Fuzzy Petri Nets Research Articles

An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

Aim: The present study is aimed to assess the segmentation success of an artificial intelligence (AI) system based on the deep convolutional neural network (D-CNN) method for the segmentation of masseter muscles on ultrasonography (USG) images. Materials and Methods: This retrospective study was carried out by using the radiology archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry in Ankara University. A total of 195 anonymized USG images were used in this retrospective study. The deep learning process was performed using U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures. Muscle thickness was assessed using USG by manual segmentation and measurements using USG’s software. The neural network model (CranioCatch, Eskisehir-Turkey) was then used to determine the muscles, following automatic measurements of the muscles. Accuracy, ROC area under the curve (AUC), and Precision-Recall Curves (PRC) AUC were calculated in the test dataset and compare a human observer and the AI model. Manual segmentation and measurements were compared statistically with AI ( P < .05). The Mann–Whitney U test was used to analyze whether there is a statistically significant difference between the predicted values and the actual values. Results: The AI models detected and segmented all test muscle data for FPN and U-net, while only two cases of muscles were not detected by PSPNet (false negatives). Accuracies of FPN, PSPNet, and U-net were estimated as 0.985, 0.947, and 0.969, respectively. Receiver operating characteristic scores of FPN, PSPNet, and U-net were estimated as 0.977, 0.934, and 0.969, respectively. The D-CNN measurements of the muscles were similar to manual measurements. There was no significant difference between the two measurement methods in three groups ( P > .05). Conclusion: The proposed AI system approach for the analysis of USG images seems to be promising for automatic masseter muscle segmentation and measurement of thickness. This method can help surgeons, radiologists, and other professionals such as physical therapists in evaluating the time correctly and saving time for diagnosis.

USE OF FUZZY PETRI NETWORKS FOR INFORMATION DESCRIPTION OF THE HOUSING ENVIRONMENT

The current state of comfort of an apartment building indicates many problems that exist in this area. First, there is a need for clear planning of comfortable housing, which should take into account the current socio – economic needs of the population. Secondly, there is no clear definition of comfort levels of an apartment building. Also, there is a need to identify the main factors that affect the life and comfort of the population. The article is based on two main areas of research. The first is to present a description of the information model of a hybrid intelligent system (GIS) for the comfort of the living environment of an apartment building. This tool allows you to model the effects of interaction, adaptation, which can be observed in the system when making decisions. The study also simulates complex systems based on the use of fuzzy Petri nets (FPs) to describe comfort. MP and its various types are one of the classes of models that provide an opportunity to present the structure of integrated organizational – information systems and complexes, as well as logical – temporal processes of system operation. Fuzzy Petri nets are divided into two parts. The first part describes the structure of the network, it is usually standard, the processes of initial marking and movement of markers on the network are determined by means and methods of fuzzy sets and fuzzy logic. The biggest area of their application is complex objects. Fuzzy logic is used to formalize fuzzy concepts in terms of semantics and provides efficient information processing along with clear data. With the help of this network the endurance of modeling systems and the optimality of its structure are investigated. To date, a large number of different types of Petri nets are known, which provide an opportunity to present the structure of the models in the absence of analytical representation of the influence of certain factors.

An Intelligent Algorithm for Decision Making System and Control of the GEMMA Guide Paradigm Using the Fuzzy Petri Nets Approach

The aim of this article is to present the fuzzy Petri net algorithm and its implementation on the Guide d’etude des modes de marche et d’arrêt (GEMMA) guide paradigm. Additionally, this article presents a brief description of the GEMMA guide paradigm, the fuzzy logic approach, the Petri nets theory, and fuzzy Petri nets. Each algorithm is intended for a specific set of transitions, depending on their essential conditions. All these algorithms generate intelligent and safe control of the GEMMA guide paradigm in order to have the best control system for the machine. Moreover, the algorithms are able to make the best decision automatically depending on the machine’s situation and condition, something that allows the machine to be placed in the appropriate mode for each situation. The results show that the different fuzzy Petri net algorithms were working properly for the GEMMA guide paradigm and provided a proper solution for the automation of production systems.

