DEVS Models Research Articles

A Mathematical Model for Dynamic Electric Vehicles: Analysis and Optimization

In this article, we introduce a flexible and reliable technique to simulate and optimize the characteristics of a Dynamic Electrical Vehicle (DEV). The DEV model is a discrete event-based modeling technique used in electrical vehicle research to improve the effectiveness and performance of various electrical vehicles (EVs) components. Here, the DEVS model is applied to EV research in several ways, including battery management optimization, evaluation of power train design and control strategy, and driver behavior analysis. The essential power train elements, including the battery, motor, generator, internal combustion engine, and power electronics are included in the mathematical model for the dynamic electric vehicle. The model is derived using the conservation of energy principle. The model includes mathematical equations for the electrical power output, battery charge level, motor torque, motor power output, generator power output, internal combustion engine torque, mechanical power delivered to the generator, and the efficiencies of the power electronics, motor, generator, and engine. The model is examined by using a numerical method called the Runge–Kutta Method of order 4 for dynamic electric vehicle’s performance under various driving states for maximum effectiveness and performance. It is revealed that the DEV model provides a systematic method to simulate and optimize the behavior of complex EV systems.

Speed Optimization in DEVS-Based Simulations: A Memoization Approach

The DEVS model, designed for general discrete event simulation, explores the event status and time advance of all DEVS atomic models deployed at the time of the simulation, and then performs the scheduled simulation step. Each simulation step is accompanied by a re-exploration the event status and time advance, which is needed for maintaining the casual order of the entire model. It is time consuming to simulate a large-scale DEVS model. In a similar vein, attempts to perform an HDL simulation in a DEVS space increase simulation costs by incurring repeated search costs for model transitions. In this study, we performed a statistical analysis of engine behavior to improve simulation speed and we proposed a DP-based memoization technique for the coupled model. Through our method, we can expect significant performance improvements that range statistically from 7.4 to 11.7 times.

Extending the Hierarchy of System Specifications and Morphisms with SES Abstraction

This article works toward a unification of two related concepts that underpin system-theory-based modeling and simulation–the hierarchy of system specifications and morphisms and the System Entity Structure (SES). The hierarchy organizes system specification along levels ranging from behavior to structure capturing increasing knowledge of the system input/output processing and state dynamics. The SES is a constructive ontology describing compositions of modular components via coupling of input/output ports. Toward unification of these concepts, we propose an abstraction of the SES called the MetaSES that supports the construction of complex systems of systems with multiple components belonging to specified classes. Moreover, we place the MetaSES within a computational framework with the goal of making it easier to design and build complex hierarchical DEVS models and to communicate their structures and intended behaviors to foster continued reuse and development. We discuss several examples of applications to illustrate how the MetaSES-based enhancement of the hierarchy of system specifications and morphisms helps to push the boundaries of complexity management in the theory and practice of modeling and simulation. Research directions stemming from the proposed concepts are suggested.

A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects

A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects

A grid-shaped cellular modeling approach for wireless sensor networks.

WSN (Wireless Sensor Network) applications have been widely used in recent years. We introduce a new method for modeling WSN, based on the specification of the WSN using the Cell-Discrete-Event Systems Specification (DEVS) formalism: the space is partitioned into cells where each cell can be considered a sensor, an obstacle, or anything of a behavior with defined rules. This model is then converted automatically into DEVS model at runtime. We present two case studies analyzing the use of energy in WSN member nodes, which have impact on prolonging the overall network lifetime. We study to analyze energy consumption related to routing and data transmission at the node level, and topology residual energy control methods at the cluster level (i.e. group of sensors) level. The goal is to show how these spatial modeling methods can be used for building WSN models in a simple but efficient fashion.

