Showing papers on "Petri net published in 2019"

Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net

Abstract: Epilepsy is a common neurological disease that can cause seizures and loss of consciousness and can have a severe negative impact on long-term cognitive function. Reducing the severity of impact requires early diagnosis and treatment. Epilepsy is traditionally diagnosed using electroencephalography (EEG) performed by trained physicians or technicians but this process is time-consuming and prone to interference, which can negatively impact accuracy. This paper develops a model for epilepsy diagnosis using discrete wavelet transform to analyze sub-bands within the EEG parameter and select EEG characteristics for epilepsy detection. The minimize entropy principle approach is used to build fuzzy membership functions of the characteristics of each brain wave and are then used as the basis for the construction of an associative Petri net model. Using our APN model, the associative Petri net approach provides diagnosis accuracy rates of 93.8%, outperforming similar approaches using decision tree, support vector machine, neural network, Bayes net, naive Bayes, and tree augmented naive Bayes. Thus, the proposed approach shows promise for fast, accurate, and objective diagnosis of epilepsy in clinical settings.

The reachability problem for Petri nets is not elementary

Abstract: Petri nets, also known as vector addition systems, are a long established model of concurrency with extensive applications in modelling and analysis of hardware, software and database systems, as well as chemical, biological and business processes. The central algorithmic problem for Petri nets is reachability: whether from the given initial configuration there exists a sequence of valid execution steps that reaches the given final configuration. The complexity of the problem has remained unsettled since the 1960s, and it is one of the most prominent open questions in the theory of verification. Decidability was proved by Mayr in his seminal STOC 1981 work, and the currently best published upper bound is non-primitive recursive Ackermannian of Leroux and Schmitz from LICS 2019. We establish a non-elementary lower bound, i.e. that the reachability problem needs a tower of exponentials of time and space. Until this work, the best lower bound has been exponential space, due to Lipton in 1976. The new lower bound is a major breakthrough for several reasons. Firstly, it shows that the reachability problem is much harder than the coverability (i.e., state reachability) problem, which is also ubiquitous but has been known to be complete for exponential space since the late 1970s. Secondly, it implies that a plethora of problems from formal languages, logic, concurrent systems, process calculi and other areas, that are known to admit reductions from the Petri nets reachability problem, are also not elementary. Thirdly, it makes obsolete the currently best lower bounds for the reachability problems for two key extensions of Petri nets: with branching and with a pushdown stack. At the heart of our proof is a novel gadget so called the factorial amplifier that, assuming availability of counters that are zero testable and bounded by k, guarantees to produce arbitrarily large pairs of values whose ratio is exactly the factorial of k. We also develop a novel construction that uses arbitrarily large pairs of values with ratio R to provide zero testable counters that are bounded by R. Repeatedly composing the factorial amplifier with itself by means of the construction then enables us to compute in linear time Petri nets that simulate Minsky machines whose counters are bounded by a tower of exponentials, which yields the non-elementary lower bound. By refining this scheme further, we in fact establish hardness for h-exponential space already for Petri nets with h + 13 counters.

Robust Deadlock Control for Automated Manufacturing Systems With Unreliable Resources Based on Petri Net Reachability Graphs

Abstract: Resource failures may happen in automated manufacturing systems (AMSs) because of different reasons in the real world, making most existing deadlock control policies unapplicable. This paper develops methods for the robust deadlock control of AMSs with unreliable resources based on Petri nets. The considered AMSs are modeled with generalized systems of simple sequential processes with resources (GS3PR). First, a method based on reachability graph partition technique is provided to analyze the robust legal markings and the forbidden ones in an unreliable GS3PR (U-GS3PR), in which resource failures and recovery procedures are modeled with recovery subnets. Then, the control problem for such a system is converted into a problem for controlling the forbidden states in a U-GS3PR and control places can be designed by solving the maximal number of forbidden markings problems. Since the robust legal reachability spaces computed may be nonconvex and such a system cannot be optimally controlled by the conjunctions of linear constraints, we propose an interval-inhibitor-arc-based robust deadlock control policy for a system with nonconvex legal reachability spaces by solving the maximal number of ${t_{q}}$ -critical marking/transition separation instances problems (MNTMPs( ${t_{q}}$ )). Finally, examples are presented to demonstrate the proposed methods.

