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Linear systems over the max-plus algebra provide a suitable formalism to model discrete event systems where synchronization, without competition, is involved. In this paper, we consider a formulation of the model matching problem for systems of such class, in which the output of a given system, called the plant, is forced, by a suitable input, to track exactly that of a given model. A necessary and sufficient condition for its solvability is obtained by making a suitable use of geometric methods in the framework of systems over the max-plus algebra.

This paper proposes a deadlock prevention policy for flexible manufacturing systems (FMSs) based on a think-globally-act-locally approach and a structurally minimal method. First, by using the think-globally-act-locally approach, a global idle place is temporarily added to a Petri net model with deadlocks. Then, at each iteration, an integer linear programming problem is formulated to design a minimal number of maximally permissive control places. Therefore, a supervisor with a low structural complexity is obtained since the number of control places is greatly compressed. Finally, by adding the designed supervisor, the resulting net model is optimally or near-optimally controlled. Three examples from the literature are used to illustrate the proposed method.

This paper presents an approach to automatically amend an erroneous model of an implementation using a safety specification as the basis to ensure safety. Industrially, safety PLCs are common to ensure safe operations. However, before its commissioning, the implemented safety code must be tested for faults caused by spurious transitions and missing safety transitions. Spurious transitions are implemented events that are not prescribed by the safety specification, while missing safety transitions are unimplemented safety events that are prescribed by the safety specification. The presence of these faults can result in material or human damage. The proposed approach requires the model of an implementation to be trace equivalent with the given safety specification only in terms of traces composed of safety events, which is captured by the notion of safe-IOCOS. If the implementation emits other than the specified safety events then the implementation is not safe-IOCOS and requires amendment. This is achieved by removing the spurious transitions and adding the missing safety events in the implementation using synthesis techniques from the supervisory control theory. The infimal controllable superlanguage is used to compute the infimal safety extension, which adds the missing safety transitions. It is shown how the resulting model of an implementation after amendment is both safe-IOCOS and controllable with respect to the specification.

This article investigates the enforcement of generalized mutual exclusion constraints (GMECs) and deadlock-freeness on a time Petri net (TPN) system with uncontrollable transitions, motivated by the fact that the existing methods enforcing GMECs may degrade the performance of a closed-loop system and lead to deadlock states. A supervisor enforcing a set of GMECs and deadlock-freeness on an underlying untimed Petri net system is assumed to be available. By exploiting timing information and mathematical programming, a control function is designed to restrict the firing intervals of transitions such that a TPN system can avoid entering forbidden states. The key idea behind the proposed approach is the online computation of a graph, called reduced modified state class graph (RMSCG), that is an extension of the partial modified state class graph recently introduced by the authors. Based on the RMSCG, an online control synthesis procedure is developed, which can enforce the originally given GMECs and deadlock-freeness in a maximally permissive way.

In this paper, our goal is to propose a new extension to the Discrete Event System (DEVS) formalism, which is based on Dynamic Bayesian Network (DBN), and this extension will be called DBN-DEVS, wh...

Uncertainty Propagation methods are well-established when used in modeling and simulation formalisms like differential equations. Nevertheless, until now there are no methods for Discrete-Dynamic Systems. Uncertainty-Aware Discrete-Event System Specification (UA-DEVS) is a formalism for modeling Discrete-Event Dynamic Systems that include uncertainty quantification in messages, states, and event times. UA-DEVS models provide a theoretical framework to describe the models’ uncertainty and their properties. As UA-DEVS models can include continuous variables and non-computable functions, their simulation could be non-computable. For this reason, we also introduce Interval-Approximated Discrete-Event System Specification (IA-DEVS), a formalism that approximates UA-DEVS models using a set of order and bounding functions to obtain a computable model. The computable model approximation produces a tree of all trajectories that can be traversed from the original model and some erroneous ones introduced by the approximation process. We also introduce abstract simulation algorithms for IA-DEVS, present a case study of UA-DEVS, its IA-DEVS approximation and, its simulation results using the algorithms defined.

This paper proposes algorithms for supervisor synthesis in discrete event system models with distinguishers. Distinguishers are special components responsible to select an enabled event from a group of related refined events. They are a helpful modelling tool, but their use increases the state space and makes supervisor synthesis more difficult. The paper shows how general algorithms for modular or compositional synthesis can be enhanced by considering the special properties of distinguishers. This gives rise to systematic algorithms that compute least restrictive controllable and nonblocking supervisors, while working with only a part of the distinguisher model. A worked case study of a bottling plant demonstrates the efficacy of the approach.

Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework.

This paper is towards the study of a fuzzy partitioned discrete-event system and its supervisory control theory under partial/full observations. Specifically, we study the concept of a fuzzy partit...

The responses of vehicles to the changes in traffic situations inevitably have delays in observing an event and implementing a control command, which often causes fatal accidents. So far, the methods for handling delays are empirical and cannot be mathematically proven. To eliminate the accidents caused by such delays, in this paper, we develop mathematically provable methods to handle these delays. Specifically, we use networked discrete event systems to model the process of driving vehicles and present a supervisory controller for handling delay situations. The method developed in this paper could serve as a new start for modeling and controlling the responsive behaviors of self-driving vehicles in the future.

Automated guided vehicles (AGVs) are extensively used in many applications such as intelligent transportation, logistics, and industrial factories. In this paper, we address the path planning problem for an AGV system (i.e. a team of identical AGVs) with logic and time constraints using Petri nets. We propose a method to model an AGV system and its static environment by timed Petri nets. Combining the structural characteristics of Petri nets and integer linear programming technique, a path planning method is developed to ensure that all task regions are visited by AGVs in time and forbidden regions are always avoided. Finally, simulation studies are presented to show the effectiveness of the proposed path planning methodology.

The good functioning of a discrete event system is related to how much the temporal constraints are respected. This paper gives a new approach, based on a statistical model and neural network, that allows the verification of temporal constraints in DES. We will perform an online temporal constraint checking which can detect in real time any abnormal functioning related to the violation of a temporal constraint. In the first phase, the construction of temporal constraints from statistical model is shown and after that neural networks are involved in dealing with the online temporal constraint checking.

This paper describes algorithms to compute a counterexample when compositional nonblocking verification determines that a discrete event system is blocking. Counterexamples are an important feature of model checking that explains the cause of a detected problem, greatly helping users to understand and fix faults. In compositional verification, counterexamples are difficult to compute due to the large state space and the loss of information after abstraction. The paper explains the difficulties and proposes solutions, and experimental results show that counterexamples can be computed successfully for several industrial-scale systems.

In this article, we propose an online fault diagnosis approach for labeled Petri nets. When observing an event, the reachability graph of the unobservable subnet (i.e., the net consisting of all places, unobservable transitions, and the attached arcs between them) is first constructed and then a topological sort (i.e., an ordered list of markings) of the reachability graph is obtained. According to the topological sort, each marking in the reachability graph is associated with a vector that contains diagnosis information to form a new graph, called a diagnosis graph. Based on the diagnosis graph constructed for each observed event, an online algorithm is developed to perform diagnosis. When the considered Petri net systems have a small amount of unobservable transitions, the proposed approach enjoys a high computational efficiency compared with the existing ones using integer linear programming.

Despite advances in automated manufacturing systems (AMSs), faults occur from time to time, which cannot be avoided in a complex real system. A fault is one of the primary causes of failures making some AMS operations unable to complete, and the diagnosis is one of the most important steps in fault-recovery and repair. This work develops a methodology for investigating the behavior of faults on the resources in discrete event systems that are failure-prone. We tackle the fault-tolerant problem and propose a method to make the system able to continue performing its duties, while the failed resources are under a repair and recovery process. In this work, a failure-safe model is proposed and, at the same time, a method for fault recovery and repair of a faulty element is presented without interrupting task processing due to the occurrence of fault to some elements. We use redundant elements to replace the target elements, and these redundant elements are used to do the same work as the target elements do, when faults occur to the target elements. A target element is an unreliable element that is prone to failure. After a faulty target element is repaired and recovered, its failure model is automatically replaced by its repaired model to indicate that the corresponding element has returned to work. The proposed method is tested using an application example. The results show, compared with those obtained by the studies in the literature, that the proposed method has a great performance and outperforms the existing studies.

This paper deals with control of industrial systems which can be depicted by timed event graphs. A methodology and software tools are presented to allow engineers to implement automatically controllers in PLC. Three steps are needed and recalled: the modelling of the system in max plus algebra, the design of the controller and the implementation in a Supervisory Control and Data Acquisition (SCADA) system. An example on a real system is given to illustrate these steps.

