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A two dimensional language is a collection of two dimensional words, which are rectangular array of symbols made up of finite alphabets. Fuzzy Petri nets are the generalization of classical Petri nets designed to deal with imprecise and ambiguous data which usually occurs in knowledge based systems, image processing, etc. They have been widely used to represent fuzzy production rules and fuzzy rule-based reasoning. In this paper, a new model called array token fuzzy Petri net to generate two dimensional fuzzy regular languages has been introduced. Array token fuzzy Petri nets are used to deal with impreciseness and uncertainties occurring in two dimensional regular languages. Furthermore, proved that for every two dimensional fuzzy regular grammar there exists an array token fuzzy Petri net that generates the same two dimensional fuzzy regular language and also establish some closure properties of the languages generated by array token fuzzy Petri net. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The modelling of biological systems is accompanied with epistemic uncertainties that range from structural uncertainty to parametric uncertainty due to such limitations as insufficient understanding of the underlying mechanism and incomplete measurement data of a system. Fuzzy logic approaches such as fuzzy Petri nets (FPNs) are effective in addressing these issues. In this paper, we review FPNs that have been used for modelling uncertain biological systems, which we classify in three categories: basic fuzzy Petri nets, fuzzy quantitative Petri nets and Petri nets with fuzzy kinetic parameters. For each category of these FPNs, we summarize its modelling capabilities and current applications, discuss its merits and drawbacks and give suggestions for further research. This understanding on how to use FPNs for modelling uncertain biological systems will assist readers in selecting appropriate FPN classes for specific modelling circumstances. This review may also promote the extensive research and application of FPNs in the systems biology area. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid fault location method for smart distribution systems by using available multi-source data, including data from protective devices, fault indicators (FIs), smart meters, voltage and current meters, and computer simulations. A fault diagnosis model based on fuzzy Petri net (FPN) technique is developed that employs discrete evidences, such as status of protective devices and FIs to estimate the faulted section. To eliminate the possible false fault section estimations and pinpoint the actual location of the fault a mismatch index is defined. It quantifies the similarity between recorded measurements, such as voltage and current signals during faults and the corresponding values obtained by performing short circuit analysis. The fault scenario with minimum mismatch degree is determined as the actual location of the fault. The proposed method can precisely determine the fault location in distribution systems with many laterals and sub-laterals with heterogenous line parameters in the presence of distributed generators (DGs). The accuracy and effectiveness of the proposed method are validated by applying the proposed method to the simulated IEEE 33 node feeder distribution system. [ABSTRACT FROM AUTHOR]

A novel approach of fuzzy Petri net (FPN) is presented to aircraft terrain following (TF) through aircraft flights. The FPN controller system as presented in this paper determine where the aircraft needs to modification its altitude. Depending on the concepts of real-time system applications the decision-maker (i.e. Terrain-Following Radar (TFR)) will be promising notwithstanding for hard terrains in expression of shape and features. The implementation process for terrain following collaborative design is fuzzily dedicated by the algorithm of FPN. The FPN approach employs special connections among existing Slope, Height, and Spacing of the terrain with aircraft flights velocity overhead the ground to construct appropriate FPN rules. Settling on knowledge implies a basic decision making over passing across or over a terrain. Slope angle of terrain (Sl1 and Sl2), spacing is the ratio of the largest and smallest peak-to-peak terrain (Sp1 and Sp2) and height of vehicle (He1 and He2), and the main system is the velocity (Sl, He and Sp) is output all the inputs and outputs are linguistic variables. The case study of aircraft flights shows the effectiveness of the FPN approach as compared to other existing approaches. Based on the FPN approach the results illustrate a perfect chasing while using four models with a number of fuzzy rules equal to 200 rules will causes an optimal result. While a suitable height of an aircraft flights above the ground is substantial this given us good motivation to develop a new approach in maintaining the height of aircraft. Finally, the presented method is applied with low-level flight to be authenticity be applied for such applications. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of fault diagnosis result of gear reducer, this paper researches a fault diagnostic new method based on neural network optimized FPN. The BP algorithm has the characteristics of adaptive learning, and the original value of FPN related network parameters is optimized. Based on the FPN network, the BP algorithm is used to train the gear reducer fault samples, so that the FPN parameter values gradually approach the true value. The results of application examples show that this new algorithm for single or multiple fault diagnosis of gear reducer is very effective, the fault diagnosis accuracy has improved significantly, and the effectivity and correctness of optimization algorithm are verified. [ABSTRACT FROM AUTHOR]