A simulation engine for stochastic timed petri nets and application to emergency healthcare systems

Abstract: In many service delivery systems, the quantity of available resources is often a decisive factor of service quality. Resources can be personnel, offices, devices, supplies, and so on, depending on the nature of the services a system provides. Although service computing has been an active research topic for decades, general approaches that assess the impact of resource provisioning on service quality matrices in a rigorous way remain to be seen. Petri nets have been a popular formalism for modeling systems exhibiting behaviors of competition and concurrency for almost a half century. Stochastic timed Petri nets ( STPN ), an extension to regular Petri nets, are a powerful tool for system performance evaluation. However, we did not find any single existing STPN software tool that supports all timed transition firing policies and server types, not to mention resource provisioning and requirement analysis. This paper presents a generic and resource oriented STPN simulation engine that provides all critical features necessary for the analysis of service delivery system quality vs. resource provisioning. The power of the simulation system is illustrated by an application to emergency health care systems.

Trajectory generation for robotic assembly operations using learning by demonstration

Abstract: The number of industrial robots and the type of industrial applications for which they are used, such as assembly operations, has increased considerably in the last few decades. However, programming continues to be a task which requires a high level of technical knowledge, and which is quite time consuming. This document proposes a methodology for the execution of parts assembly operations, using Learning by Demonstration (LbD) techniques, such as TP-GMM (Task Parametrized Gaussian Mixture Model). Task demonstrations were carried out by a person, and were visually acquired using a Kinect motion sensor. Later, these were used to train probabilistic models. Petri nets were used for the automatic generation of assembly plans, from those parts detected by the Kinect. Finally, operations were simulated in RobotStudio software, and later performed by an ABB IRB 140 robot. The results obtained demonstrate that it is possible to use programming techniques, which are different from traditional methodology such as LbD, to obtain satisfactory results in trajectories generation for simple assembly operations.

Fault Identification of Discrete Event Systems Modeled by Petri Nets With Unobservable Transitions

Abstract: This paper deals with the identification problem of faulty behavior in a discrete event system, assuming that the fault-free model of a system is given in terms of Petri nets, where the set of transitions is divided into two disjoint subsets: 1) observable and 2) unobservable ones. The observed system output is defined as a transition-marking sequence, i.e., each transition is followed by a marking. First, a nonlinear integer programming model that characterizes the faults modeled by fault transitions is built according to the abnormal behavior extracted from the observed sequence. Then, it is converted into an integer linear programming (ILP) problem and a faulty net that preserves the structure of the fault-free one is obtained by solving this ILP model. In addition, an algorithm is developed to ensure acyclicity of the resulting unobservable subnet whose transition set is composed of the unobservable transitions of the fault-free net and the identified fault transitions.

The Quantitative Verification Benchmark Set

Abstract: We present an extensive collection of quantitative models to facilitate the development, comparison, and benchmarking of new verification algorithms and tools. All models have a formal semantics in terms of extensions of Markov chains, are provided in the Jani format, and are documented by a comprehensive set of metadata. The collection is highly diverse: it includes established probabilistic verification and planning benchmarks, industrial case studies, models of biological systems, dynamic fault trees, and Petri net examples, all originally specified in a variety of modelling languages. It archives detailed tool performance data for each model, enabling immediate comparisons between tools and among tool versions over time. The collection is easy to access via a client-side web application at qcomp.org with powerful search and visualisation features. It can be extended via a Git-based submission process, and is openly accessible according to the terms of the CC-BY license.

Describing Behavior of Processes with Many-to-Many Interactions

Abstract: Processes are a key application area for formal models of concurrency. The core concepts of Petri nets have been adopted in research and industrial practice to describe and analyze the behavior of processes where each instance is executed in isolation. Unaddressed challenges arise when instances of processes may interact with each other in a one-to-many or many-to-many fashion. So far, behavioral models for describing such behavior either also include an explicit data model of the processes to describe many-to-many interactions, or cannot provide precise operational semantics.