There are many cases in which our understanding of a system may be limited due to its complexity or lack of access into the entire system, leaving us with only partial system knowledge. This paper proposes a novel systematic active-learning method for realizing a partially-known Discrete Event System (DES). The proposed technique takes the available information about the system into account by tabularly capturing the known data from the system, and then, discovers the unknown part of the system via an active-learning procedure. For this purpose, a series of tables will be constructed to first infer the information about the system from the available data, and if unavailable, the developed algorithm collects the information through basic queries made to an oracle. It is proven that the developed technique returns a language-equivalent finite-state automaton model for the system under identification after a finite number of iterations. A real-world illustrative example is provided to explain the details of the proposed method.

Cyber-physical systems have been highly integrated into many contemporary infrastructures. As this integration deepens, the importance of protecting these systems from unauthorized access and data corruption increases. Nowadays, cyber-physical systems are not well protected against network attacks. One solution is to improve the security of a system by encrypting the transmitted data. In this paper, we consider the encryption of supervisors of discrete event systems modeled with deterministic finite-state automata. We propose an encryption framework of supervisory control systems based on the matrix notation of automata. The purpose of using matrix notation is to make it suitable for homomorphic encryption schemes over integers, which are emerging in the cryptography area. We calculate the entropy of the matrix notation and find that as the size of a system increases, it gets smaller and approaches zero. Owing to the low entropy of the matrix notation, we propose an algorithm to enhance its entropy. By applying the entropy-enhancing process, the distribution characteristics of entries in matrices or vectors can be hidden to avoid a brute force attack. Correspondingly, we propose an entropy restoration algorithm to ensure that the control action can be transmitted correctly.

This paper proposes algorithms for supervisor synthesis in discrete event system models with distinguishers. Distinguishers are special components responsible to select an enabled event from a group of related refined events. They are a helpful modelling tool, but their use increases the state space and makes supervisor synthesis more difficult. The paper shows how general algorithms for modular or compositional synthesis can be enhanced by considering the special properties of distinguishers. This gives rise to systematic algorithms that compute least restrictive controllable and nonblocking supervisors, while working with only a part of the distinguisher model. A running example of a manufacturing cell illustrates the approach.

The boom seen in artificial intelligence in recent years has led to a revolution in the automotive industry. Numerous automakers around the world, such as Tesla, Toyota, Honda, and BMW, have achieved giant strides in the development of e-autonomous vehicles. Consequently, shared electric automated vehicle mobility (SEAVM) systems, which are a crucial part of future innovative transportation solutions, have attracted significant attention from the research community, particularly from a design perspective. However, the flexibility of shared automated mobility systems may lead to a self-operating technology issue (unequal distribution of vehicles), since users in these systems can pick up and drop off electric vehicles wherever they like. With this in mind, this paper addressed the issues of autonomous repositioning and the assignment of shared autonomous electric vehicle systems to balance a system’s network and fulfill its demand. Modeling, analysis and assessment of the system’s performance were carried out using stochastic Petri nets formalism, which included determining the average time areas were empty/congested and the number of unserved consumers, and estimating the redistribution service launch moment. Furthermore, many simulation scenarios were analyzed, including repositioning and without repositioning scenarios, in order to evaluate the efficiency of the model and to show the potential of using Petri nets as a probabilistic formalism approach for the modeling of e-automated mobility systems.

This chapter discusses the state, input, and output variables of discrete time systems along with the methods for building discrete time simulation (DTS). It also discusses comparison of DTS and discrete event simulation (DES). The chapter describes the ‘sample path’ – also known as ‘trajectory’ – a useful technique to extract the relevant dataset and conduct analysis. A vending machine example is used to demonstrate that DTS is a general case of DES, being able to model and simulate a discrete event system. However, DES can simplify the modelling process for a discrete event system, and is easy to understand. DTS is more suitable for systems in which the states change at uniformly distributed time points, as in the car‐following example. Finally, the chapter concludes with an example that simulates microscopic traffic – a car‐following model. If the system is event driven, DES is better, whereas if the system is time driven, DTS is better.

