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The present work is an effort to support the typographical errors of keywords that are not supported by existing compilers and integrated development environment(IDE) in 'C' language. The fuzzy automata modelling approximate string matching is proposed for error handling during lexical analysis. By introducing fuzziness to lexemes the typographical errors can be rectified at the time of compilation and flexibility of lexical analyser can be greatly improved. The recognition of fuzzy tokens during lexical analysis is described in order to correct errors caused by sticking key, deletion, typing and swapping key in keywords during C programming. Algorithms and pseudo code are being developed to measure the degree of membership of crisp and fuzzy lexemes. Accuracy is tested and examined once the fuzzy lexemes are trained using a neural network. The proposed method is an add on feature that can be incorporated in existing compilers and IDEs to increase their flexibility. [ABSTRACT FROM AUTHOR]

In this paper, at first, we define the notion of general fuzzy automaton over a field; we call this automaton vector general fuzzy automaton (VGFA). Moreover, we present the concept of max-min vector general fuzzy automaton. We show that if two max-min VGFA are similar, they constitute an isomorphism. After that, we prove that if two VGFA constitute an isomorphism with threshold α, they are equivalent with threshold α, where α ∈ [ 0 , 1 ]. Also, some examples are given to clarify these new notions. [ABSTRACT FROM AUTHOR]

Enterprise Internet of Things (E-IoT) systems allow users to control audio, video, scheduled events, lightning fixtures, door access, and relays in complex smart installations. These systems are widely used in government or smart private offices, smart buildings/homes, conference rooms, schools, hotels, and similar professional settings. However, even with their widespread use, the security of many E-IoT systems and components has not been researched in the literature. To address this research gap, we focus on E-IoT communication buses, one of the core components used to connect E-IoT devices, and introduce LightningStrike attacks that demonstrate several weaknesses with E-IoT proprietary communication protocols used in E-IoT communication buses. Specifically, we show that popular E-IoT proprietary communication protocols are susceptible to Denial-of-Service (DoS), eavesdropping, impersonation, and replay attacks. As such threats cannot be mitigated through traditional defense mechanisms due to the limitations posed by E-IoT, we propose LGuard, a defense system to protect E-IoT systems against the attacks over communication buses. LGuard uses closed-circuit television footage and computer vision techniques to detect replay attacks. For impersonation and DoS attacks, LGuard utilizes traffic analysis. Finally, LGuard obfuscates the E-IoT traffic via inserting redundant traffic to the bus against eavesdropping attacks. We evaluated the performance of LGuard in a realistic E-IoT deployment, and our detailed evaluations show that LGuard achieves an overall accuracy and precision of 99% in detecting DoS, impersonation, and replay attacks while effectively increasing the difficulty of extracting valuable information for eavesdroppers. In addition, LGuard does not incur any operational overhead or modification to the existing E-IoT system. [ABSTRACT FROM AUTHOR]

Zigbee is widely adopted as a resource-efficient wireless protocol in the IoT network. IoT devices from manufacturers have recently been affected due to major vulnerabilities in Zigbee protocol implementations. Security testing of Zigbee protocol implementations is becoming increasingly important. However, applying existing vulnerability detection techniques such as fuzzing to the Zigbee protocol is not a simple task. Dealing with low-level hardware events still remains a big challenge. For the Zigbee protocol, which communicates over a radio channel, many existing protocol fuzzing tools lack a sufficient execution environment. To narrow the gap, we designed Z-Fuzzer, a device-agnostic fuzzing tool for detecting security flaws in Zigbee protocol implementations. To simulate Zigbee protocol execution, Z-Fuzzer leverages a commercial embedded device simulator with pre-defined peripherals and hardware interrupt setups to interact with the fuzzing engine. Z-Fuzzer generates more high-quality test cases with code-coverage heuristics. We compare Z-Fuzzer with advanced protocol fuzzing tools, BooFuzz and Peach fuzzer, on top of Z-Fuzzer's simulation platform. Our findings suggest that Z-Fuzzer can achieve greater code coverage in Z-Stack, a widely used Zigbee protocol implementation. Compared to BooFuzz and Peach, Z-Fuzzer found more vulnerabilities with fewer test cases. Three of them have been assigned CVE IDs with high CVSS scores (7.5~8.2). [ABSTRACT FROM AUTHOR]

There exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the models should be designed by domain experts, who have the required knowledge for the design of the processes, but do not have the necessary technical knowledge. To address this limitation, this paper proposes MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. With this goal in mind, we have developed a framework for fuzzy automaton model design based on a Domain- Specific Modeling Language (DSML) and a graphical editor. To improve the interoperability and functionality of this framework, it also includes a model-to-text transformation that translates the models designed by using the graphical editor into a format that can be used by a tool for data anal- ysis. The practical value of this proposal is also evaluated through a non-trivial medical protocol for detecting potential heart problems. The results confirm that MODELFY is useful for defining such a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain experts.