The inability to speak fluently degrades the quality of life of many individuals. Early intervention from childhood can reduce the disfluency of speech among adults. Traditionally, disfluency of speech among children is diagnosed based on the speech intelligibility assessment by speech and language pathologists, which can be expensive and time-consuming. Hence, numerous attempts were made to automate the speech intelligibility detection. Current detectors can discriminate unintelligible speech by calculating the posterior probability scores for each articulatory feature class. However, their major drawback is producing results that are most likely based on training and input data, leading to inconsistencies in discriminating speech sounds. As such, the performance of detectors is still far below humans. To overcome this limitation, a new classification method based on Fuzzy Petri Nets (FPN) is proposed to improve the classification accuracy. FPN was proposed as it has greater knowledge representation ability to reason using uncertain or ambiguous information. In this research, the speech features of Malay impaired children's speeches are analyzed for the identification of the significant speech features in the impaired speech which are related to the intelligibility deficits. The results showed that FPN is more reliable in discriminating speech sounds than the baseline classifiers with improvements in the classification accuracy and precision. [ABSTRACT FROM AUTHOR]

The simulation of knowledge-based systems (KBSs) has become a significant challenge owing to the rapid increase in the scale of accumulated data. The extended formalisms that are widely used to test, model, and analyze such systems include the fuzzy production rule (FPR) and fuzzy Petri net (FPN). However, with the growth in magnitude of KBSs, it has become difficult to manually generate an FPN. Hence, the authors propose an equivalent transformation algorithm that automatically models an FPN for a sizeable KBS. The proposed method produces a final FPR by initially investigating the inner-inference path(s) between FPRs, followed by a four-phase transformation algorithm that automatically generates an equivalent FPN model for the corresponding KBS rooted in the inner-inference path(s) obtained. A KBS with 13 FPRs is used to demonstrate both the validity and feasibly of the proposed transformation algorithm. The results validate the capability of the generated FPN to fully represent the complete information base contained in the corresponding KBS. [ABSTRACT FROM AUTHOR]

Superheat degree identification of aluminum electrolysis cell is a typical knowledge-intensive work which is the precondition of efficient production. However, existing methods have application limitations, and the large cognitive differences among experts are ignored in the existing methods. To address these issues, we propose the consensus-based probabilistic hesitant intuitionistic linguistic Petri nets (CPHILPNs). The truth values of places in CPHILPNs are represented by probabilistic hesitant intuitionistic linguistic term sets (PHILTSs), which can enrich the experts' expression and improve the efficiency of dealing with uncertainties. The proposed probabilistic hesitant intuitionistic linguistic consensus optimization model can reduce the deviation of individual preferences to reach a consensus. Then the obtained experts' weights and decision assessments will be more accurate and comprehensive with objective evaluations. The order weighted averaging PHILTSs concurrent reasoning algorithm is proposed to enhance the inference efficiency. Finally, the usefulness and validity of the proposed CPHILPNs are verified by conducting comparisons and actual experiments in a real-world plant. [ABSTRACT FROM AUTHOR]

Esophageal cancer is one of the most common cancers world-wide and also the most common cause of cancer death. In this paper, we present an adaptive fuzzy reasoning algorithm for rule-based systems using fuzzy Petri nets (FPNs), where the fuzzy production rules are represented by FPN. We developed an adaptive fuzzy Petri net (AFPN) reasoning algorithm as a prognostic system to predict the outcome for esophageal cancer based on the serum concentrations of C-reactive protein and albumin as a set of input variables. The system can perform fuzzy reasoning automatically to evaluate the degree of truth of the proposition representing the risk degree value with a weight value to be optimally tuned based on the observed data. In addition, the implementation process for esophageal cancer prediction is fuzzily deducted by the AFPN algorithm. Performance of the composite model is evaluated through a set of experiments. Simulations and experimental results demonstrate the effectiveness and performance of the proposed algorithms. A comparison of the predictive performance of AFPN models with other methods and the analysis of the curve showed the same results with an intuitive behavior of AFPN models.

