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To solve some scheduling problems of batch processes based on timed Petri net models, timed extended reachability graphs (TERGs) and approximated TERGs can be used. Such graphs abstract temporal specifications and represent parts of timed languages. By exploring the feasible trajectories in a TERG, optimal schedules can be obtained with respect to the makespans of batch processes that are modeled by timed Petri nets. Nevertheless, the rapid growth of the number of states in a TERG makes the problem intractable for large systems. In this paper, we improve the existing clustering TERG approach, and we make it suitable for large sized batch processes. We also enlarge a systematic approach to model batch processes with timed Petri nets. Finally, a comprehensive example of scheduling problem is studied for an archetypal chemical production plant in order to illustrate the efficiency of the proposed approach. • The scheduling issue of batch chemical processes is formalized with timed Petri nets. • A graph named ClusTERG approximates the state space through hierarchical clustering. • The approximation error of this ClusTERG is far less than other approximations. • Such an approach is used to obtain near-optimal schedules of a batch process. [ABSTRACT FROM AUTHOR]

Timely fault diagnosis and localization of transmission lines is crucial for ensuring the reliable operation of increasingly complex power systems. This study proposes an optimized R-Net algorithm based on a feature pyramid network (FPN) and densely connected convolutional network (D-Net) for transmission line fault diagnosis and localization. The R-Net network is enhanced by reshaping the anchor points using an improved K-means algorithm and incorporating an FPN for multi-scale feature extraction. The backbone network is further optimized using D-Net to strengthen inter-layer connections and improve feature reuse. Experimental results demonstrate that the optimized R-Net achieves an overall average accuracy of 0.64, outperforming the original network by 1.30%. The accuracy improvement is particularly significant for ground wire defects (2.40%). The D-Net-based R-Net, despite having fewer parameters, maintains high accuracy (0.6502). Compared to other object detection algorithms, such as YOLO-v3 and Faster R-CNN, the optimized R-Net exhibits superior performance in terms of mean average precision (15.58% and 2.45% higher, respectively) and parameter efficiency (17M vs. 38M and 81M). Considering both performance and speed, the optimized R-Net achieves a processing rate of 10.5 frames per second. This study provides an efficient and accurate tool for transmission line fault diagnosis and localization, with significant practical implications for power system operation and maintenance. [ABSTRACT FROM AUTHOR]

Marine intelligent net tank aquaculture monitoring plays an important role in improving aquaculture efficiency, environmental monitoring efficiency, and environmental safety. The underwater environment has complex light, often with problems such as scattering and absorption, resulting in poor image quality, making it difficult to accurately analyze and judge the aquaculture environment. Improving marine intelligent net tank aquaculture monitoring has the following three advantages: 1) better observation and monitoring of the aquaculture process, timely detection of problems and abnormalities, to protect the benefits of aquaculture and product quality. 2) more convenient and rapid monitoring of the aquaculture environment, improving monitoring efficiency and reducing monitoring costs. 3) effective monitoring of the underwater environment around the farm, and timely detection of foreign pollution, harmful substances, and other problems, to protect the safety of the aquaculture environment. Therefore, in order to solve the two degradation problems of scattering and absorption in the process of marine smart net farm monitoring, we propose a marine smart net farm monitoring method using multiple scattering models and multiple spatial transformations, called MINM. Specifically, inspired by the image chromatic aberration correction method, we design a color correction method in the multicolor space, which is implemented by using the Lab and RGB color space by performing contrast-constrained adaptive histogram equalization and gray world assumptions, respectively, to correct color shifts in different color models. Based on this, we propose a de-scattering method using a multi-scattering model, which eliminates the effect of scattering on underwater imaging by embedding a complete multi-scattering underwater imaging model to guide the extraction of different features in the multi-scattering model. To obtain more qualified results, we also propose an efficient perceptual fusion to mix the output of the de-scattering and color correction. Thus, our method can take advantage of multiple scattering models and multiple spatial transformations to effectively improve the visual quality of underwater images, producing enhanced results that fit the complete underwater imaging model and have bio-visual characteristics. In extensive experimental demonstrations, our MINM method has shown higher performance than the state-of-the-art methods in terms of both visual quality and quantitative metrics. All experimental results and datasets in this paper are available from the following website: https://github.com/An-Shunmin/MINM. [ABSTRACT FROM AUTHOR]