On consistency of physical and DEVS models in control-targeted DTs: an industrial case study

On consistency of physical and DEVS models in control-targeted DTs: an industrial case study

DEVS-Based Building Blocks and Architectural Patterns for Intelligent Hybrid Cyberphysical System Design

The DEVS formalism has been recognized to support generic open architectures that allow incorporating multiple engineering domains within integrated simulation models. What is missing for accelerated adoption of DEVS-based methodology for intelligent cyberphysical system design is a set of building blocks and architectural patterns that can be replicated and reused in system development. As a start in this direction, this paper offers a notional architecture for intelligent hybrid cyberphysical system design and proceeds to focus on the decision layer to consider DEVS models for basic behaviors such as choice of alternatives, perception of temporal event relations, and recognition and generation of finite state languages cast into DEVS time segments. We proceed to describe a methodology to define DEVS-based building blocks and architectural patterns for design of systems employing fast, frugal, and accurate heuristics. We identify some elements of this kind and establish their status as minimal realizations of their defined behaviors. As minimal realizations such designs must ipso facto underlie any implementation of the same cognitive behaviors. We discuss architectures drawn from the cognitive science literature to show that the fundamental elements drawn from the fast, frugal, and accurate paradigm provide insights into intelligent hybrid cyberphysical system design. We close with open questions and research needed to confirm the proposed concepts.

GO-DEVS: Storage and Retrieval System for DEVS Models Using Graph and Ontology Representation.

DEVS is a powerful formal language to describe discrete event systems in modeling and simulation areas and useful for component-based design. One of the advantages of component-based design is reusability. To reuse or share DEVS models developed by many other modelers, a system to systematically store and retrieve many DEVS models should be supported. However, to the best of our knowledge, there does not exist such a system. In this paper, we propose GO-DEVS (Graph/Ontology-represented DEVS storage and retrieval system) to store and retrieve DEVS models using graph and ontology representation. For effective model sharing, an ontology is introduced when a DEVS model is developed. To search for DEVS models in an effective and efficient way, we propose two types of queries, IO query and structure query, and provide a method to store and query DEVS models on an RDBMS. Finally, we experimentally show GO-DEVS can process the queries efficiently.

SLLB-DEVS: An Approach for DEVS Based Modeling of Semiconductor Lithography Load Balance

In industrial applications, software related to computational lithography using a DP system method, which refers to how efficiently hardware resources are used, has a significant impact on performance. Because the amount of data to be processed per unit of time is comparatively large in the current semiconductor industry, the efficiency of hardware should be increased through job 12 scheduling by using the most efficient load balancing techniques possible. For efficient scheduling of the load balancer, these are necessary to predict the end time of a given job; this is calculated based on the performance of computing resources and the development of effective traffic distribution algorithms. Due to the high integration of semiconductor chips, the volume of mask exposure data has increased exponentially, the number of slave nodes is increasing, and most EDA tools require one license per DP node to perform a simulation. In this paper, in order to improve efficiency and reduce cost through more efficient load balancing scheduling, a new type of DEVS load balancing method was studied based on the existing industrial E-beam cluster model. The designed DEVS model showed up to four times the throughput of the existing legacy model for medium and large clusters when the BSF policy was applied.

Optimal strategies for a defined contribution pension plan with SAHARA utility under DEV model

This paper investigates the optimal investment problems for a defined contribution pension plan in an incomplete financial market that consists of a risk-free asset anda risky asset whose price process obeys the dynamic elasticity of variance model and a correlated inflation risk model. The pension plan participant aims to maximize the expected utility of the terminal wealth under the symmetric asymptotic hyperbolic absolute risk aversion utility. This paper derives the Hamilton-Jacobi-Bellman equations and the verification results, develops lower and upper bounds for the value function, and computes optimal strategies by applying the dual control Monte Carlo algorithm. Numerical examples are presented to verify the accuracy of the approach.

Building DEVS Models from the Functional Design of Software Architecture Components to Estimate Quality

Building DEVS Models from the Functional Design of Software Architecture Components to Estimate Quality

Developmental score of the infant brain: characterizing diffusion MRI in term- and preterm-born infants.