Fuzzy spatio‐temporal knowledge is required in a wide range of application fields, including GIS, spatio‐temporal database, and artificial intelligence. Ontology, as a formal method of knowledge representation, plays a key role in the Semantic Web. Therefore, how to extend ontologies to represent fuzzy spatio‐temporal knowledge needs to be solved. In this paper, we propose an approach for representing fuzzy spatio‐temporal knowledge with ontologies and investigate a formal construction of fuzzy spatio‐temporal ontologies based on fuzzy Petri nets. First, we propose a formal definition of fuzzy spatio‐temporal ontologies. In addition, based on fuzzy description logic, a description logic named the fuzzy spatio‐temporal description logic, which provides a logical basis for the fuzzy spatio‐temporal ontologies, is presented. Furthermore, a fuzzy spatio‐temporal web ontology language, which is fuzzy and spatio‐temporal extension of standard ontology language OWL, is proposed to make the fuzzy spatio‐temporal ontologies easy to use and support efficient reasoning. On the basis of fuzzy spatio‐temporal ontologies, we investigate a formal approach for constructing fuzzy spatio‐temporal ontologies from fuzzy spatio‐temporal Petri nets, ie, transforming fuzzy spatio‐temporal Petri nets (including Petri net model and Petri net instance) into fuzzy spatio‐temporal ontologies at both structure and instance levels. Finally, we prove the correctness of the transformation and provide a detailed transformation example. [ABSTRACT FROM AUTHOR]

The use of nasogastric (NG) tubes in acute, critical, and long-term care may lead to mechanical, infectious, and metabolic complications. NG intubation is a risk factor for aspiration and complications of organ injury. Mechanical complications include deliberate selfextubation and accidental extubation, both of which comprise unplanned extubation and occur in >35% of cases in rehabilitation rooms. Therefore, we intend to propose a digital warning tool to detect NG tube dislodgment over several days or weeks for a continuous insertion of the NG tube. On the basis of fog computing, integrating dexter-to-sinister light-controlled sensors and fuzzy Petri net (FPN) was performed to achieve the proposed assistant tool. The proposed intelligent algorithm can also be easily implemented using a high-level programming language (Language C/C++) in an embedded system. The experimental results demonstrated the feasibility of the algorithm under normal conditions and partial and NG two-tube dislodgments. [ABSTRACT FROM AUTHOR]

Drilling risers are the critical connection of subsea wellhead and the floating drilling unit. With exploration and development of oil and gas resources moving into deepwater, drilling riser operations have been characterized with high safety risk and occurrence rate of accidents causing high drilling downtime and drilling cost. In the present study, a Fuzzy Petri Net (FPN) methodology is proposed to evaluate the comprehensive risk of deepwater drilling risers. A risk index evaluation system was established based on analyses of drilling riser accidents and identification of risk factors, and the AHP-EM method was used to determine the weights of them. A 9-tuple set was defined to model drilling riser risks according to the FPN theories, and by using the fuzzy reasoning algorithm, risk values of risk factors at different levels and the integrated system were gained by iteration of state matrix. A specific case study of deepwater drilling risers of NANHAI-8 drilling unit in Liuhua oilfield in South China Sea is presented to illustrate the application of proposed approach, and some suggestions drawn from the investigation are presented to further mitigate the risk of drilling riser operations. The case study showed that FPN is a practical and reliable method in comprehensive risk evaluation of deepwater drilling riser system. [ABSTRACT FROM AUTHOR]

Time fuzzy Petri nets (TFPNs) have been widely used to describe the transfer correlations among industrial process variables. However, the assignments of parameters associated with traditional TFPNs mostly rely on human expert knowledge. Additionally, traditional TFPNs have limited abilities to deal with dynamic time delays between correlated variables. In response to these problems, a dynamic timed fuzzy Petri nets (DTFPNs) modeling approach based on the dynamic time delay analysis (e-DTA) is proposed. Firstly, the basic structure of Petri nets is determined by taking advantage of process knowledge. Subsequently, as an improvement, a colored graph describing dynamic time delays between correlated variables is created using data mining techniques. A reachability analysis with temporal constraints is accordingly performed to track the system evolution dynamically. The proposed method is applied to a numerical case and a distillation column simulation, verifying the effectiveness of the contribution. [ABSTRACT FROM AUTHOR]