Fluidization is a crucial technique for converting stochastic Petri nets (SPNs) into continuous Petri nets (CPNs) to overcome the challenge of combinatorial state explosion, which results in prolonged computational time for steady-state probability estimates. The ultimate aim is to obtain a precise continuous model that captures the behavior of the SPN. However, fluidization's continuous behavior is distinct from typical stochastic systems. In this study, we conducted an in-depth analysis of fluidization in single and multiple regions. We considered different approaches, including piecewise linear and adaptive techniques, and added sufficient conditions to both approaches to achieve superior convergence between the two models. Our results are interpreted and compared. The adaptive approach, which utilizes a nonlinear adaptive law to minimize errors caused by average throughput and average marking variations, achieved excellent convergence compared to the piecewise linear approach, which involves several steps, such as subdividing marking trajectories into multiple phases and intermediate points. Overall, this study highlights the effectiveness of the adaptive approach in enhancing convergence in CPNs. [ABSTRACT FROM AUTHOR]

Due to the cultural and traditional diversity of the Indian environment, the successful implementation of self-automated and smart architectures is very challenging. As the IoT expands the network of devices, it also increases the hazards with increasing demands. This paper aims to propose an efficient model with the combination of interdisciplinary aspects of Indian building automation and numerous factors of Indian cultural differences. The proposed fuzzy-colored Petri-nets-based ubiquitous framework utilizes the capabilities of ubiquity for the diversified culture of Indian Building Automation systems. The proposed algorithms establish the Wi-Fi component connectivity and the working of the LED pin. Further, the modules of the Wi-Fi component connectivity, set up function and get values function to illustrate the working principle of the proposed model. Lastly, the integrated system using the FCPN tools. The significance of developing the proposed model is in its usefulness, usage of maximum ideas of different modeling techniques, cultural support, and its conversion into a formal qualitative and quantitative model. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0 and beyond, intelligent and reliable models are vital for processes and assets. Models in smart manufacturing involve combining knowledge-based and data-driven methods with discrete and continuous modelling components. Formalism choice determines models' strengths and weaknesses in accuracy, efficiency, robustness, and explainability. Hybrid models seem to be the only way to address the complexity of modern industrial systems with respect to different and conflicting quality criteria. This study focuses on three paradigms: Petri nets, cellular automata, and neural network driven deep learning. We create four hybrids: Petri nets controlling deep neural networks, and vice versa; cellular automata controlling deep neural networks, and vice versa. These hybrids combine explainable discrete models with continuous black-box models, enhancing either explainability with robustness or elevating accuracy with efficiency. The flexibility of these and similar hybrids enable enhancement of the scope and quality of modeling and simulation in smart manufacturing. [ABSTRACT FROM AUTHOR]

Process mining has received much attention in the field of business process management. Event logs that are generated from information systems can be correlated with the process models for conformance checking. The process models describe event activities at an abstraction level. However, hierarchical business processes, as a kind of typical complex process scenario, describe sub-processes invocation and multi-instantiation patterns. As existing conformance, checking approaches cannot identify sub-processes within hierarchical process models, they cannot be used for conformance checking of hierarchical process models. To handle this limitation, a definition of hierarchically alignment sequences is presented in this paper. Meanwhile, a novel conformance checking approach for hierarchical process models and event logs is proposed. The proposed method has been implemented within the ProM toolkit, which is an open-source process mining software. To evaluate the effectiveness of the proposed approach, both artificial and real-world event logs are utilized in a comparative analysis against existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The development of structures that can adapt spontaneously to achieve desired functions in complex environments is crucial for new unmanned countermeasures, such as prey capture or net-recovery. Conventional structural optimization methods based on a singular net–like configuration may lead to functional limitations and fail to achieve specific objectives. In this study, we utilized an evolutionary algorithm that incorporated mechanical features and biological corrections to construct spider threads with advanced properties capable of efficient and reliable trapping behavior in arbitrary boundary conditions. We employed distinct thread types in different components, which achieved distinguished stiffness and strength that could not be accomplished by a single kind of thread. By assembling prestress reinforcement threads, we developed an orb-web-like trap that demonstrated effective trapping performance in experiments. The adaptive evolutionary method could be applied to design intelligent intercepting devices suited to particular functions and extreme environments, with wide application prospects in net-recovery system of UAV. Structures that adapt spontaneously to perform desired functions in difficult environments are crucial for rising unmanned countermeasures. Conventional structural optimization methods based on a singular net–like configuration may lead to functional limitations and fail to achieve specific objectives. We used an evolutionary algorithm that combined mechanical features and biological corrections to create spider threads in arbitrary boundary circumstances in this work. The adaptive evolutionary method could be applied to design intelligent intercepting devices suited to particular functions and extreme environments, with wide application prospects in net-recovery system of UAV. [Display omitted] [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate a unit namely Condensate Structure of coal-fired thermal power plant. The data collected may not be exact. There may always be an imprecision due to human errors. To extract the useful information from the records, data have been tackled by tolerance in context of fuzzy logics. To handle the uncertainty in the data, two kinds of tolerances have been used. Fuzzification of data have been done using unilateral tolerance with the membership function as Right triangular generalized fuzzy numbers (RTrGFN) and bilateral tolerance with the membership function as generalized trapezoidal fuzzy numbers (GTFN). Using lambda-tau expressions, failure rate, and repair time for the entire structure has been calculated and all reliability parameters of Condensate structure have been calculated as well. The defuzzified values are also obtained for application purpose. This paper aimed at finding the performance of the structure and best possible way to increase the reliability of the structure. The results have been shown with the help of graphs and tables. Then, ranking has been used to identify the critical component of the system. Comparative study of the considered structure has been made between the results obtained by two different methodologies. [ABSTRACT FROM AUTHOR]

