Search Results (31)

Modern physical protection systems (PPSs) play a pivotal role in safeguarding critical infrastructure and maintaining public safety. Yet increasingly complex system architectures and evolving threat landscapes pose significant vulnerability challenges to PPSs. Conventional vulnerability assessment methods predominantly rely on expert knowledge or single-path analysis, which inadequately captures complex inter-component relationships and the impact of uncertainties on PPS vulnerabilities. To bridge this gap, this paper introduces a hybrid analytical framework synergizing complex network theory with fuzzy Petri net (FPN). The proposed method operates through two integrated phases: (1) constructing topological models of PPS using complex network theory to characterize component interrelationships, and (2) incorporating FPN to establish vulnerability propagation models that simulate cascading effects and quantify overall system vulnerability. Compared with conventional methods, the proposed approach demonstrates superior effectiveness in identifying critical vulnerability points within the system, providing a scientifically grounded foundation for enhancing PPS security and implementing risk control measures. Full article

In this study, we conceptualize the demands imposed on emergency supply chains during extraordinary emergency events as “stress” and develop a scenario-based stress evolution (SE) analytical approach in emergency mobilization decision-making. First, we characterize emergency supply chain stress by uncertainty, abruptness, urgency, massiveness of scale, and latency. Leveraging lifecycle theory and aligning it with the event’s natural lifecycle progression, we construct a dual-cycle model—the emergency event-stress dual-cycle curve model—to intuitively conceptualize the SE process. Second, taking China’s emergency medical supply chain as an illustrative example, we employ set theory to achieve a structured representation of emergency supply chain stress evolution (ESCSE). Third, we propose a novel ESCSE modeling methodology based on stochastic Petri nets and establish both an ESCSE model and a corresponding isomorphic Markov chain model. To address parameter uncertainties inherent in the modeling process, the fuzzy theory is integrated for parameter optimization, enabling realistic simulation of emergency supply chain stress evolution dynamics. Finally, the SE of the ibuprofen supply chain in Beijing during the COVID-19 pandemic is presented as a case study to demonstrate the working principle of the model. The results indicate that the ESCSE model effectively simulates the SE process, identifies critical states, and triggers actions. It also reveals the evolution trends of key scenario elements, thereby assisting decision-makers in deploying more targeted mobilization strategies in dynamic and changing environments. Full article

Emergency scenario characterization and analysis is an essential approach to describing and understanding the future development of emergencies and assisting in response decision-making. This paper aims to develop a method for emergency evolution analysis in a scenario-based way to improve “scenario response” decision-making. A systematic conceptual framework for emergency scenario evolution (ESE) analysis has been developed based on the domain knowledge of emergency management and the disaster system, combined with the representational ability of the knowledge element model. In addition, a modelling approach for ESE based on the generalized stochastic Petri net (ESEGSPN) is proposed to depict the evolutionary uncertainty through basic control flow and to optimize the parameter uncertainty using fuzzy theory. Finally, the COVID-19 pandemic is used as a case study to show how ESEGSPN works. The results indicate that ESEGSPN can simulate the emergency evolution process, identify critical states and trigger actions, present the evolution trend of typical scenario elements, and assist decision-makers in deploying more targeted emergency responses in dynamically changing situations. Full article

Since the 1990s, Petri nets have been used in systems biology for quantitative modeling. Despite the increasing number of models developed during this period, doubts remain about their biological relevance. Although biological systems predominantly exhibit intracellular or cellular structures, the models rely largely on deterministic predictions, failing to capture the inherent randomness and uncertainties of such systems. The question arises whether these models accurately describe the dynamic behavior of biological systems. This paper introduces a methodology for selecting the appropriate modeling paradigms in systems biology. Initially, we construct a Petri net model and perform deterministic, stochastic, and fuzzy stochastic simulations. Then we perform various statistical tests to measure the discrepancies between the simulation results. Based on scale-density analysis, we determine the modeling approach that best approximates the biological system. Finally, we compare the results of the statistical tests and the scale-density analysis to identify the optimal modeling approach. We applied the proposed methodology to the synthesis of spinal motor neuron protein from the spinal motor neuron-2 gene. Analysis revealed significant discrepancies between the simulation results of different modeling paradigms. Due to the sparse nature of the underlying drug-disease network, we conclude that the fuzzy stochastic paradigm provides the most biologically relevant results. We predict drug combinations that could lead to an up to 149-fold increase in spinal motor neuron protein levels, indicating a promising treatment for the disease. This methodology has the potential for application to other gene-drug-disease networks and broader biological systems. Full article