Diagrammatic algebra: from linear to concurrent systems

Abstract: We introduce the resource calculus, a string diagrammatic language for concurrent systems. Significantly, it uses the same syntax and operational semantics as the signal flow calculus --- an algebraic formalism for signal flow graphs, which is a combinatorial model of computation of interest in control theory. Indeed, our approach stems from the simple but fruitful observation that, by replacing real numbers (modelling signals) with natural numbers (modelling resources) in the operational semantics, concurrent behaviour patterns emerge. The resource calculus is canonical: we equip it and its stateful extension with equational theories that characterise the underlying space of definable behaviours---a convex algebraic universe of additive relations---via isomorphisms of categories. Finally, we demonstrate that our calculus is sufficiently expressive to capture behaviour definable by classical Petri nets.

A Petri Net Based Deadlock Avoidance Policy for Flexible Manufacturing Systems With Assembly Operations and Multiple Resource Acquisition

Abstract: Efficient deadlock control policies are very important in the operation of flexible manufacturing systems (FMS). This paper focuses on deadlock control problems for a general class of FMS. They have three interesting characteristics from the application point of view. First, flexible routes of parts and assembly operations are allowed. Second, the number of parts of the same type in a product may be more than one. Third, an operation may require multiple resources. To characterize such FMS, a Petri net model that can deal well with all FMS characteristics is developed. Based on it, this paper proposes a Banker's-algorithm-like deadlock avoidance policy. The proposed policy is proved to be polynomial in the model size. Moreover, experimental results indicate its effectiveness and superiority over the state-of-the-art policies.

On a maximally permissive deadlock prevention policy for automated manufacturing systems by using resource-oriented Petri nets

Abstract: It is theoretically and practically significant to synthesize a maximally permissive (optimal) controller to prevent deadlocks in an automated manufacturing system (AMS). With an AMS being modeled with Petri nets, by the existing methods, integer linear programming (ILP) problems are usually formulated and solved to obtain optimal policies by forbidding illegal markings at the same time no legal marking is excluded. Without an efficient technique for solving an ILP, such a method is usually computationally prohibitive. A resource-oriented Petri net (ROPN) is employed to model a class of AMS for resolving the deadlock control problem with maximal permissiveness in this paper. Efficient methods are developed to figure out the key structures in an ROPN model for deadlock prevention. Based on the structural properties of ROPN models, this work explores several types of illegal markings that can be prohibited optimally by structural analysis. For these markings, a deadlock prevention policy can be derived in an algebraic way without solving a notorious ILP problem. For the other markings, linear programming (LP), instead of ILP, approaches are developed to forbid them optimally. Thus, a maximally permissive controller can be developed while the computational cost is reduced greatly. The proposed methods are verified by typical examples in the literature.

Petri Net-Based Specification of Cyber-Physical Systems Oriented to Control Direct Matrix Converters With Space Vector Modulation

Abstract: This paper proposes a Petri-net-based specification of cyber-physical systems dedicated to the control of a direct matrix converter with space vector modulation (SVM) and transistor commutation. The technique employed is further applied for hardware implementation in a programmable logic device [namely, field-programmable gate array (FPGA)]. Contrary to the traditional SVM computation methods, concurrency aspects of the digital devices are highly utilized in the presented solution. Therefore, the hardware system is specified by a live and safe Petri net, which is based on the parallelism. Moreover, such a specification can be easily analyzed and verified against the structural properties in order to avoid formal errors and prototyping mistakes (such as deadlocks or non-reachable states). The proposed idea is illustrated by a case-study example of the real prototype of the SVM algorithm. The system has been specified by a live and safe Petri net, analyzed, verified, and finally implemented in the FPGA device. The obtained results of the physical implementation are presented and discussed.

Petri net-based scheduling strategy and energy modeling for the cylinder block remanufacturing under uncertainty