The process of miner selection in blockchain networks plays a significant role in maintaining the efficiency and security of the net- work. In blockchain, the consensus algorithm is crucial for miner selection. Considering the downsides of the consensus algorithm (PoW, PoS) this paper employs proof of artificial intelligence (PoAI) over proof of stake and proof of work. This paper proposes an approach for miner selection using the Proof of Artificial Intelligence (PoAI) framework, which leverages Deep Neural Networks (DNNs) to predict efficient miners for blockchain networks. This paper describes the design and implementation of our PoAI that uses DNN algorithm on a bitcoin dataset, is a combination of supervised and unsupervised learning techniques to model the be- havior of miners using suitable features such as age, number of blocks mined, transaction volume and number of transactions. The proposed methodology improves the scalability of the consensus algorithm. [ABSTRACT FROM AUTHOR]

Model checking is a software automation verification technique. However, the complex execution process of concurrent software systems and the exhaustive search of state space make the model-checking technique limited by the state-explosion problem in real applications. Due to the uncertain input information (called system parameterization) in concurrent software systems, the state-explosion problem in model checking is exacerbated. To address the problem that reachability graphs of Petri net are difficult to construct and cannot be explored exhaustively due to system parameterization, this paper introduces parameterized variables into the program dependence net (a concurrent program model). Then, it proposes a parameterized reachability graph generation algorithm, including decision algorithms for verifying the properties. We implement LTL-X verification based on parameterized reachability graphs and solve the problem of difficulty constructing reachability graphs caused by uncertain inputs. [ABSTRACT FROM AUTHOR]

On-board subsystem is a key of guaranteeing traffic security and enhancing operational efficiency for highspeed railway (HSR). Thus, the test of the subsystem is the main means of ensuring both the functionality and reliability of the train control system. To deal with the low efficiency of existing manual mode, an automated test data generation method combining back propagation neural network (BPNN) and multi-population genetic algorithm (MPGA) is presented. First, colored petri net (CPN) models are designed according to the relevant requirements specifications of the on-board subsystem. The feedback information of each model is taken as the fitness function. Second, the BPNN method is adopted to simulate the solving process of individual fitness value, which is to reduce the running cost of the designed model. Third, to overcome the shortcomings of genetic algorithm (GA), the MPGA is used to search for test data in the input space. Finally, we take the startup procedure of the on-board subsystem as a case study. The results reveal that the novel method has the advantages of short running time, higher efficiency, and better stability compared with the traditional GA. [ABSTRACT FROM AUTHOR]

The issue of modeling holds significant importance for the applicability of control theory, particularly at higher control levels where the verbal description of control objects becomes increasingly crucial. However, a notable disadvantage of such verbal descriptions is the inherent ambiguity and vagueness of the obtained results. This article aims to address this challenge by proposing an approach that utilizes Petri nets (PN) to model and control a specific component within an intelligent home. Additionally, the Simulink modeling tool and Petri net analysis tools are introduced as aids in the modeling process. The article focuses on creating a model that accurately represents the temperature regulation system in the home, considering the constant rise in energy consumption and costs. Furthermore, the transfer of the model to the Simulink environment using IF THEN rules is demonstrated, allowing for an evaluation of its performance. Through the comprehensive application of this approach, the aim is to optimize energy usage and reduce costs within household environments. [ABSTRACT FROM AUTHOR]