Fuzzy approaches play an important role in the modeling of genetic regulatory networks (GRNs) with incomplete quantitative data. However, current fuzzy approaches such as fuzzy logic and fuzzy Petri nets (FPNs) can neither clearly describe causal relationships between genes as each interaction between genes is represented by a couple of fuzzy rules, nor easily deal with large GRNs. To address these issues, this paper presents a new class of colored fuzzy Petri nets (CFPNs) by combining colored Petri nets with FPNs, which makes it possible to clearly represent interactions among genes or to construct a compact model for a large GRN requiring many fuzzy rules. We give the definition of CFPNs and a simulation approach which incorporates a reasoning algorithm, as well as a detailed procedure for modeling and analyzing GRNs with CFPNs. We illustrate our approach using a simple example comprising six genes. [ABSTRACT FROM AUTHOR]

Fuzzy Petri nets (FPNs) are a vital modeling technique for the construction of knowledge-based systems, which have been commonly used in many fields, such as fault diagnosis, risk assessment, workflow management, and disassembly process planning. However, the conventional FPNs have been blamed for the following reasons: 1) the representation parameters in FPNs cannot precisely model experts' experience since it is difficult to manage the fuzziness and randomness of knowledge assessments simultaneously, and 2) the weight coefficients in the existing approximate reasoning algorithms are hardly enough to reflect the associated weights of reordered places. In response, we propose a new type of FPNs, called cloud reasoning Petri nets (CRPNs) based on the concept of interval clouds and the hybrid averaging operator. The cloud production rules in a knowledge-based system are modeled by CRPNs, where the truth degrees of places, the certainty factors of rules, and the thresholds of transitions are represented by interval clouds. Moreover, a matrix operation-based reasoning algorithm is proposed to improve the efficiency of calculating final truth degrees, in which both local and ordered weight coefficients are taken into consideration. Finally, a practical example concerning a power system is provided to demonstrate the usefulness and advantages of the proposed CRPN model. [ABSTRACT FROM AUTHOR]

Petri nets (PNs) are capable of modeling nearly any conceivable system and can provide a better understanding of the idealized action sequence in which to most effectively describe or execute said system through their powerful analytical capabilities. However, because real world instances are rarely as consistent and ideal as simulated models, basic PN modeling and simulation properties may be insufficient in practical application. We remedy this through specialization in Fuzzy Petri nets (FPNs). Fuzzy logic is incorporated to better model a self-navigating robot algorithm, thanks to its versatile multi-valued logic reasoning. By using FPNs, it is possible to simulate, assess, and communicate the process and reasoning of the navigational algorithm and apply it to real world programming. In this paper, we propose a series of specific fuzzy algorithms intended to be implemented in concert on a mobile robot platform in order to optimize the sequence of actions needed for a given task, primarily the navigation of an unknown maze. A set of varied maze configurations were developed and simulated as PN and FPN models, providing a testing environment to examine the efficiency of several methodologies. Five methods, including an original proposal in this paper, were compared across 30,000 simulations, evaluating in particular performance in processing cost in time. Our experiments concluded with results suggesting a very competitive task completion time at a considerable fraction in processing cost compared to the closest performing alternatives. [ABSTRACT FROM AUTHOR]

In the highly competitive environment, capturing and disseminating of tacit knowledge are significant to an organization's success with the development of knowledge-based systems. However, in practical knowledge acquisition process, domain experts tend to express their judgments using multigranularity linguistic term sets, and there usually exists uncertain and incomplete information since expert knowledge is experience-based and tacit. In addition, although the technical capabilities of expert systems based on fuzzy Petri nets (FPNs) are expanding, they still fall short of meeting the increasingly complex knowledge demands. Therefore, this paper develops a theoretical model based on linguistic interval 2-tuples and interval-valued intuitionistic FPNs (IVIFPNs) for acquiring and representing tacit knowledge to increase and sustain the competitive advantages of knowledge intensive organizations. An empirical case study in medical practice is provided to demonstrate the application and feasibility of the proposed model, and the results show that it can well capture experts' tacit knowledge and reuse the acquired knowledge productively. [ABSTRACT FROM AUTHOR]

