Petri Net Method Research Articles

Research of methods of modeling of mass service enterprise

Objective. The purpose of the study is to use artificial intelligence tools to optimize and model the automated organization of work at a mass service enterprise. Method. The research is based on the use of a Petri net as a modeling tool, as well as mathematical hardware that makes it possible to implement simulation models of subsystems and conduct additional research on GPSS artificial intelligence models. Result. To process the results of the modeling process, the Petri net modeling method was used and a network activity model graph was developed. The modeling process used with the Petri Net application allows such functions to be described more clearly. The principles of modeling systems based on the GPSS language and Petri nets are considered. Conclusion. It is advisable to use the language devices GPSS and SP, which provide information support for the activities of a mass service enterprise, modeling the structural organization and information flows.

Urban Advanced Mobility Dependability: A Model-Based Quantification on Vehicular Ad Hoc Networks with Virtual Machine Migration

In the rapidly evolving urban advanced mobility (UAM) sphere, Vehicular Ad Hoc Networks (VANETs) are crucial for robust communication and operational efficiency in future urban environments. This paper quantifies VANETs to improve their reliability and availability, essential for integrating UAM into urban infrastructures. It proposes a novel Stochastic Petri Nets (SPN) method for evaluating VANET-based Vehicle Communication and Control (VCC) architectures, crucial given the dynamic demands of UAM. The SPN model, incorporating virtual machine (VM) migration and Edge Computing, addresses VANET integration challenges with Edge Computing. It uses stochastic elements to mirror VANET scenarios, enhancing network robustness and dependability, vital for the operational integrity of UAM. Case studies using this model offer insights into system availability and reliability, guiding VANET optimizations for UAM. The paper also applies a Design of Experiments (DoE) approach for a sensitivity analysis of SPN components, identifying key parameters affecting system availability. This is critical for refining the model for UAM efficiency. This research is significant for monitoring UAM systems in future cities, presenting a cost-effective framework over traditional methods and advancing VANET reliability and availability in urban mobility contexts.

A fire risk assessment system and application for process industry using fuzzy Petri net and HAZID method

The safety risk assessment of process industries can determine the degree of safety risk and remind or warn safety management personnel to take corresponding measures according to the probability of accidents. In this paper, hazard identification (HAZID) method is combined with fuzzy Petri net to construct a hybrid safety assessment system. Firstly, the fire risk factors of HAZID are determined according to the fire statistics data. By dividing the whole industrial process into four stages, the complex hazard sources in the factory are investigated item by item, thus the fuzzy Petri net of real-time risk assessment is constructed. Taking an acetic acid enterprise as a case, this paper introduces how to construct such a risk assessment system in detail, and the system can dynamically predict the risk with changes in process conditions. This also demonstrates the feasibility of this method, which can be used as a reference for related research.

Логическая модель построения пошаговой контекстной помощи пользователю САПР

Automatic stepwise contextual help for CAD systems users reduces time to solve the application task since it saves time to search the prompt message in system documentation. Petri nets (PN) can be used to bind available actions of the user considering the state of application data (context). Application of the Petri net inversion method, which uses limited enumeration to construct chains of recommended actions is preferable than using the standard reachability analysis procedure based on exhaustive enumeration. However, the absence in the known implementations of an explicit separation of the axioms of inversion (knowledge) from the mechanism of their processing (inference) deprives the stepwise contextual help system of the necessary flexibility when changing the axioms to consider the assumptions associated with a particular model. Thus, the aim of this research is to provide the necessary flexibility of the contextual help system by separating the knowledge representation model from the inference engine. A colored PN is used as a model of user action scenarios. The inversion axioms are implemented in the PROLOG language. The standard inference engine of the PROLOG language is used as a tool to construct chains of recommended actions. The authors have proposed an axiomatic model of PN inversion and a method to construct a stepwise contextual help by the standard inference engine of the PROLOG language. The method differs by explicit separation of knowledge (inversion axioms) from the inference engine (stepwise recommendations). It reduces the computational costs of adapting the contextual help system when changing the inversion axioms. The proposed method allows to reduce the time spent on adapting the contextual help system, since the field of the changes is limited by the declarations of the inversion axioms. The reliability of the results is confirmed since the proposed method of contextual help is used for the CAD “Model and Archive” user of CSoft company. The results obtained allow creating contextual help services for existing applications with minimal changes to their code base.