Human-Machine Interfaces (HMIs) are getting more and more important in a hyper-connected society. Traditional HMIs are built considering cognitive features while emotional ones are often neglected, bringing sometimes such interfaces to misuse. As a part of a long run research, oriented to the definition of an HMI engineering approach, this paper concretely proposes a method to build an emotional-aware explicit model of the user starting from the behaviour of the human with a virtual agent. The paper also proposes an instance of this model inference process in voice assistants in an automatic depression context, which can constitute the core phase to realize a Human Digital Twin of a patient. The case study generated a model composed of Fluid Stochastic Petri Net sub-models, achieved after the data analysis by a Support Vector Machine. [ABSTRACT FROM AUTHOR]

In this paper, we analyze selected structural and dynamic properties of uninorm Petri nets in terms of their use in modeling knowledge-based systems operating in an uncertain environment. The uninorm Petri nets use uninorms to describe their behavior. Uninorms are a special type of aggregation functions that generalize both t-norms and s-norms. This generalization is that in the case of a uninorm, the neutral element can take any value in the unit interval, in contrast to triangular norms, for which the neutral element is equal to 1 or 0 depending on whether the triangular norm is a t-norm or an s-norm. Thanks to this, by manipulating the neutral element in the unit interval, it is possible to better match the behavior of the used net to the requirements of the modeled system. When modeling systems using uninorm Petri nets, as well as nets using triangular norms, the following questions are important for the designer of such models: (1) What properties of the net affect the uniqueness of the model's decisions? (2) How to choose uninorms assigned to net transitions so that the behavior of the net model is deterministic? This work provides answers to these two questions above. The subject of this paper is a continuation of the author's earlier research on generalized fuzzy Petri nets based on triangular norms. This also falls within the range of challenges outlined in a review article on the state of research on the theory and applications of fuzzy Petri nets, developed by Zhou & Zain authors and published in Artificial Intelligence Review, 45, 405–446, 2016. [ABSTRACT FROM AUTHOR]

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering. [ABSTRACT FROM AUTHOR]

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

This paper presents a novel framework to assist organizations with coping with a disruptive technology such as industry 4.0. This coping requires assessing and adjusting existing practices at both strategy and operation levels. The proposed framework features 4 components referred to as, goals and beliefs, associated with the strategy level, and, tasks and resources, associated with the operation level. The components are related to each other tracking who is doing what, using what, and working with whom. Each component is represented as a cycle along with the disruptions that could hinder the cycle's progress. The framework's technical doability is also discussed using a set of Petri Nets associated with its components. Although these Petri Nets are separate, they are interleaved when disruptions cross the strategy level to the operation level and vice-versa. [ABSTRACT FROM AUTHOR]

In the last decade Internet of Things (IoT) emerged as a concept for intelligent, flexible, autonomous, and distributed system while IoT-based devices have highlighted for agent communication, data analysis, and decision-making tasks. Integrating high-level behavioral and low-level control has capital significance for their implementation and is still a challenge. In the field of high-level behavior, Petri nets are widely applied for modeling, simulating, and analyzing concurrent, discrete-event and knowledge-based systems, furthermore even models are used as basis for control algorithm, direct controlling devices' behavioral with high-level Petri nets is little diffused. This paper presents a control architecture used to integrate models in high-level Petri nets with IoT-based devices. Operational tests were provided using CPN Tools and micro-controlled physical devices. A study with a mobile robot is shown and results indicate the reasonability and applicability of this proposed approach in IoT-based devices. [ABSTRACT FROM AUTHOR]

The paper proposes multi-operation blank localization to fit final product geometries into near net shape blanks. Groups of machining features are located subject to tolerance intervals on their relative positions and a lower bound on the machining allowance which accommodates for uncertainties of measurement and machining. The tolerance error, i.e., the deviation of the resulting dimensions from the center of the tolerance intervals is minimized. The blank localization problem is formulated as a convex quadratically constrained quadratic program that can be solved efficiently for parts with real-life complexity, as demonstrated by a case study from the automotive industry. [ABSTRACT FROM AUTHOR]

