egress models representing aspects of human behaviors under emergencies and the activities of authorities in guiding the crowd. The proposed framework explores random and controlled human movement, as well as implementing psychological conditions such as herd following and panicked states. We provide results analysis to compare evacuation speed under various egress methods, allowing for decision making when using the suite for training purposes. The outcome of this work is available on a public repository to serve the DEVS community, researchers, and public safety authorities interested in emergency evacuation simulations.

Enhancing the reusability and interoperability of artificial neural networks with DEVS modeling and simulation

Artificial neural networks (ANNs), a branch of artificial intelligence, has become a very interesting domain since the eighties when back-propagation (BP) learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear problems. It is used extensively to solve complex nonalgorithmic problems such as prediction, pattern recognition and clustering. However, in the context of a holistic study, there may be a need to integrate ANN with other models developed in various paradigms to solve a problem. In this paper, we suggest discrete event system specification (DEVS) be used as a model of computation (MoC) to make ANN models interoperable with other models (since all discrete event models can be expressed in DEVS, and continuous models can be approximated by DEVS). By combining ANN and DEVS, we can model the complex configuration of ANNs and express its internal workings. Therefore, we are extending the DEVS-based ANN proposed by Toma et al. [A new DEVS-based generic artficial neural network modeling approach, The 23rd European Modeling and Simulation Symp. (Simulation in Industry), Rome, Italy, 2011] for comparing multiple configuration parameters and learning algorithms and also to do prediction. The DEVS models are described using the high level language for system specification (HiLLS), [Maïga et al., A new approach to modeling dynamic structure systems, The 29th European Modeling and Simulation Symp. (Simulation in Industry), Leicester, United Kingdom, 2015] a graphical modeling language for clarity. The developed platform is a tool to transform ANN models into DEVS computational models, making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation (MAS).

Discrete Event Modeling and Simulation-Driven Engineering for the ATLAS Data Acquisition Network

The authors present an iterative and incremental development methodology for simulation models in network engineering projects. Driven by the DEVS (Discrete Event Systems Specification) formal framework for modeling and simulation, they aim to assist network design, test, analysis, and optimization processes. A practical application of the methodology is presented for a case study in the data acquisition system of the ATLAS particle physics experiment at CERN's Large Hadron Collider at CERN. By adopting the DEVS MaS formal framework in combination with software engineering best practices, the authors develop network simulation models along with enhanced modeling capabilities and boosted simulation performance for tools in a robust yet flexible way.

Turbulence and fire-spotting effects into wild-land fire simulators

This paper presents a mathematical approach to model the effects and the role of phenomena with random nature such as turbulence and fire-spotting into the existing wildfire simulators. The formulation proposes that the propagation of the fire-front is the sum of a drifting component (obtained from an existing wildfire simulator without turbulence and fire-spotting) and a random fluctuating component. The modelling of the random effects is embodied in a probability density function accounting for the fluctuations around the fire perimeter which is given by the drifting component. In past, this formulation has been applied to include these random effects into a wildfire simulator based on an Eulerian moving interface method, namely the Level Set Method (LSM), but in this paper the same formulation is adapted for a wildfire simulator based on a Lagrangian front tracking technique, namely the Discrete Event System Specification (DEVS). The main highlight of the present study is the comparison of the performance of a Lagrangian and an Eulerian moving interface method when applied to wild-land fire propagation. Simple idealised numerical experiments are used to investigate the potential applicability of the proposed formulation to DEVS and to compare its behaviour with respect to the LSM. The results show that DEVS based wildfire propagation model qualitatively improves its performance (e.g., reproducing flank and back fire, increase in fire spread due to pre-heating of the fuel by hot air and firebrands, fire propagation across no fuel zones, secondary fire generation, ...) when random effects are included according to the present formulation. The performance of DEVS and LSM based wildfire models is comparable and the only differences which arise among the two are due to the differences in the geometrical construction of the direction of propagation. Though the results presented here are devoid of any validation exercise and provide only a proof of concept, they show a strong inclination towards an intended operational use. The existing LSM or DEVS based operational simulators like WRF-SFIRE and ForeFire respectively can serve as an ideal basis for the same.

The high level language for system specification: A model-driven approach to systems engineering

We present HiLLS (High Level Language for System Specification), a graphical formalism that allows to specify Discrete Event System (DES) models for analysis using methodologies like simulation, formal methods and enactment. HiLLS’ syntax is built from the integration of concepts from System Theory and Software Engineering aided by simple concrete notations to describe the structural and behavioral aspects of DESs. This paper provides the syntax of HiLLS and its simulation semantics which is based on the Discrete Event System Specification (DEVS) formalism. From DEVS-based Modeling and Simulation (M&S) perspective, HiLLS is a platform-independent visual language with generic expressions that can serve as a front-end for most existing DEVS-based simulation environments with the aid of Model-Driven Engineering (MDE) techniques. It also suggests ways to fill some gaps in existing DEVS-based visual formalisms that inhibit complete specification of the behavior of complex DESs. We provide a case study to illustrate the core features of the language.

Building a hybrid DEVS and GRASS model using a composable cellular automaton

Modeling and simulation is pervasive throughout many different disciplines. As computing technology has provided more capability, the systems being modeled and simulated have grown larger and more complex. Often times, these large systems are managed as interacting subsystems. When it is necessary for the simulation to allow disparate subsystems to maintain their independence, then a hybrid model of the subsystems should be used. Furthermore, to ease the burden of verification and validation of simulation results, a proven system theoretical modeling specification should be used. However, many communities have already adopted nonsystem theoretical software solutions and established a group of domain experts familiar with these tools. This paper provides two things: a formal approach to building a hybrid model, and a discussion of how to incorporate a nonsystem theoretical software implementation into a proven framework. The first is done through the implementation of a Knowledge Interchange Broker (KIB) as an Interaction Model (IM). The second is accomplished by exemplifying the use of the IM in an agent-environment hybrid model. In the hybrid model, the agent is implemented in the Discrete-event System (DEVS) specification and the environment is implemented in the Geographical Resources Analysis Support System (GRASS) using a Composable Cellular Automaton (CCA) specification. This concept has been successfully applied to both example models and an interdisciplinary research project where the interactions between human activities and landscape processes are studied.