Abstract: Scheduling has been extensively applied to remanufacturing for the organization of production activities, and it would directly influence the overall performance of the remanufacturing system. Since the conjunction of Petri net (PN) and artificial intelligence (AI) searching technique was demonstrated to be a promising approach to solve the scheduling problems in manufacturing systems, this study built a transition timed PN combined with heuristic A* algorithm to deal with the scheduling in remanufacturing. The PN was applied to the formulation of remanufacturing process, while the A* algorithm generated and searched for an optimal or near optimal feasible schedule through the reachability graph (RG). We took the high value-added cylinder block of engine as a research object to minimize the makespan of reprocessing a batch used components. This scheduling problem involved in batch and parallel processing machines, and the uncertain processing time and routes will complicate the scheduling problem. Three heuristics were designed to guide the search process through the RG in PN. To avoid state space explosion and select promising nodes, a new rule-based dynamic window was developed to improve the efficiency of the algorithm, and this rule was examined to outperform the conventional one. Under the determined scheduling strategy, the dynamic behavior of energy consumption rate during the processing time was simulated using PN tool, which would assist remanufacturers to develop potential strategies for energy efficiency improvement. Considering the uncertainty of processing time, the Monte Carlo simulation method was adopted to statistically analyze the distributions of makespan and total energy consumption, which would contribute to the comprehensive production scheduling and energy profile assessment for sustainable remanufacturing.

Dynamic Role Binding in Blockchain-Based Collaborative Business Processes

Abstract: Blockchain technology enables the execution of collaborative business processes involving mutually untrusted parties. Existing tools allow such processes to be modeled using high-level notations and compiled into smart contracts that can be deployed on blockchain platforms. However, these tools brush aside the question of who is allowed to execute which tasks in the process, either by deferring the question altogether or by adopting a static approach where all actors are bound to roles upon process instantiation. Yet, a key advantage of blockchains is their ability to support dynamic sets of actors. This paper presents a model for dynamic binding of actors to roles in collaborative processes and an associated binding policy specification language. The proposed language is endowed with a Petri net semantics, thus enabling policy consistency verification. The paper also outlines an approach to compile policy specifications into smart contracts for enforcement. An experimental evaluation shows that the cost of policy enforcement increases linearly with the number of roles and constraints.

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems

Abstract: In this paper, we first outline the motivation and the need for a new approach for online scheduling in flexible manufacturing systems (FMS) based on reinforcement learning (RL). We then present an initial concept for such an approach. In our method, we use Deep RL agents, who have learned to efficiently guide products through the plant and achieve near-optimal timing regarding resource allocation. Each product is controlled by its own agent, which can handle unforeseen machine failures, re-configurations of the plant topology and the consideration of local and global optimization goals. We created a virtual representation of the FMS using a Petri net, modelling the plant topology and the product flow. The agents' task is to navigate the product to the corresponding machine by choosing the according transition of the Petri net. The training consists of four stages from learning rough rules in order to fulfill a job in a Single-Agent RL setup to learning thoughtful collaboration between agents in a Multi-Agent RL (MARL) setup. We proved the feasibility of the first and second training stage by applying it to the virtual depiction of a specific research plant and obtained promising results. With this concept of a self-learning control policy, online-scheduling can be applied with less effort required to customize the setup for various FMSs.

Change propagation and bidirectionality in internal transformation DSLs

Abstract: Despite good results in several industrial projects, model-driven engineering (MDE) has not been widely adopted in industry. Although MDE has existed for more than a decade now, the lack of tool support is still one of the major problems, according to studies by Staron and Mohaghegi (Staron, in: Model driven engineering languages and systems, Springer, Berlin, 2006; Mohagheghi et al. in Empir Softw Eng 18(1):89–116, 2013). Internal languages offer a solution to this problem for model transformations, which are a key part of MDE. Developers can use existing tools of host languages to create model transformations in a familiar environment. These internal languages, however, typically lack key features such as change propagation or bidirectional transformations. In our opinion, one reason is that existing formalisms for these properties are not well suited for textual languages. In this paper, we present a new formalism describing incremental, bidirectional model synchronizations using synchronization blocks. We prove the ability of this formalism to detect and repair inconsistencies and show its hippocraticness. We use this formalism to create a single internal model transformation language for unidirectional and bidirectional model transformations with optional change propagation. In total, we currently provide 18 operation modes based on a single specification. At the same time, the language may reuse tool support for C#. We validate the applicability of our language using a synthetic example with a transformation from finite state machines to Petri nets where we achieved speedups of up to multiple orders of magnitude compared to classical batch transformations.

Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management

Abstract: This chapter considers the importance of decision support systems for supply chain risk management (SCRM). The first part provides an overview of the different operations research techniques and methodologies for decision making for managing risks, focusing on multiple-criteria decision analysis methods and mathematical programming. The second part is devoted to artificial intelligence (AI) techniques which have been applied in the SCRM domain to analyse data and make decisions regarding possible risks. These include Petri nets, multi-agent systems, automated reasoning and machine learning. The chapter concludes with a discussion of potential ways in which future decision support systems for SCRM can benefit from recent advances in AI research.

Information Systems Modeling: Language, Verification, and Tool Support

Abstract: Information and processes are both important aspects of information systems. Nevertheless, most existing languages for modeling information systems focus either on one or the other. Languages that focus on information modeling often neglect the fact that information is manipulated by processes, while languages that focus on processes abstract from the structure of the information. In this paper, we present an approach for modeling and verification of information systems that combines information models and process models using an automated theorem prover. In our approach, set theory and first-order logic are used to express the structure and constraints of information, while Petri nets of a special kind, called Petri nets with identifiers, are used to capture the dynamic aspects of the systems. The proposed approach exhibits a unique balance between expressiveness and formal foundation, as it allows capturing a wide range of information systems, including infinite state systems, while allowing for automated verification, as it ensures the decidability of the reachability problem. The approach was implemented in a publicly available modeling and simulation tool and used in teaching of Information Systems students.

Coloured Petri nets for multilevel, multiscale and multidimensional modelling of biological systems.

Abstract: Owing to the availability of data of one biological phenomenon at different levels/scales, modelling of biological systems is moving from single level/scale to multiple levels/scales, which introduces a number of challenges. Coloured Petri nets (ColPNs) have been successfully applied to multilevel, multiscale and multidimensional modelling of some biological systems, addressing many of these challenges. In this article, we first review the basics of ColPNs and some popular extensions, and then their applications for multilevel, multiscale and multidimensional modelling of biological systems. This understanding of how to use ColPNs for modelling biological systems will assist readers in selecting appropriate ColPN classes for specific modelling circumstances.

A user requirement oriented web service discovery approach based on logic and threshold petri net

Abstract: In recent years, the number of Web services has increased significantly. Web service discovery has drawn much attention with the development of Web service applications and big data analysis. Under this circumstance, traditional Web service discovery strategies cannot adequately meet high user requirements due to the efficiency and precision of service discovery is low. In order to improve the accuracy and efficiency of service discovery, a user requirement oriented Web service discovery approach based on Petri nets is proposed in this study. A data preprocessing strategy of Web service is first designed. Then, a service clustering method is proposed based on Petri nets, which can conduct service cluster head generation, service cluster composition, and service discovery. The proposed method utilizes a superior data preprocessing method. Using simulation experiments, the efficiency and precision of Web service discovery are illustrated. Finally, the application value of the approach on real Web service is discussed.

Enforcement of Diagnosability in Labeled Petri Nets via Optimal Sensor Selection

Abstract: In this paper, we deal with the problem of enforcing diagnosability to labeled Petri nets (PNs) appropriately adding new sensors. We show that, solving an integer linear programming problem, it is possible to select a solution that is optimal with respect to a given objective function (e.g., the cost of sensors). The solution is based on two notions, already introduced by the authors in previous works, namely basis marking and unfolded verifier . This allows to solve the considered problem in a more efficient way with respect to other approaches in the literature. Finally, we propose an algorithm to compute the smallest value of $K$ such that the PN system is $K$ -diagnosable under the new labeling function, which implies that faults can be detected in at most $K$ observations after their occurrence.

On Algebraic Identification of Critical States for Deadlock Control in Automated Manufacturing Systems Modeled With Petri Nets

Abstract: Petri nets are an important and popular tool to model and analyze deadlocks in automated manufacturing systems. The state space of a Petri net model can be divided into two disjoint parts: a live-zone and a dead-zone. A first-met bad marking (FBM) is a marking in the dead-zone, representing the very first entry from the live-zone to the dead-zone, and the calculation of FBMs to a large extent contributes to the complexity of designing optimal liveness-enforcing supervisors. Most existing studies have to fully enumerate the reachable markings of a Petri net model to obtain the FBMs, which exacerbates the computational overheads. This paper first explores a variation mechanism of calculating FBMs with respect to the resource capacity in a class of S 3 PR (Systems of Simple Sequential Processes with Resources) from the structural analysis perspective, which contains a ξ-resource. More generally, for the class of S 3 PR with an η-resource as defined in this paper, the FBMs can be calculated in an algebraic way by a customized structural analysis technique without enumerating all the reachable markings. Finally, the variation mechanism of calculating FBMs is revealed for these considered classes of Petri net models. Examples are given to demonstrate the proposed method.