SQL injection is becoming more frequent and is urgent to be studied the defense mechanism. In this paper, a fine-grained Petri Net (PN) model is proposed to describe the process of SQL Boolean injection and SQL time blind injection. The results show that the speed of PN model simulating SQL injection can be scaled. such as 10 times the speed. It is conducive to faster-than-real-time simulation and prediction of unknown vulnerabilities when the injection speed is fast. It is good for fine-grained analysis of the attack and establish a patch model when the injection speed is slow. The modeling and simulation in this paper provide a theoretical guarantee for the occurrence mechanism of vulnerabilities and the discovery of unknown vulnerabilities. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to verify the functional correctness of a specific process mining algorithm and to validate the requirement satisfaction of the targeted algorithm, as well. The functional requirement of the process mining algorithm is to discover all the structural process patterns, such as linear (sequential), disjunctive (selective-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP) process patterns, from a dataset of process enactment event logs, and to eventually build a structured business process model by assembling all the discovered structural process patterns. The targeted algorithm to be verified and validated in this paper is called as ρ - Algorithm that is especially devised for discovering a structured information control net process model (SICN-oriented process model) from a dataset especially prepared in the standardized IEEE XES event stream format. In order to carry out the verification and validation, we have successfully developed a process mining system based upon the ρ - Algorithm , and through which four rounds of experiments are taken with four real datasets that are well fitted into the following situational mining and discovering scenarios: Faultlessness Process, Matched-Pairing Violation but Sound Process, Matched-Pairing and Proper-Nesting Violation but Sound Process, and Nebulous Process (Adaptive Cases). Conclusively, we strongly believe that the ρ - Algorithm is theoretically safe as well as functionally operable, and that the implemented ρ - Algorithm is also practically applicable to a process mining system as one of the business process and workflow intelligence solutions. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to verify the functional correctness of a specific process mining algorithm and to validate the requirement satisfaction of the targeted algorithm, as well. The functional requirement of the process mining algorithm is to discover all the structural process patterns, such as linear (sequential), disjunctive (selective-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP) process patterns, from a dataset of process enactment event logs, and to eventually build a structured business process model by assembling all the discovered structural process patterns. The targeted algorithm to be verified and validated in this paper is called as ρ - Algorithm that is especially devised for discovering a structured information control net process model (SICN-oriented process model) from a dataset especially prepared in the standardized IEEE XES event stream format. In order to carry out the verification and validation, we have successfully developed a process mining system based upon the ρ - Algorithm , and through which four rounds of experiments are taken with four real datasets that are well fitted into the following situational mining and discovering scenarios: Faultlessness Process, Matched-Pairing Violation but Sound Process, Matched-Pairing and Proper-Nesting Violation but Sound Process, and Nebulous Process (Adaptive Cases). Conclusively, we strongly believe that the ρ - Algorithm is theoretically safe as well as functionally operable, and that the implemented ρ - Algorithm is also practically applicable to a process mining system as one of the business process and workflow intelligence solutions. [ABSTRACT FROM AUTHOR]

A lack of curvature reduces the out-of-plane stiffness of cable net façades. Therefore, they may experience large deformations and severe damage under heavy vertical loads such as earthquakes and wind. Axially movable smart supports are proposed for controlling structural vibrations by controlling tension of the cables. The equation of motion of the cable net, controlled by the smart support, is developed following a finite element method based program to solve the equation. The linear quadratic regulator control strategy is utilized to find the optimal control gain. The results show that smart supports, especially when connected to the central cables, can significantly control the vibrational amplitude of the structure. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new model named linguistic Z-number Petri nets for knowledge acquisition and representation in the large group environment. First, the linguistic Z-number production rules are introduced for knowledge representation, where truth degrees, threshold values, and certainty factors are described by linguistic Z-numbers. Subsequently, a knowledge acquisition approach is put forward to obtain the knowledge parameters of linguistic Z-number Petri nets based on a large group of experts. To reduce the complexity of knowledge reasoning, a simplification method is proposed to simplify the structure of linguistic Z-number Petri nets. Finally, a real case of chemical security risk assessment is provided to demonstrate the practicability and effectiveness of the proposed linguistic Z-number Petri net model. The results show that risk level of the given chemical plant is high and efficient actions should be taken to identify threat drivers and reduce security risk. Moreover, through a sensitivity analysis and a comparative analysis, it is concluded that the proposed linguistic Z-number Petri nets can represent experts' complex and uncertain cognitive information comprehensively and acquire more precise and reasonable knowledge from domain experts. [ABSTRACT FROM AUTHOR]