An in vitro experiment for remote health monitoring of implanted cardiac pacemakers using Petri nets

For heart disease patients with an implanted artificial pacemaker, monitoring the operational status of the pacemaker is a crucial and challenging issue. This study aims to design a transcutaneous optic telemetry (TOT) system that transmits light signals from a human body through the skin to a receiving circuit and carries pacemaker status information via the light signals to achieve transcutaneous telemetry. Petri net method is used to analyze the simulated pacemaker. Petri nets are constructed to illustrate the process of the pacemaker. Furthermore, the Petri nets for the process with four checkpoints (CPs) deployed on the pacemaker are also constructed. The TOT system uses near-infrared signals generated by a light-emitting diode as a transmission medium, and the pork skin animal model is used to validate the TOT system’s feasibility and efficiency. The correct sequence of CPs determines the normal state of the pacemaker, and the information from these CPs is transmitted in vitro for interpretation through the TOT system. This study designed and implemented the TOT system. Experiments showed that the TOT system’s design is effective in transmitting the CPs information through the pigskin layer without any significant interference. The results proved that the TOT system can transmit CPs information outside the body to achieve the transcutaneous telemetry diagnosis of the pacemaker. This study provides an alternative means to surgical operation for implanted pacemakers examination.

Dynamic and dependent tree theory (D2T2): A framework for the analysis of fault trees with dependent basic events

• A fault tree framework is provided which overcomes limitations in conventional analysis methods. • Component failure events can have any distribution of failure or repair times. • Component dependencies can be accommodated. • Complex maintenance processes for components and sub-systems can be incorporated. • The algorithm identifies independent sub-problems which are solved accurately and efficiently. Fault tree analysis remains the most commonly employed method, particularly in the safety critical industries, to predict the probability or frequency of system failures. Whilst it has its origins back in the 1960s, the assumptions employed in the majority of commercial fault tree analysis codes have not changed significantly since this time and restrict the ability of the method to represent features of the design, operation and maintenance of modern industrial systems. The inability to include general dependencies between the basic events, the requirement for invariant failure and repair rates, and the inability to account for complex maintenance strategies are major limitations. This paper proposes a new fault tree analysis framework which can overcome these restrictions. Whilst retaining the fault tree structure to express the causality of the system failure, the internal calculation method is updated by exploiting features of the Binary Decision Diagram, Stochastic Petri Net and Markov methods. The key elements of the D 2 T 2 algorithm are described in detail and the framework demonstrated through application to a case study example of a pressure vessel cooling system.

Modeling and Analyzing User Behavior Risks in Online Shopping Processes Based on Data-Driven and Petri-Net Methods

With the rapid spread of e-commerce and e-payment, the increasing number of people choose online shopping instead of traditional buying way. However, the malicious user behaviors have a significant influence on the security of users' accounts and property. In order to guarantee the security of shopping environment, a method based on Complex Event Process (CEP) and Colored Petri nets (CPN) is proposed in this paper. CEP is a data-driven technology that can correlate and process a large amount of data according to Event Patterns, and CPN is a formal model that can simulate and verify the specifications of the online shopping processes. In this work, we first define the modeling scheme to depict the user behaviors and Event Patterns of online shopping processes based on CPN. The Event Patterns can be constructed and verified by formal methods, which guarantees the correctness of Event Patterns. After that, the Event Patterns are translated into Event Pattern Language (EPL) according to the corresponding algorithms. Finally, the EPLs can be inserted into the complex event processing engine to analyze the users' behavior flows in real-time. In this paper, we validate the effectiveness of the proposed method through case studies.

Data security-web login authentication process using password generating tile array token interval timed coloured Petri nets

In this paper, 'Password Generating Tile Array Token Interval Timed Coloured Petri Nets' is proposed to create a secured password. The proposed method provides a new process of authentication for individuals to secure their password. In spite of using only alphabets and characters as a password, the web users can use tiles as their password, which has alphabets or characters on it. It provides a new security to protect the information and gives a big challenge for the hackers to identify the password. The main objective of this paper is 'to provide high security with low-computational cost'. The proposed password generating tile array token interval timed coloured Petri nets (PGTAITCPN) method provides low computational cost 45.6%, low latency 65.3% and high-security level 95.6% better performance when compared with the existing methods, such as MEAS and OTPT.

