Discrete Event Systems Research Articles

On Deadlock Analysis and Characterization of Labeled Petri Nets with Undistinguishable and Unobservable Transitions

This work addresses the analysis and characterization of deadlocks in discrete-event systems modeled by labeled Petri nets (LPNs) with undistinguishable and unobservable transitions. To provide a solution for the notorious problem, it is essential to present an effective characterization in such a way that deadlock control and synthesis are technically and methodologically possible. To this end, we introduce the notion of dangerous implicit vectors (DIVs), which implicitly threaten the system deadlock-freedom. The set of dead markings is divided into two subsets: dead basis markings (DBMs) and dangerous implicit markings (DIMs). An algorithm is designed to compute the sets of DIVs and DIMs at a given basis state of a system. Moreover, by virtue of linear algebraic equations, we formulate sufficient conditions for identifying the existence of blocking markings in an LPN. Finally, an algorithm is developed to construct an observed graph that is a compendious presentation of the reachability graph of a net system, with respect to the existence of dead reaches. At the end of this paper, experiment results that illustrate the correctness and effectiveness of the reported solution are presented.

Discrete-Time Finite Fuzzy Markov Chains Realized Through Supervised Learning Stochastic Fuzzy Discrete Event Systems

Discrete-Time Finite Fuzzy Markov Chains Realized Through Supervised Learning Stochastic Fuzzy Discrete Event Systems

On the Existence of Nonblocking Bounded Supervisors for Discrete-Event Systems

On the Existence of Nonblocking Bounded Supervisors for Discrete-Event Systems

Building credibility for human systems integration in model‐based systems engineering

AbstractRobust and trusted digital human representations are necessary to successfully account for human considerations in model‐based systems engineering (MBSE). Multiple domains and modeling frameworks leverage verification, validation, and accreditation (VV&A) processes to characterize when and under what conditions a model is valid to establish credibility. A literature review was completed on mathematical, physics‐based, software development, discrete event simulation, agent‐based, system dynamics, and MBSE models with the goal of proposing a process for performing VV&A on digital engineering (DE) and MBSE models for sociotechnical systems. However, this research also revealed the need for a broader framework to characterize the risk associated with using these models for making high‐consequence decisions. While accomplishing the literature review, another approach to building credibility was identified that is used heavily in the financial industry, namely model risk management (MRM). This process is extended by leveraging MRM approaches from within the financial community to propose a framework for sociotechnical model users to characterize the risk of using MBSE models to make programmatic decisions. The primary contribution of this work is to document a meta‐analysis of model VV&A while proposing an alternative approach to characterizing and communicating credibility that was discovered during this analysis. This approach could be a viable option for ensuring the credibility of human systems integration in MBSE models.

Digital-twin co-simulation framework to support informed decision in healthcare planning and management

This manuscript aims to show the proof-of-concept for the integration of digital twin (DT) and discrete event co-simulation through its application to forward simulation of physical activities of care providers and robots in an intensive care unit (ICU). Co-simulation focuses on modular integration of heterogeneous simulation components to capture the complexity inherent in systems. Advancing this conception, the proposed co-simulation attempted to narrow gaps with real-world systems by using DT while simultaneously leveraging the forward simulation capacity of discrete event simulation (DES). In particular, we created a co-simulation for human–robot interactions in a simulated ICU facility, which modeled the hospital layout including the facility, robot, avatars for healthcare professionals, equipment, patient bed, and vital signs, which comprise physical twin (PT). In addition, specific patient-care scenarios were defined to drive PT for which the states of interest were represented in the DT, and then used for a dynamic input to the discrete event co-simulation. At the onset of the co-simulation, the parameters needed to run DES were manually configured to align the parameters with those of the DT. For internal validation, this DES was replicated under different work-system policies, and the co-simulation outcomes were interpreted in clinical context. Our co-simulation approach has potential for application to a broader class of problems in healthcare planning and management by supporting informed decision-making. Methodologically, timely information exchanges of PT with DT, and ensuing model updates in the Discrete Event System Specification (DEVS) is a promising approach for co-simulation, to maintain robustness against perturbations to real-world systems.

Optimal control of discrete event systems under uncertain environment based on supervisory control theory and reinforcement learning

Discrete event systems (DESs) are powerful abstract representations for large human-made physical systems in a wide variety of industries. Safety control issues on DESs have been extensively studied based on the logical specifications of the systems in various literature. However, when facing the DESs under uncertain environment which brings into the implicit specifications, the classical supervisory control approach may not be capable of achieving the performance. So in this research, we propose a new approach for optimal control of DESs under uncertain environment based on supervisory control theory (SCT) and reinforcement learning (RL). Firstly, we use SCT to gather deliberative planning algorithms with the aim to safe control. Then we convert the supervised system to Markov Decision Process simulation environments that is suitable for optimal algorithm training. Furthermore, a SCT-based RL algorithm is designed to maximize performance of the system based on the probabilistic attributes of the state transitions. Finally, a case study on the autonomous navigation task of a delivery robot is provided to corroborate the proposed method by multiple simulation experiments. The result shows the proposed approach owning 8.27%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} performance improvement compared with the non-intelligent methods. This research will contribute to further studying the optimal control of human-made physical systems in a wide variety of industries.