DC converter stations have a high voltage level, a long transmission distance, and complex internal equipment, and contain power electronic devices, which seriously endanger the stable operation of the system itself and the active distribution network at the receiving end when faults occur. Accurate fault analysis and diagnosis are critical to the safe and stable operation of power systems. Traditional fault diagnosis methods often rely on a single source of information, leading to issues such as insufficient information utilization and incomplete diagnostic scope when applied to DC transmission systems. To address these problems, a fault diagnosis method for converter stations based on preliminary identification of the fault range and the fusion of evidence information of the switch signal and electrical quantity is proposed. First, the preprocessing of converter station sequential event recording (SER) events and a statistical analysis of event characteristics are completed to initially determine the range of the fault.Then, a fuzzy Petri net model and a BP neural network model are constructed on the basis of the fault data from a real-time digital simulation system (RTDS), and the corresponding evidence information of the switch signal and electrical quantity are obtained via iterative inference and deep learning methods. Finally, on the basis of D-S evidence theory, a comprehensive diagnosis result is obtained by fusing the switch and electric evidence information. Taking the fault data of a DC converter station as an example, the proposed method is analyzed and compared with the traditional method, which is based on single information. The results show that the proposed method can reliably and accurately identify fault points in the protected area of the converter station. Full article

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node’s safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases. Full article

Due to the Internet’s explosive growth, network security is now a major concern; as a result, tracking network traffic is essential for a variety of uses, including improving system efficiency, fixing bugs in the network, and keeping sensitive data secure. Firewalls are a crucial component of enterprise-wide security architectures because they protect individual networks from intrusion. The efficiency of a firewall can be negatively impacted by issues with its design, configuration, monitoring, and administration. Recent firewall security methods do not have the rigor to manage the vagueness that comes with filtering packets from the exterior. Knowledge representation and reasoning are two areas where fuzzy Petri nets (FPNs) receive extensive usage as a modeling tool. Despite their widespread success, FPNs’ limitations in the security engineering field stem from the fact that it is difficult to represent different kinds of uncertainty. This article details the construction of a novel packet-filtering firewall model that addresses the limitations of current FPN-based filtering methods. The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area of firewall packet filtering that are characterized by imprecise knowledge. Because of SNPNs’ symbolic ability, the packet filtration model can be quickly and easily established, examined, enhanced, and maintained. Based on the idea that the ambiguity of a packet’s movement can be described by if–then fuzzy production rules realized by the truth-membership function, the indeterminacy-membership function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. In addition, we simulate the dynamic behavior of the tracking system in light of the ambiguity inherent in packet filtering by presenting a two-level filtering method to improve the ranking of the filtering rules list. Results from experiments on a local area network back up the efficacy of the proposed method and illustrate how it can increase the firewall’s susceptibility to threats posed by network traffic. Full article

This study addresses the pressing issue of energy consumption and efficiency in the Kingdom of Saudi Arabia (KSA), a region experiencing growing demand for energy resources. Temperature control plays a vital role in achieving energy efficiency; however, traditional control systems may struggle to adapt to the non-linear and time-varying characteristics of the problem. To tackle this challenge, a fuzzy petri net (FPN) controller is proposed as a more suitable solution that combines fuzzy logic (FL) and petri nets (PN) to model and simulate complex systems. The main objective of this research is to develop an intelligent energy-saving framework that integrates advanced methodologies and air conditioning (AC) systems to optimize energy utilization and create a comfortable indoor environment. The proposed system incorporates user identification to authorize individuals who can set the temperature, and FL combined with PN is utilized to monitor and transmit their preferred temperature settings to a PID controller for adjustment. The experimental findings demonstrate the effectiveness of integrating the FPN controller with a convertible frequency AC compressor in significantly reducing energy consumption by 94% compared to using the PN controller alone. The utilization of the PN controller alone resulted in a 25% reduction in energy consumption. Conversely, employing a fixed-frequency compressor led to a 40% increase in energy consumption. These results emphasize the advantages of integrating FL into the PN model, as it effectively reduces energy consumption by half, highlighting the potential of the proposed approach for enhancing energy efficiency in AC systems. Full article