As an important modeling tool for knowledge representation and reasoning (KRR), fuzzy Petri nets (FPNs) have been applied in kinds of fields. However, there exist some deficiencies for existing FPNs, resulting in application limitations. (1) The sum of membership and non-membership is less than 1, which is hard to model various types of uncertain knowledge by existing FPNs; (2) The reasonable and nonidentical experiential knowledge for electrolysis operation among domain experts should be fused; (3) The existing knowledge reasoning algorithm min, max, and product operators may not work well in many practical applications. In an effort to overcome the shortcomings of existing FPNs, a new type of FPNs is proposed, called simplified neutrosophic Petri nets (SNPNs). First, the simplified neutrosophic sets (SNSs) are introduced into SNPNs, characterized by three independent degrees of truth-membership, indeterminacy-membership and falsity-membership, to depict the experiential cognition of domain experts. Second, the extended TOPSIS (ETOPSIS) is proposed for knowledge fusion. Third, hybrid averaging SNNs concurrent reasoning algorithm (HASCRA) is also proposed to improve the knowledge reasoning efficiency. Finally, a novel model for the identification of superheat degree of aluminum electrolysis cell (ISDAEC) is proposed based on SNPNs. Comparative experimental results show that the SNPNs provide a feasible and practical decision-making method. Moreover, this fact denotes that the proposed method has potential applications in intelligent ISDAEC. [ABSTRACT FROM AUTHOR]

Around the world, more and more attention has been paid to product traceability, and many different traceability models have been established. In response to the existing traceability models with low universality, poor scalability, and incomplete traceability, this paper analyzes and integrates processes in the supply chain, simplifying it by solving unclear hierarchy and high complexity problems. Then three essential process model elements are abstracted using Petri nets, which can build a traceability model applicable to most products and processes. In order to verify the availability of the model, an example of the Petri net traceability model is constructed. The incidence matrix analysis method is used to analyze the characteristics of the model, and the Petri net modeling and simulation tool PIPE is used to verify the properties and simulation. The results of the two methods prove that the model is practical and feasible. [ABSTRACT FROM AUTHOR]

In the field of scene segmentation, the precise segmentation of object boundaries in sports movement scene images is a great challenge. The geometric information and spatial information of the image are very important, but in many models, they are usually easy to be lost, which has a big influence on the performance of the model. To alleviate this problem, a parallel dense dilated convolution merging Network (termed PDDCM-Net) was proposed. The proposed PDDCMNet consists of a feature extractor, parallel dilated convolutions, and dense dilated convolutions merged with different dilation rates. We utilize different combinations of dilated convolutions that expand the receptive field of the model with fewer parameters than other advanced methods. Importantly, PDDCM-Net fuses both low-level and high-level information, in effect alleviating the problem of accurately segmenting the edge of the object and positioning the object position accurately. Experimental results validate that the proposed PDDCM-Net achieves a great improvement compared to several representative models on the COCO-Stuff data set. [ABSTRACT FROM AUTHOR]

In this work, we propose a new algorithm for the verification of codiagnosability of a discrete event system with dynamic observations, which is an extension of a recently proposed algorithm for the verification of codiagnosability of discrete event systems with static observations. The proposed verifier, called E-VERIFIER, is compared with two different verifiers presented in the literature: (i) the T-VERIFIER, which is proposed for the verification of transition-based codiagnosability; and (ii) the C-VERIFIER, which employs cluster automata as the basic element for computation instead of the original states of the automaton system model. We show that although the E-VERIFIER has the same computational complexity as the C-VERIFIER and the T-VERIFIER, it has the following advantages over the other methods: (i) since the E-VERIFIER represents all faulty and fault-free traces of the system that have the same dynamic projection, then, unlike the C-VERIFIER, the traces that lead the system to violating the codiagnosability condition can be straightforwardly obtained from the E-VERIFIER, and the delay for decentralized diagnosis can be computed using only the E-VERIFIER; and (ii) the E-VERIFIER can be computed more efficiently than the T-VERIFIER when its number of states grows. As a consequence of the proposed approach, we present a method for the computation of the delay for decentralized diagnosis of discrete event systems with dynamic observations, adapting an algorithm based on the verifier for the case of static observations. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm capable of modelling shadows from nearby obstructions onto photovoltaic arrays is proposed. The algorithm developed is based on the calculation of the solar position in the sky for any given instant in order to obtain the shadow projection for any object point. The computation is based on considering the shadows as convex regions and on a rasterization process to evaluate the shadowed area of the array. The idea is extended to provide the shading patterns for a desired range of time and to calculate the efficiency rate of the irradiation power incident on the array in comparison with the non-shadowed case. The algorithm has interesting applications, such as optimizing array positioning and orientation, evaluating the impact of new obstructions on pre-existing array installations, allowing precise and practical data for control strategies and MPPT techniques for partially shaded systems, calculating more realistically constrained payback scenarios and finding the optimal PV array interconnection. The results are illustrated by three numerical examples, in which the effects of a nearby building in the irradiation received by a photovoltaic array throughout the year, panel relocation and different interconnections are analysed. [ABSTRACT FROM AUTHOR]