The problem of reversion of unknown input sequences of fuzzy discrete systems by its observed outputs is investigated. As a mathematical model of fuzzy systems the fuzzy automata (FA) is used. This problem has been considered earlier for deterministic systems. Unambiguous solutions of the problem for such systems have been obtained using the model of finite automata, called the information lossless automata (IL-automata). In the article, for fuzzy discrete systems described by the FA model a similar problem is considered. Due to the specifics of functioning of such systems, unambiguous decoding of messages coming to their inputs is not always possible in principle. For this reason, there are problems of minimization of information lossless (according to various criteria) while solving the address problem. Automata are introduced, which allow solving such problems, called automata with minimized information lossless (FA MIL-automata). Solution of the problem of reversion for FA is a finite set of input words. Each such solution can be estimated according to various criteria — the cardinality of a set of words of the solution, the probability of appearance of these words on the system inputs, the complexity of obtaining different variants of the solutions. In order to minimize information lossless, the article formulates corresponding optimization tasks for FA and specifies possible ways of solving them. Different kinds of FA MIL-automata are considered. The obtained results show that the considered problems of reversion for fuzzy automata inputs are multi-criteria. It is known that solutions of such problems for discrete systems are traditionally evaluated by only one criterion.

Approximate bisimulations for fuzzy automata have recently drawn attention of researches, since they allow to correlate different fuzzy automata which behave equivalently only to the chosen degree. This is of a particular interest in the state reduction of fuzzy automata, since it allows us to find a fuzzy automaton with a much smaller number of states and a slightly different behaviour from the original fuzzy automaton. The contribution of this paper is twofold. First, we study special types of approximate bisimulations which allow us to correlate all elements from both sets of states of two fuzzy automata. We show their properties, and particularly that they induce the so-called approximate isomorphism between the corresponding factor fuzzy automata. We pay a special attention to similarities and differences between homotypic and heterotypic approximate bisimulations. Second, we provide an efficient algorithm with the complexity O ((m + n) n) for computing the greatest λ -approximate bisimulation between two finite fuzzy automata over the Gödel structure (if it exists), where λ is the threshold for approximation, n is the number of states and m is the number of non-zero transitions of the automata. This algorithm gives a great reduction of complexity when compared to the previously developed one, which has the complexity O (m n 5). We also design an algorithm with the complexity O ((m + n) n) for computing the greatest λ ∈ [ 0 , 1 ] such that there exists a λ -approximate bisimulation between two given finite fuzzy automata over the Gödel structure. [ABSTRACT FROM AUTHOR]

Compared to other system modeling techniques, the fuzzy discrete event systems (FDESs) methodology has the unique capability of modeling a class of event-driven systems as fuzzy automata with ambiguous state and event-invoked state transition. In two recent papers, we developed algorithms for online-supervised learning of the fuzzy automaton’s event transition matrix using fuzzy states before and after the occurrence of fuzzy events. The post-event state was assumed to be readily available while the pre-event state was either directly available or estimatable through learning. This article is focused on algorithm development for learning the transition matrix in a different setting—when the pre-event state is available but the post-event state is not. We suppose the post-event state is described by a fuzzy set that is linked to a (physical) variable whose value is available. Stochastic-gradient-descent-based algorithms are developed that can learn the transition matrix plus the parameters of the fuzzy sets when the fuzzy sets are of the Gaussian type. Computer simulation results are presented to confirm the theoretical development. [ABSTRACT FROM AUTHOR]