Knowledge representation and acquisition using R‐numbers Petri nets considering conflict opinions

AbstractAs a vital modelling technique, fuzzy Petri nets (FPNs) have been widely used in various areas for knowledge representation and reasoning. However, the conventional FPNs have many deficiencies in representing inaccurate knowledge, acquiring knowledge parameters and conducting approximate reasoning when used in the real world. In this article, a new version of FPNs, called R‐numbers Petri nets (RPNs), is proposed to overcome the shortcomings and enhance the effectiveness of FPNs. Based on R‐numbers, expert knowledge is depicted in the form of weighted R‐numbers production rules. The interrelationships among input places (or transitions) are modelled by the R‐numbers Maclaurin symmetric mean operator in the knowledge reasoning process. In addition, the conflict opinions of experts are handled with the proposed RPN model in order to obtain more precise knowledge parameters. Finally, the effectiveness and practicality of the proposed RPNs are illustrated by a realistic example concerning reliability analysis of an electric vehicle motor.

A Pythagorean fuzzy Petri net based security assessment model for civil aviation airport security inspection information system

This paper investigates the problem of the existing information system security situation assessment models that are less concerned about fuzzy factors and lack of reasoning. First, Pythagorean fuzzy sets are adopted to deal with the ambiguity and uncertainty of the civil aviation airport security inspection information system security situation indicators, and a Pythagorean fuzzy Petri nets (PFPNs) model is established according to the indicator system. Then, based on the characteristics of PFPN, the propositions credibility reasoning algorithm and the security situation fuzzy reasoning algorithm are designed. Finally, the feasibility of the model is verified through the assessment experiment on the security inspection information system of a civil aviation airport. The experimental results show that the PFPN model has better stability and lower algorithm time complexity compared with other models.

Contributions of Petri Nets to the Reliability and Availability of an Electrical Power System in a Big European Hospital - A Case Study

The energy power supply infrastructure of a hospital, to function correctly, needs to be well maintained to ensure its reliability and, by consequence, the maximum integrated availability. In this paper, the authors propose the use of Petri Nets to help the improvement of the electric power system reliability, having as a case study a big European Hospital. The purpose of the research is to identify and analyse the potential failures of the system and to suggest solutions to improve the operations and maintenance to maximise the availability and reliability of those assets through possible and objective answers. It was necessary to develop a diagnosis and planning methodology to assess the reliability of several components of the energy power supply system. It is dynamic modelling based on a block diagram of the system and transposed to representation by Petri Nets. The analysis and the simulation of the discrete events of the system, as well as the visualisation of the process functioning and the communications inside, was made. Additionally, they were referred to other approaches, like the Fuzzy Petri Nets and Stochastic Petri Nets, as well as a future balance about its application in a situation like the analysed in this paper

Predicting heart failure using data mining with Rough set theory and Fuzzy Petri Net

The Rough Set Theory (RST) is a method that has proven its efficiency and simplicity in machine learning and successfully developing now a day’s vastly and rapidly. Fuzzy Petri nets (FPNs) are a potential modelling technique, which is used for knowledge representation and reasoning of rule-based expert systems. Though, RST has the efficiency in dimension reduction, it has to be proved with evidence by creating model using FPN with rule based reasoning. In this paper the induction of decision rules by RST executed with Fuzzy Petri Nets (FPN) is analyzed in the sense how it performed better than other data mining classifiers. The rule-based classifiers like jrip, part R, zero R are used for the comparison purposes. The knowledge captured from the rules through the best reduct has performed with the efficiency of RST and formulating rules by FPN. This paper experiments the Heart failure data to investigate the decision making from the rules generated by LERS system of RST with the approach of FPN. The heart failures during the follow up period of the patents are predicted with the pattern recognized form the data using the above process and the best evaluators are found during the experiments were pictured.