Most research in fault diagnosis of discrete event systems has been focused on permanent failures. However, experience with monitoring of dynamic systems shows that intermittent faults are predominant, and that their diagnosis constitutes one of the most challenging tasks for surveillance activities. Among the main existing approaches to deal with permanent faults, two were widely investigated while considering different settings: the Diagnoser based approach, and the Twin-plant based approach. The latter was developed to cope with some complexity limitations of the former. In the present paper, we propose a twin-plant based approach to deal with diagnosability of intermittent faults. Firstly, we discuss various notions of diagnosability, while considering the occurrence of faults, their recovery, and the identification of the system status. Then, we establish the necessary and sufficient conditions for each notion, and develop on-the-fly algorithms to check these properties. The discussed approach is implemented in a prototype tool that is used to conduct experiments on a railway control benchmark.

Given the non-repeatability, complexity, and unpredictability of unconventional public health emergencies, building accurate models and making effective response decisions based only on traditional prediction–response decision-making methods are difficult. To solve this problem, under the scenario–response paradigm and theories on parallel emergency management and discrete event system (DES), the parallel simulation decision-making framework (PSDF), which includes the methods of abstract modeling, simulation operation, decision-making optimization, and parallel control, is proposed for unconventional public health emergency response processes. Furthermore, with the example of the severe acute respiratory syndrome (SARS) response process, the evolutionary scenarios that include infected patients and diagnostic processes are transformed into simulation processes. Then, the validity and operability of the DES–PSDF method proposed in this paper are verified by the results of a simulation experiment. The results demonstrated that, in the case of insufficient prior knowledge, effective parallel simulation models can be constructed and improved dynamically by multi-stage parallel controlling. Public health system bottlenecks and relevant effective response solutions can also be obtained by iterative simulation and optimizing decisions. To meet the urgent requirements of emergency response, the DES–PSDF method introduces a new response decision-making concept for unconventional public health emergencies.

Fault detection and diagnosis &#x0028 FDD &#x0029 facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy &#x0028 AR &#x0029, Principal component analysis &#x0028 PCA &#x0029, Discrete event system &#x0028 DES &#x0029 model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters. Due to various reasons like faults, communication errors etc., sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc., have mechanisms to handle measurement inconsistency, however, they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent &#x0028 MI &#x0029 parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system &#x0028 MIDES &#x0029 framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state&#x0028 s &#x0029 the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pump-valve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol &#x0028 DHCP&#x0029 server attack by mapping the attack to a fault in the DES framework.

This paper focuses on the algebraic expression of discrete event systems (DES), which bridges the gap between linear control system theory and discrete event system theory. For this purpose, we firstly give an overview of different approaches towards an algebraic description existing in the literature in the area of different classes of DES, such as Petri nets, automata and Boolean networks. After that, the complexity, applicability for control and the structural restrictions are compared. Finally, possible future developments are highlighted.

This paper presents a method to improve the diagnosability of power network under multiple faults. In this paper, the steps of fault diagnosis are as follows: first, constructing finite automata model of the diagnostic system; then, a fault diagnoser model is established through coupling operation and trajectory reasoning mechanism; finally, the diagnosis results are obtained through this model. In this paper, the judgment basis of diagnosability is defined. Then, based on the existing diagnosis results, the information available can be increased by adding sensor devices, to achieve the purpose of diagnosability in the case of multiple faults of the system.

The paper provides a general view on the original logical inference based approach to dealing with discrete event systems as subject to supervisory control theory. The approach proposes a representation of automata-based discrete event system as a positively constructed formula and implementation of the calculus of positively constructed formulas. The stages of a supervisor construction are illustrated with a simplified model of an autonomous underwater vehicle operational modes switching. The supremal controllable sublanguage of the specification and the supervisor are constructed.

This paper formulates a new control problem of achieving the global objective of a discrete event system with networked control agents that pursue only their own objectives and compete with...

This paper considers the problem of diagnosability analysis of discrete event systems modeled by labeled Petri nets (LPNs). We assume that the LPN can be bounded or unbounded with no deadlock after firing any fault transition. Our approach is novel and presents the on-the-fly diagnosability analysis using verifier nets. For a given LPN model, the verifier net and its reachability graph (for a bounded LPN) or coverability graph (for an unbounded LPN) are built on-the-fly and in parallel for diagnosability analysis. As soon as a diagnosability decision is established, the construction is stopped. This approach achieves a compromise between computation limitations due to efficiency and combinatorial explosion and it is useful to implement an engineering approach to the diagnosability analysis of complex systems.