Abstract: There exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the models should be designed by domain experts, who have the required knowledge for the design of the processes, but do not have the necessary technical knowledge. To address this limitation, this paper proposes MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. With this goal in mind, we have developed a framework for fuzzy automaton model design based on a DomainSpecific Modeling Language (DSML) and a graphical editor. To improve the interoperability and functionality of this framework, it also includes a model-to-text transformation that translates the models designed by using the graphical editor into a format that can be used by a tool for data analysis. The practical value of this proposal is also evaluated through a non-trivial medical protocol for detecting potential heart problems. The results confirm that MODELFY is useful for defining such a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain experts. [ABSTRACT FROM AUTHOR]

The problem of checking whether a state in a finite fuzzy labeled transition system (FLTS) crisply simulates another is one of the fundamental problems of the theory of FLTSs. This problem is of the same nature as computing the largest crisp simulation between two finite FLTSs. A naive approach to the latter problem is to crisp the given FLTSs and then apply one of the currently known best methods to the obtained crisp labeled transition systems. The complexity of the resulting algorithms is of order O (l (m + n) n), where l is the number of fuzzy values occurring in the specification of the input FLTSs, m is the number of transitions and n is the number of states of the input FLTSs. In the worst case, l can be m + n and O (l (m + n) n) is the same as O ((m + n) 2n). In this article, we design an efficient algorithm with the complexity O ((m + n) n) for computing the largest crisp simulation between two finite FLTSs. This gives a significant improvement. We also adapt our algorithm to computing the largest crisp simulation between two finite fuzzy automata. [ABSTRACT FROM AUTHOR]

Opacity is an important security notion for discrete event systems (DESs) to measure the secret behaviors leaked to an intruder. The intruder is usually modeled as a malicious observer who always attempts to infer secret behaviors based on his dynamical observations of the system. In our earlier research, we proposed several opacity measures for DESs from the perspective of fuzzy logic, in which the intruder is assumed to only infer the current secret behaviors. Considering that modern computers usually have a powerful computation ability and rich memory resources, this article presents a new opacity measure of DESs, called as fuzzy infinite-step opacity (FIO) measure, where the intruder is assumed to have the ability of inferring all the secret behaviors that occurred in the past. In addition, the properties and the calculation of the FIO measure are also investigated. The FIO measure can be used to assess the secret-leakage situations of fuzzy DESs attacked by powerful intruders. For the purpose of illustration, we present a practical application of the FIO measure in evaluating the privacy protection mechanisms in location-based services. [ABSTRACT FROM AUTHOR]

In this article, we define $\lambda$ -approximate simulations and bisimulations for fuzzy automata over complete Heyting algebras. The value $\lambda$ presents the degree of language similarity or equality between observed fuzzy automata. Algorithms for computing the greatest $\lambda$ -approximate simulations and bisimulations are given. We show that $\lambda$ -approximate simulations and bisimulations on a fuzzy automaton can be effectively used for factorization of fuzzy automata. We present the algorithm that splits the interval of the degrees of language similarity or equality into subintervals with the same minimal corresponding factor fuzzy automata. [ABSTRACT FROM AUTHOR]

Due to the globalization of competition in various fields, suppliers must have the best performance by creating different competitive advantages. In addition, the oil and gas industry has lots of sustainability issues in the supply phase. Accordingly, this study provides a comprehensive framework for ranking and selecting sustainable suppliers in the oil and gas industry. In this research, first, using the fuzzy analytical network process method, the indicators of selecting stable suppliers are ranked. Next, by applying the Electre method, the most appropriate suppliers are chosen. Numerical results show that the quality indicators of materials purchased from suppliers and commitment to contracts were ranked first and second among all criteria. Moreover, the efficiency of the proposed framework is approved by comparing with the real-world situation. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy-based assessment model to evaluate system reliability of a labour-intensive manufacturing system with repair actions. Due to the uncertainty in human performance, labour-intensive manufacturing systems must determine the capacity of each labourer in order to accurately characterise the performance of the systems. Therefore, we model such a manufacturing system as a fuzzy multi-state network in order to characterise the labourers’ influence on workstation performance. First, the workstation reliability is defined according to the loading state by three fuzzy membership functions, namely ‘under loading’, ‘normal loading’ and ‘over loading’, respectively. The system reliability is subsequently evaluated with fuzzy intersection operations in terms of these workstation reliabilities. Thus, the system reliability is defined as a fuzzy membership function to assess whether the manufacturing system performance is sufficient to satisfy the demand reliably. A case study of a footwear manufacturing system is illustrated to explain the proposed model. Furthermore, we apply the proposed model to a non-labour-intensive manufacturing network in order to validate the applicability to this class of systems. [ABSTRACT FROM PUBLISHER]