An intuitionistic fuzzy probabilistic Petri net method for risk assessment on submarine pipeline leakage failure

Under the influence of various factors, the submarine pipelines will be damaged, resulting in oil and gas leakage accidents. It is essential to perform risk assessments to prevent accidents. However, accurate and sufficient historical data usually can not be obtained. To solve this problem, this study proposes a probabilistic Petri net (PPN) method based on intuitionistic fuzzy evidential reasoning (IFER), which can determine the failure probability of basic events through domain expert experience, and quantitatively evaluate the probability of submarine pipeline leakage failure. The main contributes include: according to DNV-RP-F107, a group of intuitionistic fuzzy numbers is defined and an improved probability conversion method is proposed for defuzzification; an IFER approach is proposed, which uses belief structures as expert linguistic terms and transforms them into IFNs to deal with the uncertainty of expert evaluation; a probabilistic Petri net method is proposed to quantitatively calculate the failure probability of top event and identify the critical events. Finally, the proposed intuitionistic fuzzy probabilistic Petri net (IFPPN) method is illustrated by a case study. By comparing with the existing methods, the feasibility of this method is validated, and the evaluation results can provide a reference for the safe operation of the pipeline system.

Behavior Data Analysis of English Learners Based on Discrete Dynamic System Modeling Method.

With the rapid development of computer science, there are more and more kinds of discrete dynamic systems. Computer integrated system CIMS, network communication database administrator system, and human behavior analysis system are all discrete dynamic systems. At present, many researchers have studied by adding human behavior data to discrete dynamic systems. This paper aims to study the behavior data of English learners by using the discrete dynamic modeling technology of complex systems and the discrete dynamic system modeling method of Petri nets. By adding the behavior data of learners to the discrete dynamic system of fuzzy Petri nets, the system is diagnosed and optimized. The experimental results show that the complex discrete dynamic system in this paper has achieved good experimental results according to the performance indicators selected in theory. Based on the combination of the above technologies and systems, the fuzzy Petri net discrete dynamic system studied in this paper improves the processing speed of English learners' behavior data.

Integrated Offsite Logistics Scheduling Approach for High-Rise Modular Building Projects

By maximizing offsite work and minimizing onsite activities, modular construction has become increasingly popular in high-rise, high-density cities. For successfully delivering high-rise modular buildings, a key issue is the effective scheduling of offsite logistics, consisting of module manufacturing, transportation, and storage. This paper proposes an integrated offsite logistics scheduling approach for high-rise modular building projects that addresses project-specific constraints and uncertainties, based on the stochastic Petri net (SPN) method. The proposed approach supports multi-criteria decision-making, that considers completion time, total cost, storage capacity utilization, and production process stability under what-if scenarios. Its efficacy and novelty for offsite logistics scheduling were demonstrated through a real-life project case study. The results indicate the need to (1) strengthen suppliers’ demand responsiveness by shortening module production time; (2) enhance manufacturing flexibility by increasing factory storage utilization; (3) improve supply chain synchronization; and (4) capitalize on the government’s role in offsite logistics enhancement. The findings methodologically innovate offsite logistics scheduling, and practically support contractors and module suppliers in successfully delivering high-rise modular building projects in high-density cities.

A smart guidance navigation robot using petri net, database location, and radio frequency identification

The objective of this research is, to explain a new framework to navigate the movement of the robot towards a target goal. This involves the need for the robot to move from the initial position to 1 out of 30 rooms. Therefore the strategy used involves the combination of the room database stored in the RFID data using the petri net (PN) method to simulate and model the movement of the robot for navigation after which the dynamic behavior of the robot is moving to the desired location was analyzed. The process started from the creation of an environmental map determined by the user followed by modeling through PN and the result was used to produce a marking value which explains and navigates the movement of the robot towards the selected room. The marking value was also used as the database for the robot's movement and later substituted with the RFID to be used as the sensor input in the implementation stage. It was concluded that the robot has the ability to move to the target position according to the database stored in RFID and designed to move forward and turn left and right. For example, it followed the marking value M1 M2 M3M13M12 M11 M10 M9 M8 to Room 1 and M1 M2 M46 M47 to Room 29.