In a multi-state system, because of performance degradation or partial failure, the system or its components could exist in a series of intermediate states between the perfect state and the failure state. These intermediate states will directly influence the system reliability. To address this problem, the fuzzy colored Petri nets (FCPNs) method is proposed. The model fully characterizes the multi-state reliability through fuzzy state information and the dynamic transition between state nodes in the fuzzy colored Petri nets, and the weighted threshold will change with the dynamic change of fuzzy information and node state changes. Therefore, the adaptive fuzzy reasoning algorithm of threshold adjustment is proposed, which provides sufficient model guidance for multistate system reliability analysis and performance optimization. Multi-state reliability is verified, which shows that the FCPNs and the parameter adjustment strategy are reasonable and effective and improve the overall performance of the multi-state system. [ABSTRACT FROM AUTHOR]

Developing an Enterprise Architecture is a complex task and to control the complexity of the regulatory framework we need to measure the relative performance of one system against other available systems. On the other hand, enterprise architecture cannot be organized without the use of a logical structure. The framework provides a logical structure for classifying architectural output. Among the common architectural framework, the C4ISR framework and methodology of the product is one of the most popular techniques. In this paper, given the existing uncertainties in system development and information systems, a new version of UML called Fuzzy-UML is proposed for enterprise architecture development based on fuzzy Petri nets. In addition, the performance of the system is also evaluated based on Fuzzy sequence diagram.

As large amounts of hazardous chemicals are handled in the petrochemical industries, the plants in these industries are attractive for terrorists because they can cause great losses and have important social impact. Security risk assessment is important to determine the risk level of a plant in order to take targeted measures to reduce the security risk. Based on Security Risk Factor Table (SRFT) which covers the essential elements for the security risk assessment, a weighted fuzzy Petri-net (WFPN) based security risk assessment approach is proposed in this paper. This approach can easily model different relationships between the risk factors as well as their importance, and use the analysis method of Petri-nets to perform the risk assessment. Two WFPN models of security risk assessment are established according to different relationships between the factors, and a matrix operation based security risk inference method is developed. An illustrative example is used to demonstrate the approach. The results show that correctly determining and modeling the relationships among the risk factors is important to assess the security risk. [ABSTRACT FROM AUTHOR]

Fuzzy Petri net (FPN), as one type of high-level Petri nets, has attracted a lot of attention over the recent decade due to its adequacy for knowledge representation and logic reasoning. However, in the FPN literature, the truth degrees of input places are usually given directly or supposed by researchers. No or little research has been performed on the determination of initial marking vector for a specific FPN. In this correspondence paper, we introduce a group decision-making model using hesitant 2-tuple linguistic term sets to obtain the initial truth values of FPNs based on domain experts’ knowledge and gathered data. As is illustrated by the numerical example, the proposed framework can well capture domain experts’ diversity judgements and derive initial truth degrees for an FPN under different types of uncertainties. [ABSTRACT FROM AUTHOR]

With the advances in technique for high throughput data gathering such as microarrays, DNA sequencing machines and continuous development of human genome project, the traditional physical and chemical methods have been more difficult to meet the requests of time consuming and results accuracy. Exploring and understanding the causal relationship of complex gene regulatory networks and transforming the massive data of large-scale biological research to useful biological knowledge are the present challenge. As a result, there are two typical applications both the confidence value prediction of DNA sequence and influence degree prediction of gene expression which have become the hot issues in our daily life. In this paper, two extended fuzzy Petri nets approaches are proposed, based on the existing fuzzy Petri net model, to model and analyze for the hot issues respectively. One is the fuzzy colored Petri net, which combines fuzzy Petri net with colored Petri net to model fuzzy rule-based reasoning and determine confidence values for bases called in DNA sequence. The other is extended fuzzy Petri net, which integrates reverse reasoning into fuzzy Petri net and is proposed to model gene regulatory network. It can predict the change in expression level of target based on the input expression level of activator/repressor. Compared with the method of fuzzy Petri net, the two extended fuzzy Petri nets models perform more accurately in the following typical experiment reasoning outcomes and show that the proposed methods are feasible and available. [ABSTRACT FROM AUTHOR]