Fault diagnosis plays a crucial role in enhancing system dependability and minimizing potential catastrophic consequences for both equipment and human safety. This article presents a research study focused on developing a diagnosis and control approach for discrete event systems using the Petri net Fuzzy Interval (IFPN). The Petri net is utilized as a modeling tool for the target system. The paper describes a case study conducted on an ingredient mixing system, where the objective is to maintain the concentration of ingredients within a valid range. A diagnostic framework is constructed and successfully applied to identify faults in the system. The proposed approach is further validated through simulation tests conducted on a mixing system. Full article

An interval type-2 fuzzy set and fuzzy Petri net combined risk assessment model for chemical production was proposed to solve the problems of disorganized hierarchy and poorly targeted measures, as well as the requirement for complex equipment associated with chemical production risk assessment. First, four different types of risk databases were established according to the production process of cyclohexane. Considering the intrinsic relationship between the risk factors in the fault database, the interval type-2 fuzzy set was used to improve the semantic transformation accuracy and calculate the confidence in the risk factors. The fuzzy Petri net model was used to simulate the dynamic development of accidents, and the parallel relationship between risk factors was intuitively described. Thereafter, the external relationship between risk factors was analyzed, and the net structure of each layer was divided to build a multilevel model. Finally, the catalyst activation process during cyclohexane production was taken as an example for risk assessment calculation, and the accident risk probability was calculated by multilevel fuzzy reasoning. The results demonstrate that the model is an improvement over traditional methods and can be used for precise prevention and control. Moreover, it can accurately analyze risk probability during chemical production, determine the risk associated with the reaction process, effectively prevent accidents, and provide a reference for risk evaluation and risk classification. Full article

The intelligent adjustment method of the shearer drum is the key technology to improve the intelligent level and safety degree of fully mechanized mining face. This paper proposes a shearer drum intelligent height adjustment model based on rough set significance attribute reduction (AR) and fuzzy rough radial basis function neural network (FRRBFNN) optimized by adaptive immune genetic algorithm (AIGA). The model first selects the parameters of shearer process monitoring based on the importance attribute reduction algorithm of rough set, and establishes the attribute reduction set of shearer operation characteristic parameters and the drum height decision rule base. Next, a fuzzy rough radial basis function neural network determined by the decision rule space is proposed. By introducing the fuzzy rough membership function as the connection weight, the network can accurately describe the complex nonlinear relationship between the working characteristic parameters of the attribute shearer and the drum height, and measure the uncertainty of the coal seam distribution. Finally, to further optimize the performance of FRRBFNN, the adaptive immune genetic algorithm is introduced to optimize its parameters, to build a high-precision shearer drum height prediction system. For the evaluation method of the model, we use three indicators: mean absolute error, mean absolute percentage error, and root mean square error. Based on the measured data in Yujialiang area, Shaanxi Province, the experimental results show that—compared with the FRRBFNN and support vector regression (SVR) models, a gated current neural network (GRU), a radial basis function neural network (RBF), the memory strengthen long short-term memory (MSLSTM) model, and the adaptive fuzzy reasoning Petri net (AFRPN)—the MAE of the AR-AIGA-FRBFNN model for predicting the height of the left and right rollers are 18.3 mm and 17.2 mm, respectively; the MAPE is 0.96% and 0.93%, respectively; and the RMSE is 21.2 mm and 22.4 mm, respectively. The AR-AIGA-FRBFNN model is therefore more effective than the other considered methods. Full article