Large-scale longitudinal neuroimaging studies of the infant brain allow us to map the spatiotemporal development of the brain in its early phase. While the postmenstrual age (PMA) is commonly used as a time index to analyze longitudinal MRI data, the nonlinear relationship between PMA and MRI data imposes challenges for downstream analyses. We propose a mathematical model that provides a Developmental Score (DevS) as a data-driven time index to characterize the brain development based on MRI features. 319 diffusion tensor imaging (DTI) datasets were collected from 87 term-born and 66 preterm-born infants at multiple visits, which were automatically segmented based on the JHU neonatal atlas. The mean diffusivity (MD) and fractional anisotropy (FA) in 126 brain parcels were used in the model to derive DevS. We demonstrate that transforming the time index from PMA to DevS improves the linearity of the longitudinal changes in MD and FA in both gray and white matter structures. More importantly, regional developmental differences in DTI metrics between preterm- and term-born infants were identified more clearly using DevS, e.g. 79 structures showed significantly different regression patterns in MD between preterm- and term-born infants, compared to only 27 structures that showed group differences using PMA as the index. Therefore, the DevS model facilitates linear analyses of DTI metrics in the infant brain, and provides a useful tool to characterize altered brain development due to preterm-birth.

A discrete event model for analysis and verification of time‐constrained business processes

SummaryIn the purpose of analyzing and checking time constraints in business processes, we propose to model and simulate them in the “discrete event system specification” (DEVS) formalism. The proposed model will contain atomic DEVS models representing the activities, supervised by, other DEVS control models, and based on one to one mapping. Hence, guaranteeing a close concordance with the temporal requirements. Thus, the simulation of the model results in outputs about the successful execution regarding to the temporal constraints soundness. Therefore, the modeler will be able to predict unsuitable temporal behavior. Thus, the model responds to the lack of modeling tools that do not offer enough formal specification of time constraints at the earlier phases of design as in, the standard “business process management notation.” The proposed model handles different type of time constraints in business process such as intra‐activities, inter‐activities, and inter‐process time constraints. The proposed model is implemented into JAVADEVS using DEVS‐Suite platform.

Discrete Event Systems Specifications Modelling and Simulation of Wireless Networking Applications

ABSTRACT We discuss the use of formal discrete-event modelling and simulation for networking applications, in particular, for mobile networks and Wireless Sensor Networks (WSN). We show how one can develop discrete-event model libraries based on the DEVS formalism for mobile networks, allowing tracking the users’ upload status in a given area of coverage, while using a non-cooperative algorithm or Coordinated Multipoint (CoMP) synchronisation. We show a DEVS model library for CoMP-based networks, showing how control messaging can be managed among Evolved Node Base stations (eNBs). We build a case study comparing two CoMP approaches: Joint Processing (JP) and Coordinated Scheduling (CS). Finally, we show how DEVS models can be used to model malware propagation in wireless sensor networks based on epidemic theory.

XML-based DEVS modelling and simulation tracking

XML-based DEVS modelling and simulation tracking

Why we should use Min Max DEVS for modeling and simulation of digital circuits

The delay is a very important element in modeling hardware behavior, and is realized in many hardware description languages such as ADLIB-SABLE, Verilog, and VHDL. The state of the art on hardware delay identifies four classes. In the first class, mean values are used as a precise delay element in the simulation; we found it in VHDL (VHSIC (very high speed integrated circuit) Hardware Description Language), where a single value is utilized to characterize the transport delay. In the second class, the delay is represented by an interval min max, meaning that the delay value is precisely unknown and every value in the interval can represent a possible value for the actual delay. In the third class, a delay is expressed in the form of a stochastic distribution. Fuzzy models of delay constitute the last class. In reality, it is very difficult, if not impossible, to obtain a precise value of the delay; there are many reasons for that: temperature, voltage, variation in the manufacturing process, and other environment parameters. The Min Max DEVS formalism allows an efficient design of the min max delay by proposing a definition of the lifetime function based on time interval. Moreover, its simulation semantics allows the simulation of Min Max DEVS models with only one replication, allowing us to conclude whether the min max delay is too large or exact simulations cannot be obtained. In this paper, we propose to highlight the Min Max DEVS formalism through examples from digital circuits, after having recalled its basic definitions and its simulation semantics. Then, we compare the simulation results obtained with those provided by the well-known tool in the field of digital circuits, Verilog, using the same examples.

