Component-based light-rail modeling in discrete event systems specification (DEVS)

Huang, Yilin; Seck, Mamadou; Verbraeck, Alexander

doi:10.1177/0037549715614652

Cited by 9 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Δt is the time interval of DA. For example, if Δt = 5 then σ is set to be [0, 5,10,15] in the experiments depending on the corresponding error levels. In addition, σ arr and σ dep are independent to each other in the experiments.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Cho

Huang

Verbraeck

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Dynamic data-driven simulation (DDDS) incorporates realtime measurement data to improve simulation models during model runtime. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the level of actual and perceived measurement errors (or noises), and provide recommendations on how to strategically use data assimilation for DDDS considering these conditions. The results show that the estimation accuracy in DA is more constrained by the choice of time intervals than the number of particles. Good accuracy can be achieved without many particles if the time interval is sufficiently short. An over estimation of the level of measurement errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Simulation modeling has been widely used for studying complex systems [10][11][12]. In a highly evolving environment, classical simulation shows limitations in situational awareness and adaptation [8,9].…”

Section: Introductionmentioning

confidence: 99%

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Cho

Huang

Verbraeck

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…The more available data offer new opportunities to complement and empower traditional simulation modeling approaches which have limitations in situational awareness and adaptation in a highly evolving socio-technical environment. 9,10,30,31 In this context, dynamic data-driven simulation can be explored in several directions, e.g., to automatically generate model structure aggregating predefined model components; 32,33 to discover simulation models and their generative behaviors in an automated or semi automated way; 34 to assimilate real-time data into (online) simulation to support real-time decision-making. 10,35 There is a rich body of knowledge with which experts from different domains using diverse simulation modeling paradigms and methods can collaborate and learn from one another.…”

Section: Da For Discrete Systemsmentioning

confidence: 99%

Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditions

Huang

Xie

Cho³

et al. 2022

SIMULATION

Self Cite

View full text Add to dashboard Cite

Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the particle filter (PF) method is often a good option for stochastic simulation models of discrete systems. The probability distributions of model runs, however, make it computationally intensive. The experimental conditions therein are understudied. This paper studied three critical conditions of PF-based DA in a discrete event model: (1) the time interval between two consecutive DA iterations, (2) the number of particles, and (3) the actual level and perceived level of measurement errors (or noises). The study conducted identical-twin experiments of an M/M/1 single server queuing system. The ground truth is imitated in a stand-alone simulation model. The measurement errors are superimposed so that the effect of the three conditions can be quantitatively evaluated in a controlled manner. The results show that the estimation accuracy of such a system using PF is more constrained by the choice of time intervals than the number of particles. An under estimation of measurement errors produces worse state estimates than an over estimation of errors. A correct perception of the measurement errors does not guarantee better state estimates. Moreover, a slight over estimation of errors results in better state estimates, and it is more responsive to abrupt system changes than an accurate perception of measurement errors.

show abstract

“…This way of modeling has proven to be successful for software-engineering applications (Crnkovic 2001). The advantages of component-based modeling are observed to be useful for modeling for supervisor synthesis as well, as discussed in Kovács and Piétrac (2009), Kovács et al (2012), and Huang et al (2015). However, they do not discuss how these component models can be obtained for other applications.…”

Section: Component-based Modelingmentioning

confidence: 99%

Modeling for supervisor synthesis – a lock-bridge combination case study

Reijnen

Goorden

Mortel-Fronczak

et al. 2020

Discrete Event Dyn Syst

View full text Add to dashboard Cite

Designing supervisory controllers for high-tech systems is becoming increasingly complex due to demands for verified safety, higher quality and availability, and extending functionality. Supervisor synthesis is a method to automatically derive a supervisor from a model of the plant and a model of the control requirements. While supervisor synthesis is an active research topic, only a few reports exist on industrial applications. One of the reasons for this is the lack of acquaintance of control engineers with modeling and specifying in the framework of automata. In addition to this, there are no clear guidelines for obtaining the necessary models for synthesis. In this paper, we describe a general way of modeling for the plant and the requirements in order to contribute towards the acceptance of supervisor synthesis in industry. This way of modeling is illustrated with an industrial case study in which a supervisory controller is synthesized for the Algera complex. The Algera complex consists of a waterway lock and a movable bascule bridge. The supervisor has to control 80 actuators based on the observations from 96 discrete sensors, in response to 63 control commands available from the operator. We show how to model the plant as a collection of extended finite-state automata, how to model the requirement as a collection of event conditions, how to synthesize the monolithic supervisor, and how to validate the resulting supervisor using continuous-time simulation.

show abstract

Component-based light-rail modeling in discrete event systems specification (DEVS)

Cited by 9 publications

References 43 publications

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditions

Modeling for supervisor synthesis – a lock-bridge combination case study

Contact Info

Product

Resources

About