Dynamic risk assessment of deep-water dual gradient drilling with SMD system using an uncertain DBN-based comprehensive method

The subsea mudlift drilling system (SMD) belongs to the system of Dual Gradient drilling. When the natural gas hydrate layer is encountered with the SMD system during the drilling process, significant risks and failures are resulted by the decomposition and secondary formation of the hydrate. To predict failures, the environmental factors, human factors, and equipment factors are analyzed in this study. Firstly, after the fault tree is established, it is transformed into Bayesian Network (BN) using the mapping algorithm. Secondly, the Leaky Noisy-OR node is added to BN and the uncertain influence of the logical relationship is considered. Then, the established BN is transformed into the uncertainty Dynamic Bayesian Networks (UDBNs) through the transition probability matrix, if the dynamic uncertainty of equipment factors and human factors are considered. In addition, the cognitive reliability and error analysis method (CREAM) are used to determine the prior probability of human factors in the DBN model. Moreover, we also use fuzzy theory and expert judgment to quantify the prior probability of equipment failure. At the end of the experiment, the ultimate result shows that the UDBNs model, derived by the existing data, can be used to predict the risk of lost circulation (LC), sticking, and blockage during the drilling process and the dynamic success probability at different stages of the shut-in process. The correctness of the established UDBNs model is verified by the Petri nets method.

Using integrated Bayesian-Petri Net method for individual impact assessment of domino effect accidents

One of the aims of Cleaner Production refers to the reduction of risks for the human population. Domino effect accidents trigger the worst consequences in process industries. To date, several techniques and models have been used for predicting domino effect probability, such as Bayesian networks and Petri Nets. In addition, these methodologies can be combined with other classical ones, for instance, Event and Fault Trees. However, there is no mentioned methodology in specialized literature which accurately combines them in order to predict the individual impact of domino effect accidents. This paper focuses on the quantification of the individual impact of domino effect scenarios using an Integrated Bayesian- Petri Net method. For this purpose, a model of seven steps is proposed based on a combination of proven methods to obtain a new one with the advantages of both graphical structures, including the ability to minimize their limitations. To prove the validity of the new model, this model and other three ones are applied in a hydrocarbon storage area as well. The application of the proposed model in this area allows to reduce the incident occurrence by 59%. That is why we can assure that the proposed study is vital for preserving resources and minimizing risk to individual’s life in hydrocarbon facilities.

Disassembly sequence planning of waste auto parts

ABSTRACT The disassembly of used products is a critical procedure in remanufacturing, and different disassembly strategies are often obtained from different perspectives. To describe the disassembly process more accurately, the uncertainty of the information in the disassembly process should be considered. Therefore, random variables are introduced for disassembly time, cost, and effort. Based on the extended stochastic Petri net modeling method and stochastic programming theory, a stochastic optimization algorithm combined with artificial intelligence technology and a multiobjective genetic algorithm are designed, and a multiobjective optimization model for the disassembly sequence of used car parts under uncertain conditions is successfully constructed. This model considers the viewpoint of the decision maker. Moreover, the Monte Carlo method is applied to solve the multiobjective optimization model, and the validity and practicability of the model are verified by an example of an automotive transmission. Implications: With the rapid development of the economy and the shortening of the product life cycle, the rate of product renewal is getting faster and faster, which also leads to the production of a large number of waste products. According to the forecast of the relevant departments, it is estimated that, in 2020, there will be about 35 million used televisions, 15 million used refrigerators, 13 million used washing machines, 12 million used air conditioners, 57 million used computers, and 8.3 million scrapped cars. Waste products contain a lot of renewable resources. If they cannot be effectively recycled, it will be a great waste of resources, and unreasonable disposal of waste products may have a negative impact on the environment. Therefore, due to environmental pressure and economic drive, product recycling and remanufacturing activities have caused widespread concern in society. Disassembly is defined as the operation or activity of disassembling an assembly such as a product, assembly, or component, and is the result of multiple removal operations of the product. It is a prerequisite for the efficient recycling of products and the first link in remanufacturing, that is, disassembly as a new production activity, which can provide raw materials for the smooth progress of the remanufacturing production plan, namely, old rough or used parts. Efficient dismantling not only saves natural resources and energy, but also effectively reduces environmental pollution. It is also an important guarantee for promoting the healthy development of the circular economy and achieving sustainable industrial development. The length of time required for the dismantling process, the level of costs, and the amount of profits obtained will directly affect the economic benefits of the recycling of end-of-life products. Therefore, the evaluation and optimization of the dismantling process of waste products have become one of the current hot issues. The research on the dismantling of waste products is conducive to speeding up the recycling process of waste, to a greater extent, the rapid and full recovery of resources, and to a certain extent, it will provide value basis and theoretical significance for subsequent research.