Employing a state-based Discrete Event System (DES) modelling framework, this paper proposes a new fault diagnosis approach called measurement limitation-based abstract DES diagnosis (MLAD), which ...

For a real-time system (RTS) processing both sporadic and (multiple-period) periodic tasks, this study presents a novel modular modeling framework to describe the parameters of tasks, conforming to the pertinent concepts and techniques of discrete-event systems (DES). A task is represented by an automaton synchronized by the modular models corresponding to its parameters. As a consequence, a DES model depicting the RTS is synchronized by the DES representing these tasks. Based on supervisory control theory, priority-free conditionally-preemptive (PFCP) real-time scheduling is solved by finding all the safe execution sequences. Finally, the PFCP scheduling is illustrated by real-world examples.

Agent-based modeling has recently gained popularity in the field of simulation and modeling. Due to their characteristic properties, agent-based models (ABMs) allow for an improved and flexible way of modeling complex systems. Social dynamics is one such a complex system, which has complex components, such as demography, sociology, economics, psychology, health, and so on. Demography is one of the bigger and more important sub-systems of this complex system. In this paper, we have focused on utilizing the potential of ABM techniques to analyze the underlying processes in social demography. We propose and implement a holistic ABM that can be used for analysis, understanding, and prediction of socio-demographic processes as well as a tool for policy design and evaluation. The proposed model incorporates well-known factors affecting demography and provides ease and flexibility of adding newer factors. In this paper, we considered the use case of Korea and utilized the Korean census data for model development. Many ABMs have been proposed for demography but most of them not only have limited functionalities, but also lack the suitable usage of agent-based modeling itself. The proposed approach is wide-ranging, flexible, and general and can be reused by simply making changes in the initial data set for the analysis of the target society. We exhibit the validation of the proposed model, as well as its usage by executing virtual experiments for socio-demographic analysis and prediction.

A deluge of approaches has been proposed to implement and deploy a computationally effective and maximally permissive liveness-enforcing supervisor for sequential resource allocation systems (RASs). However, they are stalled by the computational complexity of a large state space that grows exponentially with respect to the size of an underlying RAS. The attention of this work is restricted to a special class of RASs: Disjunctive/Single-unit (D/SU)-RASs. Given an initial resource configuration of a D/SU-RAS, a complete state enumeration can be obtained through pure algebraic operations on this configuration with the pre-computed initial basis state space. When an explicit and complete enumeration of reachable states is impossible (due to the limited memory and storage space), we propose the exact number of reachable states. In this case, given a state vector, its reachability can be decided by an algebraic method. Experimental studies demonstrate the efficiency of the proposed approach.

This work continues our study on the control synthesis for multiple mobile robot systems (MMRS). We assume a hybrid approach that comprises the supervisory control level, based on a discrete event model of MMRS, and the robot control level, based on a continuous time model of the robot motion. Our objective is to further develop the control concept towards its implementation in a real-world application – a testbed for a fleet of six laboratory robots. In the first part of the paper, we develop a methodology of the supervisory control synthesis, that employs the Petri net formalism and formally ensures the required logics of MMRS operation, as well as propose a relevant architecture of the supervisor. The second part is focused on the low-level robot control and control procedures enabling modification of the robot motion according to the supervisor’s decisions. A simulation case is presented that illustrates the operation of the system.

International audience; This paper addresses the problem of determining the diagnosability of hybrid systems by abstracting hybrid models to a discrete event setting. From the continuous model the abstraction only remembers two pieces of information: indiscernability between modes (when they are guaranteed to generate different observations) and ephemerality (when the system cannot stay forever in a given set of modes). Then, we use standard discrete event system diagnosability algorithms. The second contribution is an iterative approach to diagnosability that starts from the most abstract discrete event model of the hybrid system. If it is diagnosable, that means that the hybrid system is diagnosable. If it is not, the counterexample generated by the diagnosability procedure is analysed to refine the DES. If no refinement is found, then it can not be proved that the hybrid system is diagnosable. Otherwise, the refinement is included in the abstract DES model and the diagnosability procedure continues.

In Shu and Lin, 2011, exponential time (actually polynomial space) algorithms for determining the weak detectability and weak periodic detectability of nondeterministic discrete event systems (DESs) are designed. In this paper, we prove that the problems of determining the weak detectability and weak periodic detectability of deterministic DESs are both PSPACE-hard (hence PSPACE-complete). Then as corollaries, the problems of determining the weak detectability and weak periodic detectability of nondeterministic DESs are also PSPACE-hard (hence PSPACE-complete).