Manufacturing firms generate a massive amount of data points because of higher than ever connected devices and sensor technology adoption. These data points could be from varied sources, ranging from flow time and cycle time through different machines in an assembly line to shop floor data collected from sensors viz. temperature, stress capability, pressure, etc. Analysis of this data can help manufacturers in many ways, viz. predict breakdown—reduction in downtime and waste, optimal inventory level—resource optimization, etc. The data may be highly voluminous, highly unstructured, coming from varied sources at a higher speed. Thus, big data analytics has become more critical than ever for the manufacturing industry to have the capability of effectively deriving business value from the vast amount of generated data. Manufacturing firms face hindrances and failures in the implementation of big data analytics. It is, therefore, necessary for the companies in the Indian manufacturing sector to identify and examine the reason and nature of barriers resisting the implementation of Big Data Analytics (BDA) to their organization. This paper explores the existing literature available to identify the barriers, categorized based on different functions of an organization. A total of 16 barriers are determined from the rigorous review of existing research. A survey is conducted on the industry experts from automobile, steel, automotive parts manufacturer, and electrical equipment industries to obtain a contextual relationship between the barriers. Interpretive Structural Modeling and MICMAC (Cross-impact matrix multiplication applied to classification) are the analytical techniques used in this research to classify the barriers into different impact levels and importance. Independent factors (barriers) have high driving power and are the key factors that were further analyzed using Fuzzy AHP to determine their comparative priority/importance. The result of this research shows that barriers related to Management and Infrastructure & Technology are the main hurdles in the implementation of big data analytics in the manufacturing industry. Six critical barriers (based on high driving power) are; lack of long-term vision, lack of commitment from top management, lack of infrastructure facility, lack of funding, lack of availability of specific data tools, and lack of training facility. Lack of commitment from top management is the most critical barrier. Research focuses on a comprehensive analysis of the barriers in implementing big data analytics (BDA) in manufacturing firms. The novelty lies in (a) finding an extensive list of barriers, (b) application domain and geography, and (c) the multi-criteria decision making technique used for finding the critical barriers to the implementation of big data analytics. The findings of this research will help industry leaders to formulate a better plan before the application of BDA in their organizations. [ABSTRACT FROM AUTHOR]

Opacity, as an important security property, has been well investigated in crisp discrete event systems. However, in some practical systems, the general behaviors, secret and nonsecret behaviors obtained by the intruder need to be expressed as fuzzy sets rather than crisp sets. Under this situation, the intruder makes inferences by fuzzy logic rather than classic logic, thus the notions of the fuzzy opacity measures are desired to be established. First, by means of the basic laws of fuzzy logic, a general fuzzy opacity measure, as a template, is presented, in this article, for the given generalized characterizations of secret and nonsecret behaviors. Moreover, a specific measure, called as fuzzy initial-final opacity (FIFO) measure, is investigated under the assumption that the secrets and nonsecrets can be coded into the initial-final state-pairs. In addition, two variants of FIFO measure are also proposed under the assumptions that the secrets and nonsecrets are only related to the initial-states or final-states. With these fuzzy opacity measures, the system designers can evaluate the opacity degree of the systems to be designed. To show the applicability of the fuzzy opacity measures to practical systems, we finally present an illustrative example of evaluating the location privacy protection systems in location-based services by means of the fuzzy opacity measures. [ABSTRACT FROM AUTHOR]