Integrating reinforcement learning and supervisory control theory for optimal directed control of discrete-event systems

Directed control is crucial for implementing controllers on programmable logic controllers. A directed controller is one that actively selects at most one controllable event (control command) to execute at any instant. This study investigates the numerical optimization problem of directed control for discrete-event systems, such as traffic systems and robotic systems. By integrating supervisory control theory and reinforcement learning, an algorithmic procedure is designed to synthesize an optimal directed controller, which minimizes the total cost associated with event execution while ensuring the safety and liveness of the controlled system. Two improved Q-learning algorithms incorporating dynamic parameter adaptation strategies are developed to enhance the global search ability of basic Q-learning. To demonstrate applicability and effectiveness, we apply the proposed method to control a guideway system and a multi-train traffic system, respectively. The experimental results indicate the superiority of the proposed method over the comparison methods.

CSS Technical Committee on Discrete Event Systems [Technical Activities

CSS Technical Committee on Discrete Event Systems [Technical Activities

Obfuscation mechanism for simultaneous public event information release and private event information hiding in discrete event systems

In this paper, we study the problem of simultaneous public event information release and private event information hiding in discrete event systems modeled by partially observed non-deterministic finite automata, where the public and private event information is respectively defined as unobservable fault and secret events. The notion of D-C-compossibility is introduced to characterize whether a system simultaneously conceals the occurrences of secret events while releasing the occurrences of fault events. When such a property does not hold, we investigate its enforcement through a defensive function. This function takes each observable event of a system as input and generates a suitably modified event sequence at the system's output using event deletion, insertion, or substitution. Specifically, our focus is on prioritizing the concealment of the secret events while maximizing the detection of the fault events through the use of defensive functions. The notion of D-C-enforceability that refers to the capability of a defensive function to employ a strategy for manipulating observations of a system to enforce D-C-compossibility is proposed. That is, a D-C-enforcing defensive function ensures that all occurrences of fault events can be revealed while concealing all occurrences of secret events. A D-C-diagnoser construction is proposed to enumerate all feasible defensive actions following system behavior. By taking advantage of the D-C-diagnoser, we obtain a necessary and sufficient condition for D-C-enforceability, along with a corresponding obfuscation strategy for defensive functions.

Reliable decentralized failure diagnosis of discrete event systems using single-level inference

Reliable decentralized failure diagnosis of discrete event systems using single-level inference

Estimation and Prevention of Actuator Enablement Attacks in Discrete-Event Systems Under Supervisory Control

Estimation and Prevention of Actuator Enablement Attacks in Discrete-Event Systems Under Supervisory Control

Quantitatively nonblocking supervisory control of discrete-event systems

In this paper, we propose a new property of quantitative nonblockingness of an automaton with respect to a given cover on its set of marker states. This property quantifies the standard nonblocking property by capturing the practical requirement that every subset (i.e. cell) of marker states can be reached within a prescribed number of steps from any reachable state and following any trajectory of the system. Accordingly, we formulate a new problem of quantitatively nonblocking supervisory control, and characterize its solvability in terms of a new concept of quantitative language completability. It is proven that there exists the unique supremal quantitatively completable sublanguage of a given language, and we develop an effective algorithm to compute the supremal sublanguage. Finally, combining with the algorithm of computing the supremal controllable sublanguage, we design an algorithm to compute the maximally permissive solution to the formulated quantitatively nonblocking supervisory control problem.

Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles

One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recognition. Consequently, vehicles that are exclusively controlled by EEG signals demonstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI performance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisory theory to verify the accuracy of the driver's brain-controlled directives. Concurrently, a fuzzy logic-based automatic controller is developed to generate decisions automatically in accordance with the present state of the vehicle via fuzzy reasoning. The final decision is then reached through the application of secondary fuzzy reasoning to the accuracy of the driver's instructions and the automated decisions to make adjustments that are more consistent with human intent. A clever BCI gadget known as the Consistent State Visual Evoked Potential (SSVEP) is utilized to show the viability of the proposed technique. We recommend that additional research should be conducted at this time to confirm that our recommended system may further improve the control execution of BCI-fueled cars, regardless of whether BCIs have special limitations.