Research on Smart Contract Optimization Method on Blockchain

Abstract: The smart contract on the blockchain allows credible transactions without a third party. These transactions are traceable and irreversible. The deployment and implementation of smart contracts in Ethernet will consume some gas, which will directly affect the cost of smart contracts. In order to reduce the consumption of gas during the execution of smart contracts, this article proposes an optimization algorithm for generating business process smart contracts. First, business process modeling notation (BPMN) models are extended to Petri nets. Second, Petri nets are simplified to find nodes in BPMN models that can be considered fusion tasks. Using new mapping rules from the BPMN model to solidity language, BPMN model is generated into Ethereum Smart contract model. In the BPMN models with multilayer fusion task, experimental results show that the proposed algorithm can save 15% gas on average for business processes with multiple fusion tasks.

Formal Verification of Blockchain Smart Contract Based on Colored Petri Net Models

Abstract: A smart contract is a computer protocol intended to digitally facilitate and enforce the negotiation of a contract in undependable environment. However, the number of attacks using the vulnerabilities of the smart contracts is also growing in recent years. Many solutions have been proposed in order to deal with them, such as documenting vulnerabilities or setting the security strategies. Among them, the most influential progress is made by the formal verification method. In this paper, we propose a formal verification method based on Colored Petri Nets (CPN) to verify smart contracts in blockchain system. First, we develop the smart contract models with possible attacker models based on hierarchical CPN modeling, then the smart contract models are executed by step-by-step simulation to validate their functional correctness, and finally we utilize the branch timing logic ASK-CTL based model checking technology in the CPN tools to detect latent vulnerabilities in smart contracts. We demonstrate that our CPN modeling based verification method can not only detect the logical vulnerabilities of the smart contract, but also consider the impacts of users behavior to find out potential non-logical vulnerabilities in the contracts, such as the vulnerabilities caused by the limitations of the Solidity language.

Decay Replay Mining to Predict Next Process Events

Abstract: In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event from a process state. However, such deep learning models by themselves lack a clear representation of the process states. At the same time, recent methods have neglected the time feature of event instances. In this paper, we take advantage of Petri nets as a powerful tool in modeling complex process behaviors considering time as an elemental variable. We propose an approach which starts from a Petri net process model constructed by a process mining algorithm. We enhance the Petri net model with time decay functions to create continuous process state samples. Finally, we use these samples in combination with discrete token movement counters and Petri net markings to train a deep learning model that predicts the next event. We demonstrate significant performance improvements and outperform the state-of-the-art methods on nine real-world benchmark event logs.

Web Service Interaction Modeling with Colored Petri Nets

Abstract: The investigation focuses on the use of Petri Nets for web services interaction modeling. It is discovered that Hierarchical Time Petri Nets is the most effective way to create web service interaction models. That is due to the fact that they can be used to build hierarchical models of complex systems, and investigate their dynamics. Moreover, the research describes a custom algebra-algorithmic system. It comprises the basis for further web services semantics and description. The models are developed with the custom algebra-algorithmic system operations. Service models are created with Petri colored nets. Models and imitational modeling are performed in the CPN-tools environment.

Single Controller-Based Colored Petri Nets for Deadlock Control in Automated Manufacturing Systems

Abstract: Deadlock control approaches based on Petri nets are usually implemented by adding control places and related arcs to the Petri net model of a system. The main disadvantage of the existing policies is that many control places and associated arcs are added to the initially constructed Petri net model, which significantly increases the complexity of the supervisor of the Petri net model. The objective of this study is to develop a two-step robust deadlock control approach. In the first step, we use a method of deadlock prevention based on strict minimal siphons (SMSs) to create a controlled Petri net model. In the second step, all control places obtained in the first step are merged into a single control place based on the colored Petri net to mark all SMSs. Finally, we compare the proposed method with the existing methods from the literature.