Since fuzzy discrete-event systems (FDESs) modeled by fuzzy automata were put forward, extensive research on FDESs has been successfully conducted from different perspectives. Recently, the safe codiagnosability of decentralized FDESs was introduced and an approach of constructing the safe codiagnoser to verify the safe codiagnosability was proposed. However, the complexity of constructing the safe codiagnoser of decentralized FDESs is exponential. In this paper, we present a polynomial verification. Firstly, the recognizer and the safe coverifier are constructed to recognize the prohibited strings in the illegal language and carry out safe diagnosis of decentralized FDESs, respectively. Then the necessary and sufficient condition for safe codiagnosability of decentralized FDESs is presented. In particular, an algorithm for verifying the safe codiagnosability of decentralized FDESs is proposed based on the safe coverifier. Notably, both complexities of constructing the safe coverifier and verifying the safe codiagnosability are polynomial in the numbers of fuzzy events and fuzzy states of FDESs. Finally, two examples are provided to illustrate the proposed algorithm and the derived results. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for the approximate determinization of fuzzy automata over the product structure. We introduce the parametric modification of the product t-norm in the pre-determinization setting. On the one hand, the behavior of a fuzzy automaton over the parametric t-norm differs from the behavior of the fuzzy automaton over the product t-norm only in words with a degree of acceptance below the given parameter. However, using the parametric t-norm, we obtain an algorithm that outputs a finite minimal deterministic fuzzy automaton whose behavior differs from the starting fuzzy automaton described above. By setting the parameter to a sufficiently small value, the proposed algorithm provides a deterministic fuzzy automaton with behavior that differs insignificantly from the starting fuzzy automaton, as the difference is achieved only for words accepted by the starting fuzzy automaton with an insignificant value. As a tradeoff, the proposed approach provides finite determinization, even when all other determinization methods would result in an infinite deterministic automaton. We support this fact with an illustrative example. [ABSTRACT FROM AUTHOR]

Bisimulation is a well-known notion in modal logic and the theory of labeled transition systems. It is used for characterizing indiscernibility between states and has important applications in minimizing structures, separating expressive powers of modal and related logics, as well as concept learning in description logics (DLs). Fuzzy bisimulation is a counterpart of bisimulation for dealing with fuzzy structures. In this article, we present an efficient algorithm with a complexity $O((m+n)n)$ for computing the greatest fuzzy bisimulation between two finite fuzzy interpretations in the fuzzy DL $\mathit {f}\!\mathcal {ALC}$ under the Gödel semantics, where $n$ is the number of individuals and $m$ is the number of nonzero instances of roles in the given fuzzy interpretations. We also adapt our algorithm for computing fuzzy bisimulations and simulations between fuzzy finite automata, as well as for dealing with other fuzzy DLs. The resulting algorithms are much more efficient than the previously known ones, as they reduce the complexity from $O(n^5)$ to $O((m+n)n)$. [ABSTRACT FROM AUTHOR]

Refinement relations, such as trace containment, simulation preorder, and their alternating versions, have been successfully applied in formal verification of concurrent systems. Recently, trace containment and simulation preorder have been adopted and developed in fuzzy systems, but the generalization of their alternating versions to fuzzy systems has not been investigated. To satisfy the need for modeling and analyzing fuzzy systems, this article proposes two types of refinement relations called fuzzy alternating trace containment and fuzzy alternating simulation preorder, based on fuzzy concurrent game structures (FCGSs) under the Gödel semantics. These two fuzzy notions inherit properties from the corresponding classical setting. For example, fuzzy alternating simulation preorder for finite-state FCGSs can be computed in polynomial time; fuzzy alternating simulation preorder is a fuzzy subset of fuzzy alternating trace containment, and both relations can be logically characterized in terms of fuzzy version of alternating-time temporal logic. These properties make the theory developed here suitable for the modeling and verification of fuzzy systems. [ABSTRACT FROM AUTHOR]

Extending classical algorithms for ordinary weighted or string-to-string automata to automata with underlying more general algebraic structures is of significant practical and theoretical interest. However, the generalization of classical algorithms sets certain assumptions on the underlying structure. In this respect the maximal factorization turns out to be a sufficient condition for many practical problems, e.g. minimization and canonization. Recently, an axiomatic approach on monoid structures suggested that monoids with most general equalizer (mge-monoids) provide an alternative framework to achieve similar results. In this paper, we study the fundamental relation between monoids admitting a maximal factorization and mge-monoids. We describe necessary conditions for the existence of a maximal factorization and provide sufficient conditions for an mge-monoid to admit a maximal factorization. [ABSTRACT FROM AUTHOR]