Detection of Cyber-Attacks in a Discrete Event System Based on Deep Learning

This paper addresses the problem of cyber-attack detection in a discrete event system by proposing a novel model. The model utilizes graph convolutional networks to extract spatial features from event sequences. Subsequently, it employs gated recurrent units to re-extract spatio-temporal features from these spatial features. The obtained spatio-temporal features are then fed into an attention model. This approach enables the model to learn the importance of different event sequences, ensuring that it is sufficiently general for identifying cyber-attacks, obviating the need to specify attack types. Compared with traditional methods that rely on synchronous product computations to synthesize diagnosers, our deep learning-based model circumvents state explosion problems. Our method facilitates real-time and efficient cyber-attack detection, eliminating the necessity to specifically identify system states or distinguish attack types, thereby significantly simplifying the diagnostic process. Additionally, we set an adjustable probability threshold to determine whether an event sequence has been compromised, allowing for customization to meet diverse requirements. Experimental results demonstrate that the proposed method performs well in cyber-attack detection, achieving over 99.9% accuracy at a 1% threshold and a weighted F1-score of 0.8126, validating its superior performance.

Predicting weight dispersion in seabass aquaculture using Discrete Event System simulation and Machine Learning modeling

Marine aquaculture, particularly in the Mediterranean region, faces the challenge of minimizing growth dispersion, which has a direct impact on the production cycle, market value and sustainability of the sector. Conventional grading methods are resource intensive and potentially detrimental to fish health. The current study presented an innovative approach in predicting fish weight dispersion in European seabass (Dicentrarchus labrax) aquaculture. Seabass is one of the two major fish species cultivated on the Mediterranean coast, with a fattening cycle of 18–24 months. During this period, several grading operations are carried out to minimize growth dispersion. The intricate feed-fish-water system, characterized by complex interactions among feeding regimes, fish behavior, individual metabolism and environmental factors, is the focus of the study. The comprehensive, five-step methodology addresses this complexity. The process begins with a Discrete Event System (DES) model that simulates the feed-fish-water dynamics, taking into account individual fish metabolism. This is followed by the development of a surrogate machine learning (ML) regressor model, which is trained on DES simulation data to efficiently predict growth distribution. The model is then calibrated and customized for specific fish stocks and production tanks. The preliminary results from 21 tanks in two trials with European seabass (D. labrax) showed the effectiveness of the method. The results from the simulation models achieved a R2 of 99.9 % and a Mean Absolute Percentage Error (MAPE) of 1.1 % for the prediction of mean final weight and a R2 of 90.3 % with a MAPE of 8.1 % for the standard deviation of final weight. In summary, this study represents a significant advance in the planning and management of seabass aquaculture. Given the lack of effective prediction tools in the aquaculture industry, the proposed methodology has the potential to reduce risks and inefficiencies, thus possibly optimizing aquaculture practices by increasing sustainability and profitability.

Fuzzy Partitioned Discrete-event System and Its Decentralized Supervisory Control

This work uses the idea of a fuzzy partitioned automaton to examine a fuzzy partitioned discrete-event system and associated decentralized supervisory control theory. We will specifically look at the notion of a fuzzy partitioned automaton and how it may be used to simulate the aforementioned system. In addition, we investigate the decentralized fuzzy supervisory control theory for a fuzzy partitioned discrete-event system and look at the conditions for its existence.

Supervisor synthesis for opacity enforcement in partially observed discrete event systems

Supervisor synthesis for opacity enforcement in partially observed discrete event systems

Opacity enforcement in discrete event systems using differential privacy

Opacity is an important information security property that characterizes whether the secret of a system can be inferred by an intruder who can partially observe the behavior of the system. In this paper, we touch upon the enforcement of the current-state opacity and initial-state opacity of discrete event systems modeled by automata. Specifically, given a partially observed discrete event system that is not current-state opaque (resp., initial-state opaque), by observing the system's behavior, the intruder stands a chance to infer that the current state (resp., initial state) is within the secret of the system. To prevent the exposure of secret information, we introduce a differential privacy mechanism to enforce a non-opaque system to be opaque. Particularly, the designed mechanism is settled to receive the observations of the system. Suppose that there are two observations such that one of them can lead to the exposure of the secret and the other is randomly observed. When the two observations are received by the designed mechanism that provides differential privacy, a randomly modified output with an approximate probability is exported, exposing to the intruder. In this way, since the observation from which the secret can be inferred by the intruder, is befuddled with one randomly generated by the system, he/she is not able to deduce which one is truly generated and received by the mechanism, even if the probabilistic generation rule of the mechanism is public.

Distributed Fault Diagnosis in Discrete Event Systems With Transmission Delay Impairments

Distributed Fault Diagnosis in Discrete Event Systems With Transmission Delay Impairments

Active Fault Isolation for Discrete Event Systems

Active Fault Isolation for Discrete Event Systems