Reliability Modeling and Analysis of Reactor Protect System Based on Petri Net

Digital control is an inevitable trend in the development of nuclear power. The application of digital control systems in nuclear power plants not only improves the control capability of nuclear power plants, but also increases the complexity of the system. Traditional reliability analysis methods (such as event trees and fault trees) can only perform static analysis on the reactor protection system, but cannot reflect the impact of maintenance activities on system reliability. In the dynamic analysis method, the Markov model is usually applied to the exponential distribution, and the failure and maintenance process of the reactor protection system does not all obey the exponential distribution. In this paper, the Petri net method is used to model the reactor protection system, and the modules are modeled separately according to the function of the system, and finally the model is integrated. Modularization improves the flexibility and understandability of the model, shortens modeling time, and improves robustness. The proposed model was simulated using the GPenSIM tool, and the state reachability diagram of the system with or without maintenance process was obtained, and the probability of shutdown and refusal was obtained.

RETRACTED ARTICLE: Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques

Literature survey shows that the recommendation systems have been largely adapted and evaluated in various domains. Due to low performances from various cyber attacks, the adoption of recommender system is in the initial stage of defense systems. One of the most common attacks for recommender system is shilling attack. There are some existing techniques for identifying the shilling attacks built in the user ratings patterns. The performance of ratings on target items differs between the attack user profiles and actual user profiles. To differentiate the certain profiles, the affected profiles are known as attack profiles. Besides the shilling attacks, real cyber attacks are taking place in the community which are being solved by Petri Net methods. These attacks can be falsely predicted (shilling attacks) by the users which can raise security threats. For identifying various shilling attacks without a priori knowledge, Recommendation System suffers from low accuracy. Basically, recommendation attack is split into nuke and push attack that encourage and discourage the recommended target item. The strength of shilling attack is usually measured by filler size and attack size. An experiment over unsupervised machine learning algorithms with filler size 3% over 3%, 5%, 8% and 10% attack sizes is presented for Netflix dataset. Furthermore, we conducted an experiment on data of 26 K videos on the Trending YouTube Video Statistics, to predict the user preferences for a particular genre of videos using Machine Learning Algorithms. Based on the results, it observed that the Boosted Decision tree performs the best with an accuracy of 99 percent.

More Efficient On-the-Fly Verification Methods of Colored Petri Nets

Colored Petri Nets (CP-nets or CPNs) are powerful modeling language for concurrent systems. As for CPNs' model checking, the mainstream method is unfolding that transforms a CPN into an equivalent P/T net. However the equivalent P/T net tends to be too enormous to be handled. As for checking CPN models without unfolding, we present three practical on-the-fly verification methods which are all focused on how to make state space generation more efficient. The first one is a basic one, based on a standard state space generation algorithm, but its efficiency is low. The second one is an overall improvement of the first. The third one sacrifices some applicability for higher efficiency. We implemented the three algorithms and validated great efficiency of latter two algorithms through experimental results.

Usability in the Product Design Process Based on the Intelligent Microprocessor

The sharing and reuse of product design experience knowledge is beneficial to shorten the product development cycle and improve product quality. The intelligent microprocessor has greatly changed the way of interaction between people and computers. This article mainly studies the usability analysis in the product design process based on the intelligent microprocessor. The experiment must first perform initial finite element calculations, and the purpose is to complete the data collection. The collection of evaluation indicators is carried out with the help of perceptual vocabulary classification, and the hierarchical inference method is used to build a tree‐like analysis chart. After the indicator collection is completed, the Likert scale method is used to screen the indicators, and the evaluators will rate the selected indicators according to their importance. The Petri net modeling method is used to model the product design process, which mainly includes task element definition, task module division, task planning, and task allocation. Finally, the usability test of the product is carried out. Experimental data shows that the probability of completing the design task within 30 days is 27.74%. The results show that the intelligent microprocessor shortens the time‐consuming process of product design and at the same time enhances product usability.

Management of operational and calendar planning of experimental and serial machine-building production based on the use of petri nets

The article deals with the management methods of serial and experimental production based on the application of Petri net modelling methods in the operational and calendar planning of the manufacturing of machine-building products. The proposed management model, in contrast to existing analogues, allows making a set of management rules, analysing the relationships formed in the process of pilot production and its structure, as well as organizing optimal production according to the criterion to minimize the costs.