Current computing concepts are migrated to a an emerging technology called cloud computing. Self-adaptive resource allocation framework and intelligent machine learning framework are proposed in various research work for providing optimized resource allocation. Privacy and security are the major concerns which restricts it's adoption to clouds. Outsourcing, resource sharing and multi-tenancy are introduced by cloud to overcome security concerns. Stackelberg Game Theory Framework (SGTF) is used in proposed security model for enhancing data's confidentiality level and over cloud environment. For multiple correlated VMs (migration requests), migration problem is studied in this work and for solving the same, an Enhanced Artificial Neural Network (EANN) and IDS based on fuzzy is introduced. In this method, migration request is included with correlations between VMs and these correlated VMs are treated integrally rather than separate treatment. [ABSTRACT FROM AUTHOR]

The fuzzy discrete event system theory is unique in that it is capable of modeling a class of event-driven systems as fuzzy automata with states and event-invoked state transitions being ambiguous. At present, the theory lacks a self-learning component, an important topic that has hardly been touched in the literature. In this article, we use stochastic gradient descent to develop online learning algorithms for the fuzzy automata. We uncover an inherent obstacle in the initial derived algorithms that fundamentally restricts their learning capability owing to dependences of the model parameters to be learned. We develop a novel mechanism to not only overcome the obstacle but also make the learning adaptive. Our final algorithms can learn an event transition matrix based on automaton's states before and after the occurrence of a fuzzy event, and learn the transition matrix and multidimensional Gaussian fuzzy sets yielding initial automaton states from relevant input variables and target states. Computer simulation results are presented to show learning performance of the final algorithms. [ABSTRACT FROM AUTHOR]

The ability to build lean and agile supply chains has not developed as rapidly as anticipated, because the development of technology to manage such concepts of lean/agile for integrated supply chains is still under way. Also, due to ill-defined and vague indicators, which exist within leanness/agility assessment, many measures are described subjectively by linguistic terms, which are characterized by vagueness and multi-possibility, and the conventional assessment approaches cannot suitably or effectively handle such dynamic situations. In this paper, we propose a novel approach to model agility (which includes leanness) and introduce dynamic agility level index (DALi) through fuzzy intelligent agents. Generally, it is difficult to emulate human decision making if the recommendations of the agents are provided as crisp, numerical values. The multiple intelligent agents used in this study communicate their recommendation as fuzzy numbers to accommodate ambiguity in the opinion and the data used for modelling agility attributes for integrated supply chains. Moreover, when agents operate based on different criteria pertaining to agility like flexibility, profitability, quality, innovativeness, pro-activity, speed of response, cost, robustness, etc., for integrated supply chains, the ranking and aggregation of these fuzzy opinions to arrive at a consensus is complex. The proposed fuzzy intelligent agents approach provides a unique and unprecedented attempt to determine consensus in these fuzzy opinions and effectively model dynamic agility. The efficacy of the proposed approach is demonstrated with the help of an illustrative example. [ABSTRACT FROM AUTHOR]

The present research work outlines a fuzzified approach using fuzzy linear programming (FLP) using a suitably designed smooth logistic membership function (MF) for finding fuzziness patterns at disparate levels of satisfaction for theory of constraints-based (TOC) product-mix decision problems. The objective of the present work is to find fuzziness patterns of product-mix decisions with disparate levels of satisfaction of the decision-maker (DM). Another objective is to provide a robust, quantified monitor of the level of satisfaction among DMs and to calibrate these levels of satisfaction against DM expectations. Product-mix decision should take into account considerations such as the DM's level of satisfaction (sometimes called 'emotions') in order to make the decision a robust one. Sensitivity of the decision has been focused on a bottleneck-free, optimal product-mix solution of a TOC problem. The inefficiency of traditional linear programming (LP) in handling multiple-bottleneck problems using TOC is discussed using an illustrative example. Relationships among the degree of fuzziness, level of satisfaction and the throughput of modified TOC guide decision-makers (DM) under tripartite fuzzy environment in obtaining their product-mix choice trading-off with a pre-determined allowable fuzziness. [ABSTRACT FROM AUTHOR]

Reliable data transmission based on dual mode of communication (RTMDC) of wireless sensor network is put forward targeting the influence of electromagnetic radiation on sensor node communication ability. In RTDMC algorithm, dual-communication mode is adopted for the sensor node. Intermediate failure node is checked in real time with D-S evidence theory DST and hypothesis testing. In case of intermediate failure node, acoustic communication mode is adopted to resist the influence of electromagnetic radiation. Otherwise, radio frequency communication mode is adopted and normal communication is executed. Dual-communication mode enhances the data transmission efficiency, shortens the transmission time delay, and reduces the energy consumption. Simulation data suggests that compared with single-communication mode, dual-communication mode is able to effectively improve the data transmission efficiency and energy utilization. [ABSTRACT FROM AUTHOR]