Sensor Fault Diagnosis Based on Adaptive Arc Fuzzy DBN-Petri Net

The adjustable parameters of the traditional fuzzy Petri net (FPN) are single and mostly depend on expert experience. This approach lacks the adaptability to the complex network of sensors, which will result in insufficient accuracy of fault diagnosis. We propose a method combining the FPN with an adaptive arc and deep belief network (DBN) and improved a fast Gibbs sampling (FGS) algorithm to realize sensor fault diagnosis. First, we present the concept of adaptive arcs with label-weights based on the confidence-weights of directed arcs, which is an important component of the sensor fault model. Then, the improved FGS algorithm optimizes the model layer-by-layer, and the adjustment of the transition threshold relies on the marginal distribution of a restricted Boltzmann machine (RBM). Finally, the optimized dual-weights and dual-transition influence factors are applied to the forward and backward fuzzy reasoning of the model to achieve network adaptability. Our studies showed that this method has obvious advantages in terms of the accuracy and adaptability of complex networks compared to other FPN fault diagnosis methods. The fault reasoning confidence can provide an effective reference for maintenance personnel and improve maintenance efficiency, ensuring the reliable operation of sensors and related systems.

Process Modelling and Creating Predictive Models of Sensory Networks Using Fuzzy Petri Nets

The current period is evidence of the massive replacement of human power with the results of the mental cognitive work of researchers. This mainly concerns the area of industrial production, where the aim is to apply new technologies on the principle of artificial intelligence, using the possibilities of a new direction in the use of a technology called the Internet of Things (IoT). With this idea, however, the question of the user disappears, who in this chaos of massive information can get into a state, where his activities will no longer be manageable. To cope with this situation, it is necessary to examine the impact of these technologies on the quality of life in the work and home environment. This paper investigates process modelling and the creation of predictive models to obtain the emotional state of users. The created predictive models will later allow the simulation of complex sensory systems, which will be able to predict the emotional states of users based on their physiological states. The aim is to also find connections between physiological states and basic emotions to be able to classify user emotions with the highest possible accuracy in the classification process.

Network Attack Path Selection and Evaluation Based on Q-Learning

As the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain propagation analysis of a power CPS risk based on reinforcement learning. First, the Fuzzy Petri Net (FPN) was used to establish an attack model, and Q-Learning was improved through FPN. The attack gain was defined from the attacker’s point of view to obtain the best attack path. On this basis, a quantitative indicator of information-physical cross-domain spreading risk was put forward to analyze the impact of cyber attacks on the real-time operation of the power grid. Finally, the simulation based on Institute of Electrical and Electronics Engineers (IEEE) 14 power distribution system verifies the effectiveness of the proposed risk assessment method.

WEATHER PREDICTION USING FUZZY PETRI NETS

In fields such as economy, agriculture, weather etc., forecasting plays an important role as it predicts a way to vague situations and events. Weather prediction plays a crucial role as it provides about Weather condition which is a main aspect in the field of agriculture. In this work, Fuzzy knowledge rule base technique is used to predict the objects contained in atmospheric temperature and wind speed. Fuzzy Petri nets are used for classification of data with linguistic variables.

Knowledge representation and reasoning with industrial application using interval-valued intuitionistic fuzzy Petri nets and extended TOPSIS

Fuzzy Petri nets (FPNs) have many successes in various applications as an important modeling technique for knowledge representation and reasoning. However, in the real world, the following conditions may make it difficult to precisely model knowledge based on current FPNs, including the cognitive nonconformity, fuzziness and uncertainty of experiential cognition of experts. In an effort to overcome the shortcomings of current FPNs, the interval-valued intuitionistic FPNs (IVIFPNs) are proposed based on interval-valued intuitionistic fuzzy sets (IVIFSs), IVIFSs hybrid averaging (IVIFSsHA) operator and extended TOPSIS (ETOPSIS). Combining with IVIFSsHA operator, an inference algorithm based on matrix operation is proposed to improve the efficiency of computing final truth values. In addition, an optimal alternative is determined based on the proposed ETOPSIS, in which intuitionistic information and fuzzy information can be considered simultaneously based on the proposed information collaborative entropy. Finally, a comparison test is presented to show the effectiveness of ETOPSIS. Moreover, a novel model for the identification of aluminum electrolysis cell condition is proposed based on IVIFPNs and ETOPSIS, and the application result shows that the proposed methods are efficient to deal with cognitive nonconformity and manage fuzziness and uncertainty of expert knowledge. These facts demonstrate the usefulness and advantages of the proposed methods in complex real-world applications.

