Discrete Event Systems Research Articles

Event Driven Phase-Locked Loop with Application to Atomic Force Microscopy Oscillation Non-Resonant Modes

Traditional PLLs are circuits synchronizing a generated periodic, usually harmonic, signal with a reference input signal in frequency (period) and phase. They are sophisticated, usually expensive, nonlinear control systems with difficult to achieve stability. Traditional PLLs are intensively used in various applications including RF communications, mechatronics, AFM (Atomic Force Microscopy) resonant modes, etc. However, similar synchronization for lower frequencies can be achieved by significantly simpler control methods and schematics. One such new method, called EDriPLL (Event Driven PLL), is the subject of this paper. The idea is to design a discrete event system that receives and generates events at the start of a new (maybe variable) period of the reference signal. Thus, EDriPLL design belongs to the discrete event systems field rather than electronic signal schematics. It can be applied, for instance, when synchronizing a team of robots with subordinate robots receiving sync events with transition noise from the leader; they estimate actual time of the sending events and synchronize themselves with the leader at high frequency. The application illustrated in this paper is estimation of times of events when the AFM tip (periodically) begins to move closer to the sample in the ONR (Oscillation Non-Resonant) mode. ONR mode allows mapping of force curves and related material properties of the sample in real time. High throughput LabVIEW FPGA implementation of EDriPLL and an illustration of an ONR AFM mode synchronized by EDriPLL are presented. There are many other applications of this technique in mechatronics and robotics. It may also be used in human-machine interactions.

Opacity Enforcement in Discrete Event Systems Using Modification Functions

Opacity Enforcement in Discrete Event Systems Using Modification Functions

Colored Petri Nets for Modeling and Simulation of a Green Supply Chain System

Green supply chain is an emerging approach in supply chain management to reduce environmental impact of the process concerning the flow of goods and materials. As a discrete-event system, supply chain can be modeled using Petri Nets. Colored Petri Nets (CPNs) extend the classical Petri net formalism with data, time and hierarchy. These extensions makes it possible to deal with the green aspects of the supply chain.This paper deals with the Colored Petri Net approach to model and simulate a green supply chain system. The forward supply chain network starts from the raw material suppliers, to the manufacturers, wholesalers, retailers and to the final customers. The reverse supply chain network is made of collecting points, recycling plants, disassembly plants, and secondary material market. A government environment agency plays the role of regulation of the recycling process. A colored Petri net model of the green supply chain is developed and simulated with CPN Tools, a dedicated software for CPN models. The simulation results as well as the state-space analysis results validate the correctness of the model.

A Uniform Framework for Diagnosis of Discrete-Event Systems With Unreliable Sensors Using Linear Temporal Logic

In this paper, we investigate the diagnosability verification problem of partially-observed discrete-event systems (DES) subject to unreliable sensors. In this setting, upon the occurrence of each event, the sensor reading may be non-deterministic due to measurement noises or possible sensor failures. Existing works on this topic mainly consider specific types of unreliable sensors such as the cases of intermittent sensors failures, permanent sensor failures or their combinations. In this work, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniform framework</i> for diagnosability of DES subject to, not only sensor failures, but also a very general class of unreliable sensors. Our approach is to use linear temporal logic (LTL) with semantics on infinite traces to describe the possible behaviors of the sensors. A new notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varphi$</tex-math></inline-formula> -diagnosability is proposed as the necessary and sufficient condition for the existence of a diagnoser when the behaviors of sensors satisfy the LTL formula <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varphi$</tex-math></inline-formula> . Effective approach is provided to verify this notion. We show that, our new notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varphi$</tex-math></inline-formula> -diagnosability subsumes all existing notions of robust diagnosability of DES subject to sensor failures. Furthermore, the proposed framework is user-friendly and flexible since it supports an arbitrary user-defined unreliable sensor type based on the specific scenario of the application. As examples, we provide two new notions of diagnosability, which have never been investigated in the literature, using our uniform framework.

Secret Protections With Costs and Disruptiveness in Discrete-Event Systems Using Centralities

Secret Protections With Costs and Disruptiveness in Discrete-Event Systems Using Centralities

Secret Protection in Discrete-Event Systems With Generalized Confidentiality Requirements

Secret Protection in Discrete-Event Systems With Generalized Confidentiality Requirements

Bounds on the growth rate of time-invariant switching max-min-plus-scaling discrete-event systems

We consider the growth rate of a switching max-min-plus-scaling (S-MMPS) system in a discrete-event framework. We show that an explicit, time-invariant, monotone, and arbitrarily switching MMPS system has a bounded growth rate. Further, we propose a mixed-integer linear programming problem to calculate the estimates of the smallest upper bound and the largest lower bound of the growth rate of an S-MMPS system.

Improving Warehouse Layout Effectiveness and Process Picking Efficiency with the Discrete Event System Simulation Approach

Field conditions show process picking inefficiencies caused by the layout and arrangement of products in the warehouse that are not optimal. The objective of this research is to estimate the actual system performance and propose a new design so that the system becomes more effective and efficient. The research methodology combines Discrete-Event System (DES) Simulation to estimate the performance of the system and Double ABC Analysis to determine the re-arrangement of the item's position based on the pickup frequency and outer volume of the item. The statistical results identified the moving time has a significant difference between the actual and proposed layout. This study concludes that the re-arrangement of items can reduce the picking time of operators in the operational warehouse process.

Stochastic Fuzzy Discrete Event Systems and Their Model Identification.