Uncertainty and imprecision play an important role in the specification and analysis of complex systems. Therefore, it is important to provide methodologies and tools to support the correct development of these systems. In this paper we present a new formalism, based on fuzzy automata, to facilitate the different phases involved in the development of a system where information is fuzzy. The formal syntax and semantics of our formalism are based on previous work, which has been adapted to be easily implemented and automated. We introduce a methodology to analyze systems modelled with one of our fuzzy automata. Finally, we show how our framework can be used to define a model of the heart based on electrocardiograms (ECGs) and use this model to analyze data of real patients. [ABSTRACT FROM AUTHOR]

In this paper, we propose a canonization method for fuzzy automata, i.e., a determinization method that is able to return a minimal fuzzy deterministic automaton equivalent to the original fuzzy automaton. The canonization method is derived from the well-known Brzozowski's algorithm for ordinary nondeterministic automata. For a given fuzzy automaton A , we prove that the construction M ˆ (r (N (r (A)))) returns a minimal fuzzy deterministic automaton equivalent to A. In that construction, r (.) represents the reversal of a fuzzy automaton, N (.) is the determinization of a fuzzy automaton based on fuzzy accessible subset construction, and M ˆ (.) is the determinization of a fuzzy automaton via factorization of fuzzy states which also includes a simple reduction of a particular case of proportional fuzzy states. The method is accomplished for fuzzy automata with membership values over the Gödel structure (also called max-min fuzzy automata). These fuzzy automata are always determinizable and have been proved useful in practical applications. [ABSTRACT FROM AUTHOR]

Uncertainty and imprecision play an important role in the specification and analysis of complex systems. Therefore, it is important to provide methodologies and tools to support the correct development of these systems. In this paper we present a new formalism, based on fuzzy automata, to facilitate the different phases involved in the development of a system where information is fuzzy. The formal syntax and semantics of our formalism are based on previous work, which has been adapted to be easily implemented and automated. We introduce a methodology to analyze systems modelled with one of our fuzzy automata. Finally, we show how our framework can be used to define a model of the heart based on electrocardiograms (ECGs) and use this model to analyze data of real patients. [ABSTRACT FROM AUTHOR]

This paper gives a decentralized approach to the problem of failure prognosis in the framework of fuzzy discrete event systems (FDES). A notion of co-predictability is formalized for decentralized prognosis of FDESs, where several local agents with fuzzy observability rather than crisp observability are used in the prognosis task. An FDES is said to be co-predictable if each faulty event can be predicted prior to its occurrence by at least one local agent using the observability of fuzzy events. The verification of the decentralized predictability is performed by constructing a fuzzy co-verifier from a given FDES. The complexity of the fuzzy co-verifier is polynomial with respect to the FDES being predicted, and is exponential with respect to the number of the local prognosis agents. Then, a necessary and sufficient condition for the co-predictability of FDESs is given. In addition, we show that the proposed method may be used to deal with the decentralized prognosis for both FDESs and crisp DESs. Finally, to illustrate the effectiveness of the approach, some examples are provided. [ABSTRACT FROM AUTHOR]

The computational study on finite/infinite-state systems, probabilistic systems, and finite-state fuzzy systems, has received much attention recently. In contrast, there are very few results for algorithmic analysis of infinite-state fuzzy systems. In this paper, we introduce fuzzy pushdown termination games (FPDTGs), which are an extension of fuzzy pushdown automata with a game feature and can serve as a formal model of infinite-state fuzzy systems. We investigate some computational issues of the games under termination objectives for two players: the goal of player-1 is to maximize the truth value of eventually terminating at some given configurations with the empty stack, while player-2 aims at the opposite. Some interesting results are obtained. For example, we show that both players have optimal memoryless strategies and the same value. The problem of computing the value can be solved in exponential time when the triangular norm is chosen as the minimum one. Furthermore, we present efficient algorithms for computing the values of two special subclasses of FPDTGs. The potential for practical use of our model is demonstrated by a case study on a manufacturing system. [ABSTRACT FROM AUTHOR]