Fault diagnosis method of distribution network based on time sequence hierarchical fuzzy petri nets

A large number of alarm information will be generated after the distribution network fails, these information can be used to identify fault components quickly. Since the existing fault diagnosis methods of Petri nets do not fully utilize the time sequence attribute of alarm information, and the time sequence reasoning and fuzzy reasoning are complicated, besides, there are few methods applied to distribution network. A novel fault diagnosis method based on time sequence hierarchical fuzzy Petri nets (TSHFPNs) is proposed for distribution network in this paper. A time interval is assigned for each proposition and rule of the TSHFPNs model to describe the timing constraint of alarm information. The matrix reasoning algorithm can be applied to different system structures, the model has good adaptability. The fuzzy reasoning and time sequence reasoning are implemented at the same time, therefore, the time point constraint and the confidence probability of fault component can be obtained at the same time. At the end of the reasoning process, Gaussian function is introduced to optimize the fault probability and improve the accuracy of fault diagnosis results. Through the comparison and analysis of the distribution system examples, the correctness and rationality of the proposed method are verified.

MODELING OF INTELLECTUAL CONTROL SYSTEMS WITH APPLICATION OF MODIFIED FUZZY COLORED PETRI NETS

A modification of the Petri nets is proposed  a Fuzzy Colored Petri Net (FCPN), leading to the integration of Fuzzy Petri Nets (FPN) and Colored Petri Nets (CPN). Separately, the shortcomings of FPN and CPN and the advantages the developed FCPN for modeling intelligent control systems are identified and justified. In the developed modified FCPN the membership functions of the terms of a linguistic variable are applied to markers of the CPN as color and fuzzy existence conditions are assigned to arcs depending on the values of the linguistic variable. As a result, FCPN with enhanced capabilities was obtained, which eliminates the shortcomings of CPN and FPN. The FCPN and a simulation approach are defined, which includes a reasoning algorithm, as well as a detailed procedure for modeling and analysis of nonlinear discrete objects. The structure is organized in the CPN Tools system with the synchronization of the CPN ML (Markup Language) language with the MatLab package. The choice of membership function and fuzzification of term values is performed in the Fuzzy Toolbox application of the MatLab system. The proposed approach is illustrated by simple example, including the control of water pumps, to maintain the required water level in the pumping well. A model is developed for the automation of adaptive fuzzy control of water pumps based on modified FCPN. Based on the criteria for the operation of water pumps according to the water levels in the pumping well, many positions and transitions of the FCPN are formed. Describing the necessary behavior of the system by the relations between the positions and transitions of the FCPN using the logic “If ... Then ...” an adaptation algorithm is developed. Based on the algorithm, the matrices of input and output incidents are determined. The graph model of the FCPN is developed. Visualization of the model is implemented in the CPN Tools system. The computer simulations and net analysis experiments demonstrate the convenience of the developed approach when modeling the intelligent control of dynamic systems.

Novel JavaScript malware detection based on fuzzy Petri nets

Novel JavaScript malware detection based on fuzzy Petri nets

Modeling and application of risk assessment considering veto factors using fuzzy Petri nets

Risk assessment is important for plant safety, and fuzzy set theory is useful for such assessment because many risk factors have fuzzy characteristics. In this study, veto factors for risk assessment are taken into account. Weighted fuzzy Petri nets (WFPN) with inhibitor arcs are proposed to model relationships between risk factors and establish the risk assessment structure considering veto factors. Definitions of WFPNs as well as the enabling rule and execution rule are provided. The modeling approach for the assessment combining normal factors with veto items is discussed. The proposed fuzzy risk assessment approach is illustrated by an example of the assessment of production installations and process technology of plants that deal with hazardous chemicals. Veto factors and non-veto factors are presented and the assessment structure based on WFPNs is established. Using the factor data of a plant, an assessment value is obtained through the operation of WFPNs and the verification of the approach is discussed.