We introduce a new class of fuzzy discrete event systems (FDESs) called stochastic FDESs (SFDESs), which is significantly different from the probabilistic FDESs (PFDESs) in the literature. It offers an effective modeling framework for applications that are unsuitable for the PFDES framework. An SFDES is comprised of multiple fuzzy automata that occur randomly one at time with different occurrence probabilities. It uses either the max-product fuzzy inference or the max-min fuzzy inference. This article focuses on single-event SFDES-each of the fuzzy automata of such an SFDES has one event. Assuming nothing is known about an SFDES, we develop an innovative technique capable of determining number of fuzzy automata and their event transition matrices as well as estimating their occurrence probabilities. The technique, called prerequired-pre-event-state-based technique, creates and uses merely N particular pre-event state vectors of dimension N to identify event transition matrices of M fuzzy automata, involving a total of MN2 unknown parameters. One necessary and sufficient condition and three sufficient conditions are established for the identification of SFDES with different settings. The technique does not have any adjustable parameter or hyperparameter to set. A numerical example is provided to concretely illustrate the technique.

Supervisory Control of Networked Discrete Event Systems to Achieve a Required Language

Supervisory Control of Networked Discrete Event Systems to Achieve a Required Language

Design of Supervisors for Partially Observed Discrete Event Systems Using Quiescent Information

Design of Supervisors for Partially Observed Discrete Event Systems Using Quiescent Information

Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems With LTL Specifications

Bounded Synthesis and Reinforcement Learning of Supervisors for Stochastic Discrete Event Systems With LTL Specifications

Minimal Sensor Activation for Detectable Networked Discrete Event Systems

Minimal Sensor Activation for Detectable Networked Discrete Event Systems

Identification of Multievent Stochastic Fuzzy Discrete Event Systems.

We recently introduced a novel category of fuzzy discrete event systems (FDESs) termed stochastic FDESs (SFDESs), wherein multiple fuzzy automata occur randomly with different probabilities. We also developed two techniques for identifying event transition matrices in single-event SFDES employing the max-product fuzzy inference. One of them, named the equation-systems-based technique, focuses on the single-event SFDES identification, where the fuzzy automaton of each FDES has only one event. Expanding on our research, this article delves into multievent SFDES identification, allowing each FDES to encompass a sequence of events. This is a new research direction that has not been mentioned in the literature before. Upon activation of an FDES, all its events occur sequentially. Our mathematical proof first establishes the associativity of the max-product inference operation, leading to the introduction of a pivotal and novel concept called an equivalent overall event transition matrix for a consecutive event sequence. This concept establishes a theoretical framework for utilizing the equation-systems-based technique in a novel three-step method for identifying multievent SFDESs. The technique is employed in the first two steps to: 1) determine the number of fuzzy automata in an SFDES and 2) calculate their occurrence frequencies. In the third step, multievent transition matrices of the SFDES are learned by using the stochastic-gradient-descent-based algorithms that we previously developed for multievent FDESs, provided the numbers of consecutive events for each fuzzy automaton within the SFDES are known. Theoretical analysis reveals, for the first time, the interconnections between the event transition matrices learned by the algorithms, the equivalent overall event transition matrices derived from these matrices, and the target event transition matrices. To illustrate our findings, we present an informative example.

Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete event system specification

Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete event system specification

Analysis of research into the mathematical foundations of model-based systems engineering

The article provides an analysis of research into the mathematical foundations of model-based systems engineering (MBSE, Model-Based Systems Engineering). Both the classical mathematical theory of Wymore’s system design and modern research are considered, in particular, the formalization of the semantics of the SysML language using automata theory, the use of category theory as a formal mathematical basis for model-based system design, the study of the possibilities of combining the theoretical basis of Wymore’s systems and the universal Discrete Event System Specification (DEVS) modeling formalism.

Supervisory Control of Networked Fuzzy Discrete Event Systems

Supervisory Control of Networked Fuzzy Discrete Event Systems

A General Architecture for Intersection-Based Decentralized Supervisory Control of Discrete Event Systems

A General Architecture for Intersection-Based Decentralized Supervisory Control of Discrete Event Systems

Better Late than Never: On Epistemic Diagnosability of Discrete Event Systems

We investigate the diagnosability verification problem in the framework of discrete-event systems. Most of the existing works on this topic assume that faults are related to the internal behaviors of the system such as occurrences of particular events. In this work, motivated by information-flow security considerations, we model faults as some critical information leakages of the system to an intruder, which may have different observations from the system user. Specifically, we say that a fault occurs if the intruder knows that the system has passed by a secret state. We present a formal notion called epistemic diagnosability to capture whether or not the system user can always detect, based on its own observation, the critical information leakage to an intruder within a bounded delay. We show that this new notion subsumes the standard notion of event-based diagnosability. Furthermore, an effective algorithm is provided to verify this new notion.

A new global optimization for hybrid automaton Identification

System Identification to a hybrid automaton is a complex and flourishing research field involving expertise in both continuous dynamics and discrete event system modeling. Current methodologies for hybrid automaton Identification predominantly rely on a framework centered around the sequential resolution of local signal processing optimization problems. This paper seeks to enhance the existing framework by introducing a novel step designed to address certain limitations inherent in the non-global optimization aspect of a sequential resolution framework. The proposed approach enables each Identification step not only to address its local optimization challenges but also to contribute to the optimization of a global cost function including model distance, sequential fidelity, and automaton structure.