We previously introduced an inference-based framework for decentralized prognosis of discrete event systems, and formulated the notion of N-inference V-prognosability to characterize the existence of a disjunctive decentralized prognosis scheme such that any fault can be predicted prior to its occurrence by at least one of the prognosers, using at most N-levels of inferencing. While the disjunctive scheme relies on one of the prognosers making a positive decision, the dual conjunctive scheme relies on none of the prognosers making a negative decision. It is known that the two schemes are incomparable, and in this paper we extend our earlier work to provide a more general framework, by introducing a notion of N-inference prognosability, that captures both disjunctive and conjunctive schemes. We also develop a method for verifying N-inference prognosability.

Accurate performance evaluation of discrete event systems needs a huge number of simulation replications and is thus time-consuming and costly. Hence, efficiency is always a big concern when simulations are conducted. To drastically reduce its cost when conducting them, ordinal optimization emerges. To further enhance the efficiency of ordinal optimization, optimal computing budget allocation (OCBA) is proposed to decide the best design accurately and quickly. Its variants have been introduced to achieve goals with distinct assumptions, such as to identify the optimal subset of designs. They are restricted in selecting the best design or optimal subset of designs. However, a highly challenging issue, i.e., subset ranking, remains unaddressed. It goes beyond best design and optimal subset problems. This work develops a new OCBA-based approach to address the issue and establishes its theoretical foundation. The numerical testing results show that, with proper parameters, it can indeed enhance the simulation efficiency and outperform other existing methods in terms of the probability of correct subset ranking and computational efficiency.

International audience; Checking the diagnosability of a discrete event system aims at determining whether a fault can always be identified with certainty after the observation of a bounded number of events. This paper investigates the problem of pattern diagnos-ability of systems modeled as bounded labeled prioritized Petri nets that extends the diagnosability problem on single fault events to more complex behaviors. An effective method to automatically analyze the diagnosability of a pattern is proposed. It relies on a specific Petri net product that turns the pattern diagnosability problem into a model-checking problem.

We present a reachability graph-based search optimization tool for scheduling.Motivated by the lack of tool support for optimization of TCPNs.Implements an event-driven timed state space with AI heuristic search algorithms.Aimed at supporting flexible decision making process with algorithm portfolio.Comparative study of nine search algorithms on real system demonstrates tool efficiency. The combination of Petri net (PN) modeling with AI-based heuristic search (HS) algorithms (PNHS) has been successfully applied as an integrated approach to deal with scheduling problems that can be transformed into a search problem in the reachability graph. While several efficient HS algorithms have been proposed albeit using timed PN, the practical application of these algorithms requires an appropriate tool to facilitate its development and analysis. However, there is a lack of tool support for the optimization of timed colored PN (TCPN) models based on the PNHS approach for schedule generation. Because of its complex data structure, TCPN-based scheduling has often been limited to simulation-based performance analysis only. Also, it is quite difficult to evaluate the strength and tractability of algorithms for different scheduling scenarios due to the different computing platforms, programming languages and data structures employed. In this light, this paper presents a new tool called TIMSPAT, developed to overcome the shortcomings of existing tools. Some features that distinguish this tool are the collection of several HS algorithms, XML-based model integration, the event-driven exploration of the timed state space including its condensed variant, localized enabling of transitions, the introduction of static place, and the easy-to-use syntax statements. The tool is easily extensible and can be integrated as a component into existing PN simulators and software environments. A comparative study is performed on a real-world eyeglass production system to demonstrate the application of the tool for scheduling purposes.

Rogue Twin Access Point (AP) is a rogue Wi-Fi hotspot/AP setup by an adversary solely with the purpose of luring Wi-Fi stations (STAs) into connecting to them. An adversary clones the Service Set IDentifier (SSID) [Hotspot Name] as well as the MAC address of the legitimate AP while setting up the rogue twin. When a STA checks for the list of available APs (under presence of rogue twin), it sees only one AP (despite there being two APs with the identical SSID and MAC address). In case the signal strength of the rogue twin is more than the legitimate AP, the STA connects to the rogue twin.In the present study, a Discrete Event System (DES) paradigm based Intrusion Detection System (IDS) for detecting rogue twin which is proposed. It overcomes many drawbacks of existing approaches in tackling rogue twin. A normal DES model corresponding to a frame exchange under normal network conditions along with a failure (attacker) DES model corresponding to a frame exchange under rogue twin network conditions is constructed. Using the knowledge of the normal and attacker DES models, a DES diagnoser is constructed to ascertain whether the frame exchange corresponds to a normal or attack condition. Even if an attacker uses multiple techniques to launch the rogue twin the proposed DES based IDS is capable of identifying all such possible instances. We validate the scheme on a real test bed.