A New Linguistic Petri Net for Complex Knowledge Representation and Reasoning

Fuzzy Petri nets (FPNs) are a useful instrument for modelling expert systems to conduct knowledge representation and reasoning. Many studies have been carried out for improving the performance of FPNs in terms of their accurate representation of knowledge and power of approximate reasoning. Nevertheless, the current representation methods with FPNs are unable to handle the uncertain linguistic knowledge given by domain experts and the reliability of their judgments. In addition, the existing reasoning algorithms have no way to capture the interrelationship of the propositions with the same output transition. Therefore, we present a new type of FPNs, called 2-dimensional uncertain linguistic Petri nets (2DULPNs). The 2-dimensional uncertain linguistic variables (2DULVs) and Choquet integral are combined for knowledge representation and reasoning for the first time. The truth degrees of propositions, thresholds and certainty values of linguistic production rules are denoted as 2DULVs. Some new aggregated operators based on Choquet integral are proposed and used in the approximate reasoning to capture the interactions among antecedent propositions. Finally, an equipment fault diagnosis example is provided to illustrate the correctness and effectiveness of the proposed 2DULPN model.

Divide Large Scale Fuzzy Petri Net into Equivalent Sub-FPNs using Reachability Tree

As real systems become larger and complicated, the number of nodes of fuzzy Petri net (FPN) also increases rapidly, it further indicated that the algorithm complexity of related application using FPN grows up sharply. To solve this state-explosion issue, a decomposition algorithm using reachability tree is proposed to divide a large-scale FPN model into a series of sub-FPNs in this paper. Comparison experiments show that all obtained sub-FPNs do not destroy internal reasoning paths of the original large-scale FPN and represent a complete reasoning path from input place(s) to the output place.

Experiential knowledge representation and reasoning based on linguistic Petri nets with application to aluminum electrolysis cell condition identification

Fuzzy Petri nets (FPNs) play an important role in knowledge representation and reasoning (KRR), and they have been widely used in many fields. Linguistic terms are usually used to express the experiential knowledge of decision-makers in the above fields. However, cognitive nonconformity, fuzziness and uncertainty of experiential knowledge are widespread in industrial production processes, making it difficult for current FPNs to precisely model the experience or cognition of experts. In an effort to overcome the shortcomings of FPNs, linguistic Petri nets (LPNs) are proposed based on interval type-2 fuzzy sets theory and FPNs in this paper. The extended TOPSIS (ETOPSIS) is proposed to fuse together the cognition of multiple decision-makers. An interval type-2 fuzzy ordered weighted averaging operator is proposed to improve the knowledge reasoning capability of LPNs. Two comparisons are presented to demonstrate the validity of the proposed ETOPSIS and LPNs. In addition, the KRR model for aluminum electrolysis cell condition identification (AECCI) is proposed and AECCI results show the proposed methods are efficient to embrace cognitive nonconformity and manage fuzziness and uncertainty of experiential knowledge.

Bipolar fuzzy Petri nets for knowledge representation and acquisition considering non-cooperative behaviors

Fuzzy Petri nets (FPNs) are a promising modeling tool for knowledge representation and reasoning. As a new type of FPNs, bipolar fuzzy Petri nets (BFPNs) are developed in this article to overcome the shortcomings and improve the performance of traditional FPNs. In order to depict expert knowledge more accurately, the BFPN model adopts bipolar fuzzy sets (BFSs), which are characterized by the satisfaction degree to property and the satisfaction degree to its counter property, to represent knowledge parameters. Because of the increasing scale of expert systems, a concurrent hierarchical reasoning algorithm is introduced to simplify the structure of BFPNs and reduce the computation complexity of knowledge reasoning algorithm. In addition, a large group expert weighting method is proposed for knowledge acquisition by taking experts’ non-cooperative behaviors into account. A realistic case of risk index evaluation system is presented to show the effectiveness and practicality of the proposed BFPNs. The result shows that the new BFPN model is feasible and efficient for knowledge representation and acquisition.