Abstract Recently, the so-called family of T -power based implications was introduced. These operators involve the use of Zadeh's quantifiers modelled by powers of t-norms in its definition. Due to the fact that Zadeh's quantifiers constitute the usual method to modify fuzzy propositions, most of the members of this family of fuzzy implication functions satisfy an important property in approximate reasoning such as the invariance of the truth value of the fuzzy conditional when both the antecedent and the consequent are modified using the same quantifier. In this paper, an in-depth analysis of this property is performed by characterizing all binary functions satisfying it. From this general result, a fully characterization of the subfamily of T -power based implications which satisfy the invariance property is presented. Furthermore, a characterization of the whole family of T -power based implications is proved in which a novel property which slightly resembles the hypothetical syllogism plays a key role. [ABSTRACT FROM AUTHOR]

Simulations and bisimulations are well-established notions in crisp/fuzzy automata theory and are widely used to compare the behaviors of automata. Their main drawback is that they compare the behaviors of fuzzy automata in a crisp manner. Recently, fuzzy simulations and fuzzy bisimulations have been defined for fuzzy automata as a kind of approximate simulations and approximate bisimulations that compare the behaviors of fuzzy automata in a fuzzy manner. However, they still suffer from serious shortcomings. First, they still cannot correlate all fuzzy automata that are intuitively "more or less" (bi)similar. Second, the currently known algorithms for computing the greatest fuzzy simulation or bisimulation between two finite fuzzy automata have an exponential time complexity when the Łukasiewicz or the product structure of fuzzy values is used. This work deals with these problems, providing approximations of fuzzy simulations and fuzzy bisimulations. We define such approximations via a novel notion of decreasing sequences of fuzzy relations whose infima are, under some conditions, fuzzy simulations (respectively, bisimulations). We call such a sequence a depth-bounded fuzzy simulation (respectively, bisimulation), as the n th element from the sequence compares the behaviors of fuzzy automata, but only for words with a length bounded by n. We further provide a logical characterization of the greatest depth-bounded fuzzy simulation or bisimulation between two fuzzy automata by proving that it satisfies the corresponding Hennessy-Milner property. Finally, we provide polynomial-time algorithms for computing the n th component of the greatest depth-bounded fuzzy simulation (respectively, bisimulation) between two finite fuzzy automata. [ABSTRACT FROM AUTHOR]

The coalgebraic method is of great significance to research in process algebra, modal logic, object-oriented design and component-based software engineering. In recent years, fuzzy control has been widely used in many fields, such as handwriting recognition and the control of robots or air conditioners. It is then an interesting topic to analyze the behavior of fuzzy automata from a coalgebraic point of view. This paper models different types of fuzzy automata as coalgebras with a monad structure capturing fuzzy behavior. Based on the coalgebraic models, we can define a notion of fuzzy language and consider several versions of bisimulation for fuzzy automata. A group of combinators is defined to compose fuzzy automata of two branches: state transition and output function. A case study illustrates the coalgebraic models proposed and their composition.

Highlights • Field surveys were conducted to obtain traffic movement data at signalised junctions. • Fuzzy inference system is applied to fit the field data for different scenarios. • FCA model was developed to evaluate the safety impacts of RWA at signalised junctions. • Conflict severity becomes less severe at junctions installed with RWA. Abstract Right-turn waiting area (RWA) is a short demarcated queueing area ahead of the stop line that allows the right-turn vehicles at signalised junctions under the permissive filtering signal operation to proceed into the junction-box at the onset of full green signal phase. The RWA layout gives guidance to vehicle placement of turning vehicles which improves safety and mitigates vehicle queue overflow of the right-turn vehicles. RWA enhances the capacity of right-turn lanes while alleviating conflict severity in some cases. This study analysed the safety impact of the conflict between opposing straight-through vehicles and right-turn vehicles at RWA junctions in Singapore. A microscopic simulation model based on Fuzzy Cellular Automata (FCA) was developed. Field surveys were carried out to obtain the inputs for calibrating the fuzzy inference system. Different scenarios were analysed and discussed including different types of junctions with and without RWAs. The proposed model is found to be able to simulate decision-making of individual drivers at RWA or before stop line, such as exiting the queueing area or crossing the stop line when faced with different gaps and velocity of opposing straight-through vehicles. [ABSTRACT FROM AUTHOR]