Petri net is a widely used fault-diagnosis algorithm. However, it presents poor fault-diagnosis effectiveness and accuracy caused by the parameter setting and adjustment, depending entirely on expert experience in a system with a single input signal type. To address this problem, a comprehensive learning particle swarm optimized fuzzy Petri net (CLPSO-FPN) algorithm is proposed for motor-bearing fault diagnosis. CLPSO is employed to obtain an adaptive system parameter set to reduce the fault-diagnosis error caused by human subjective factors. Moreover, a new proposed concept of the transition influence factor replaces the traditional transition confidence to improve the nonlinear expression ability of traditional Petri nets, which suppresses the space explosion problem of the fault-diagnosis model. Finally, experiments are implemented on a dataset of motor bearings. Compared with traditional faults diagnosis methods, the proposed method realized better performance in the fault location and prediction functions of motor bearings, which is beneficial for troubleshooting and motor maintenance. Full article

The grid-connected inverter is a vital power electronic equipment connecting distributed generation (DG) systems to the utility grid. The quality of the grid-connected current is directly related to the safe and stable operation of the grid-connected system. This study successfully constructed a robust control system for a grid-connected inverter through a dynamic recurrent fuzzy-neural-network imitating sliding-mode control (DRFNNISMC) framework. Firstly, the dynamic model considering system uncertainties of the grid-connected inverter is described for the global integral sliding-mode control (GISMC) design. In order to overcome the chattering phenomena and the dependence of the dynamic information in the GISMC, a model-free dynamic recurrent fuzzy-neural-network (DRFNN) is proposed as a major controller to approximate the GISMC law without the extra compensator. In the DRFNN, a Petri net with varied threshold is incorporated to fire the rules, and only the parameters of the fired rules are adapted to alleviate the computational workload. Moreover, the network is designed with internal recurrent loops to improve the dynamic mapping capability considering the uncertainties in the control system. In addition, to assure the parameter convergence in the adaptation and the stability of the designed control system, the adaptation laws for the parameters of the DRFNN are deduced by the projection theorem and Lyapunov stability theory. Finally, the experimental comparisons with the GISMC scheme are performed in an inverter prototype to verify the superior performance of the proposed DRFNNISMC framework for the grid-connected current control. Full article

An acknowledgment of feedback is extremely helpful in medical training, as it may improve student skill development and provide accurate, unbiased feedback. Data are generated by hundreds of complicated and variable processes within healthcare including treatments, lab results, and internal logistics. Additionally, it is crucial to analyze medical training data to improve operational processes and eliminate bottlenecks. Therefore, the use of process mining (PM) along with conformance checking allows healthcare trainees to gain knowledge about instructor training. Researchers find it challenging to analyze the conformance between observations from event logs and predictions from models with artifacts from the training process. To address this conformance check, we modeled student activities and performance patterns in the training of Central Venous Catheter (CVC) installation. This work aims to provide medical trainees with activities with easy and interpretable outcomes. The two independent techniques for mining process models were fuzzy (i.e., for visualizing major activities) and inductive (i.e., for conformance checking at low threshold noise levels). A set of 20 discrete activity traces was used to validate conformance checks. Results show that 97.8% of the fitness of the model and the movement of the model occurred among the nine activities. Full article

This paper discusses the Reliability, Availability, Maintainability, and Safety (RAMS) of an electrical power supply system in a large European hospital. The primary approach is based on fuzzy logic and Petri nets, using the CPNTools software to simulate and determine the most important modules of the system according to the Automatic Transfer Switch. Fuzzy Inference System is used to analyze and assess the reliability value. The stochastic versus fuzzy approach is also used to evaluate the reliability contribution of each system module. This case study aims to identify and analyze possible system failures and propose new solutions to improve the system reliability of the power supply system. The dynamic modeling is based on block diagrams and Petri nets and is evaluated via Markov chains, including a stochastic approach linked to the previous analysis. This holistic approach adds value to this type of research question. A new electrical power supply system design is proposed to increase the system’s reliability based on the results achieved. Full article