Activity-based DEVS modeling

Use of model-driven approaches has been increasing to significantly benefit the process of building complex systems. Recently, an approach for specifying model behavior using UML activities has been devised to support the creation of DEVS models in a disciplined manner based on the model driven architecture and the UML concepts. In this paper, we further this work by grounding Activity-based DEVS modeling and developing a fully-fledged modeling engine to demonstrate applicability. We also detail the relevant aspects of the created metamodel in terms of modeling and simulation. A significant number of the artifacts of the UML 2.5 activities and actions, from the vantage point of DEVS behavioral modeling, is covered in details. Their semantics are discussed to the extent of time-accurate requirements for simulation. We characterize them in correspondence with the specification of the atomic model behavior. We demonstrate the approach with simple, yet expressive DEVS models.

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.

Debugging Parallel DEVS

To this day, debugging support for the DEVS formalism has been provided, at best, in an ad-hoc way. The intricacies of dealing with the interplay of different notions of (simulated) time, formalism semantics, and user input have not been thoroughly investigated. This paper presents a visual modeling, simulation, and debugging environment for Parallel DEVS, which builds on a theoretical foundation for debugging DEVS models. We take inspiration from both code debugging and the simulation world to model our environment; we transpose a set of useful code debugging concepts onto Parallel DEVS, and combine those with simulation-specific operations, such as as-fast-as-possible simulation and (scaled) real-time execution. Apart from these common debugging operations, we introduce new features to the debugging of Parallel DEVS models, such as “god events,” which can alter the model state during simulation, and reversible debugging, which allows one to go back in time. To achieve this, the PythonPDEVS simulator is deconstructed and reconstructed: the modal part of the simulator–debugger, as well as the debugging operations, are modeled using the Statecharts formalism. These models are combined, resulting in a model of the timed, reactive behavior of a debuggable simulator for Parallel DEVS. The code for the simulator is automatically synthesized from this model. To improve usability, we combine the simulator with a visual modeling environment, allowing for visual and interactive live debugging.

DEVS modelling and simulation of human social interaction and influence

The social influence is at the centre of consideration in social science. In industrial engineering, although the enterprise has reached the age of the electronic communication, the human direct communication is not sufficiently considered even if it remains critical communication vector to transmit information. The idea is to predict some human attributes behaviour that will help enterprise to make efficient decision. The research in the domain gives significant results but the impact of information on individuals within a social network is, mostly, statically modelled where the dynamic aspect is not frequently tackled. The individual׳s reaction to a change within an organisation or ecosystem (implementation of a new system, new security instructions…etc.) is not always rationale. The opinion of individuals is influenced by information gathered about the attributes of the technology from other members of their social network. In addition, the works about modelling and simulation of the population’s reactions to an event do not use explicit specification languages to support their models. A behavioural specification model is one critical missing link. Adding a clear behavioural model can help for specification verification and reuse. From literature, the DEVS formalism (Discrete EVent system Specifications) appears to be general enough to represent such dynamical systems (Zeigler et al., 2000). It provides operational semantics applicable to this domain. The contributions of this work are dynamic models of individuals using low-level language to simulate the propagation of information among a group of individuals and its influence on their behaviour. In more detail, we define a set of models of individuals characterized by a set of state variables and the mesh between the individuals within a social network. Then, we introduce the information diffusion based on epidemic spreading algorithms and we transpose them into the case of the message propagation in a social network. Finally, a basic scenario is used to give a beginning of validation to our models using a platform based on DEVS formalism.