In this paper a Fuzzy Petri Net model (FPN) is developed and tested to assess the risks involved during Enterprise Resource Planning (ERP) adoption in Small and Medium Enterprises (SMEs). This model pertains only to the risks and risk factors identified in the extension phase of ERP adoption in SMEs. In other words, this work studied only the risk perception relating to extending the ERP integration between the supply chain partners. This paper addresses the problems of risk identification and assessment during the extension phase. There are two motives for this research namely, one to define the ERP extension risks and two to assess and quantify the risks in a simple yet robust model. The uniqueness of this paper is three dimensional because in addition to presenting and quantifying the risk values in FPN, the risks are categorized and further graded for decision support to SME stakeholders. Realignment and standardization of SMEs business process, lack of support from larger enterprises and incompatibility issues are perceived to be most critical risk factors. [ABSTRACT FROM AUTHOR]

In order to provide personalized healthy diet solutions based on unique health status and external environment，this paper proposed an intelligent generation mechanism based on fuzzy Petri nets. Major inputs were basic information and health status of users，as well as external environmental information. This model adopted parallel reasoning using matrix calculation to get users' personalized appropriate and taboo food based on fuzzy Petri nets transformed from large-scale of healthcare knowledge. Personalized healthy diet recommendation system has already adopted this mechanism and according to user tests，it is able to accurately recommend daily personalized healthy diet solutions for different users. [ABSTRACT FROM AUTHOR]

Fuzzy Petri nets (FPNs) are a potential modeling technique for knowledge representation and reasoning of rule-based expert systems. To date, many studies have focused on the improvement of FPNs and various new algorithms and models have been proposed in the literature to enhance the modeling power and applicability of FPNs. However, no systematic and comprehensive review has been provided for FPNs as knowledge representation formalisms. Giving this evolving research area, this work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms, knowledge representations and FPN models. In addition, we provide a survey of the applications of FPNs for solving practical problems in variety of fields. Finally, research trends in the current literature and potential directions for future investigations are pointed out, providing insights and robust roadmap for further studies in this field. [ABSTRACT FROM AUTHOR]

Evaluation of node's trust value is certainly advantage in mobile Adhoc networks (MANETs) where the applications run efficiently by involving trustable nodes only. The proposed method "A Novel QoS Trust computation in MANETs using Fuzzy Petri Nets-QTFPN", evaluates node trust value based on its quality of service (QoS) parameters. Here the MANET is represented as Dynamic Adaptive Fuzzy Petri Nets (DAFPN) model with concurrent reasoning algorithm (CRA). In which delivery of each packet from node to node requires evaluation of certainty factor (μ) using fuzzy expert system. This fuzzy inference system uses QoS parameters as fuzzy input variables namely energy, bandwidth, node mobility and reliability. In the routing process the intermediate node's trust values are evaluated based on certainty factor. The concurrent reasoning algorithm can strengthen the proposed method in selection of quality path to destination and reestablishment of path in case of path breaks. The proposed method performance is analyzed theoretically in terms of time and space complexities. The simulation results are taken against node velocity and network size, where the proposed method outperforms the existing protocols. [ABSTRACT FROM AUTHOR]

These days, we see many organizations with extremely complex systems with various processes, organizational units, individuals, and information technology support where there are complex relationships among their various elements. In these organizations, poor architecture reduces efficiency and flexibility. Enterprise architecture, with full description of the functions of information technology in the organization, attempts to reduce the complexity of the most efficient tools to reach organizational objectives. Enterprise architecture can better assess the optimal conditions for achieving organizational goals. For evaluating enterprise architecture, executable model need to be applied. Executable model using a static architectural view to describe necessary documents need to be created. Therefore, to make an executable model, we need a requirement to produce products of the enterprise architecture to create an executable model. In this paper, for the production of an enterprise architecture, object-oriented approach is implemented. We present an algorithm to use stereotypes by considering reliability assessment. The approach taken in this algorithm is to improve the reliability by considering additional components in parallel and using redundancy techniques to maintain the minimum number of components. Furthermore, we implement the proposed algorithm on a case study and the results are compared with previous algorithms.