In this paper, the composition and operation mechanism of a multi-aircraft cooperative simulation system is analyzed, with its formal architecture identified as a discrete event system & differential equation system specifications (DEV&DESS). Then, the operation and control problem of the cooperative simulation system is converted into the simulation control problem of a specific DEV&DESS system. A coordinator-based test method for cooperative simulation is proposed and validated by tests of real-time mathematical simulation.

Sleep and wakefulness are promoted not by a single neural pathway but via wake or sleep-promoting nodes distributed across layers of the brain. We equate each layer with a brain region in proposing a layered subsumption model for arousal based on a computational architecture. Beyond the brainstem the layers include the diencephalon (hypothalamus, thalamus), basal ganglia, and cortex. In light of existing empirical evidence, we propose that each layer have sleep and wake computations driven by similar high-level architecture and processing units. Specifically, an interconnected wake-promoting system is suggested as driving arousal in each brain layer with the processing converging to produce the features of wakefulness. In contrast, sleep-promoting GABAergic neurons largely project to and inhibit wake-promoting neurons. We propose a general pattern of caudal wake-promoting and sleep-promoting neurons having a strong effect on overall behavior. However, while rostral brain layers have less influence on sleep and wake, through descending projections, they can subsume the activity of caudal brain layers to promote arousal. The two models presented in this work will suggest computations for the layering and hierarchy. Through dynamic system theory several hypotheses are introduced for the interaction of controllers and systems that correspond to the different populations of neurons at each layer. The models will be drawn-upon to discuss future experiments to elucidate the structure of the hierarchy that exists among the sleep-arousal architecture.

Due to the rapid increase in digitalization and networking of systems, especially in industrial environments, the number of cyber-physical systems is increasing and thus the danger of possible attacks on such systems. As demonstrated in several recent examples, a cyber attack on an industrial control system (ICS) can have catastrophic consequences for humans and machines. This is due to the communication networks between programmable logic controllers (PLC), actuators and sensors, which are usually vulnerable to attacks, as well as various processes or devices that are not sufficiently protected. In this paper, two possible attacks on a discrete event system (DES) are introduced, in which a running process, controlled by a PLC, is manipulated without being detected. Furthermore, a detection method for such attacks is presented.

The innovation trends in the area of health and medical treatments seek how to effectively test the devices to be introduced into the human body. This work discusses the different approaches to model the human cardiovascular system. Besides, a modelling proposal, based on hybrid systems (systems of continuous variables combined with discrete event systems), of the cardiovascular system is presented. Its purpose is evaluating implantable medical devices, such as Ventricular Assist Devices (VADs).

Patients’ overcrowding in Emergency Department (ED) is a major problem in medical care worldwide, predominantly due to time and resource constraints. Simulation modeling is an economical approach to solve complex healthcare problems. We employed Discrete Event System Specification-JAVA (DEVS-JAVA) based model to simulate and test changes in emergency service conditions with an overall goal to improve ED patient flow. Initially, we developed a system based on ED data from South Carolina hospitals. Later, we ran simulations on four different case scenarios. 1. Optimum number (no.) of doctors and patients needed to reduce average (avg) time of assignment (avg discharge time = 33 min). 2. Optimum no. of patients to reduce avg discharge time (avg wait time = 150 min). 3. Optimum no. of patients to reduce avg directing time to critical care (avg wait time = 58 min). 4. Optimum no. of patients to reduce avg directing time to another hospital (avg wait time = 93 min). Upon execution of above 4 simulations, 4 patients got discharged utilizing 3 doctors; 5 patients could be discharged from ED to home; 2 patients could be transferred from ED to critical care; 3 patients could be transferred from ED to another hospital. Our results suggest that the generated DEVS-JAVA simulation method seems extremely effective to solve time-dependent healthcare problems.