[Display omitted] • The goal of this contribution is to reduce the number of migrated instances during business process change without affecting the number of instances that satisfy the new requirements. To achive this goal: • We propose the use of the Petri Nets (PNs) formalism and Graph Rewriting Rules (GRRs) to model BPs and their changes, in which a PN is used to model the current BP, and GRRs are used to model the BP change and the instance migration. • Our lazy strategy considers two consistency criteria to ignore the migration of instances that satisfy the new requirements. • We implement our approach in the framework, Flexible (Bouhamed et al., 2021). • We illustrate our approach by means of a running example modeling a real-world scenario where two BP changes are introduced. In addition, we compare our approach with a brute-force approach not only in this example but using five other BPs proposed in the literature as well. Business processes (BPs) are subject to frequent and unpredictable changes caused by new laws or modifications in the business environment, including user requirements. Many running instances are migrated to new BPs during a BP change, even when these instances satisfy the new requirements. We propose a migration strategy to ignore the migration of these instances. We specify the BP using Petri Nets (PNs), and the change including the instance migration using Graph Rewriting Rules (GRRs). We propose two consistency criteria based on the instance execution history and its possible future executions to identify the instances supporting the change. The first criterion ignores the migration of the instances satisfying the new requirements. The second criterion postpones the migration of instances that support the migration until they definitely cannot satisfy the new requirements, where some instances might avoid the states where the migration is mandatory. The proposed approach is implemented through the framework called Flexible and illustrated using two cases of BP changes. Simulation results shows that a large number of instances can be ignored during the migration, 86.34% and 42.35% respectively. The approach is evaluated using five real-life scenarios, where a positive correlation with respect to the complexity is observed. [ABSTRACT FROM AUTHOR]

The article considers the appliance possibility of mathematical apparatus of artificial neural networks for controlling processes of intellectual synthesis of large discrete systems models with a specified behavior. The analysis of the influence of different methods and its classification has carried out, which allows defining the moving trajectory of population in a space of decisions: diffusion to search new extrema and compact study the field in order to increase the speed of population convergence. To solve the problem, using the neural network approach, the two graphical presentations of population conditions have been done: the first, the value of fitness functions of each individual and, the second, some individuals with the same value of adaptation functions. The article defines the structure of the neural network in the form of the network without feed-back and number of neurons in the hidden layer. The training of neural networks has been done and the examples of management decision making based on graphical presentation of the population condition have been provided. The unification of evolutionary procedures by one mathematical apparatus of the Petri net was proposed, which allows using the technology of parallel calculation under programmed realization. Using parallel calculation, combined with proposed models and methods should increase the processing speed of decision-making support systems based on genetic algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a deadlock prevention policy for flexible manufacturing systems (FMSs) based on a think-globally-act-locally approach and a structurally minimal method. First, by using the think-globally-act-locally approach, a global idle place is temporarily added to a Petri net model with deadlocks. Then, at each iteration, an integer linear programming problem is formulated to design a minimal number of maximally permissive control places. Therefore, a supervisor with a low structural complexity is obtained since the number of control places is greatly compressed. Finally, by adding the designed supervisor, the resulting net model is optimally or near-optimally controlled. Three examples from the literature are used to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Process mining is a new field of work that aims to meet the need of the business world to improve efficiency and productivity. This field focuses on analysing, discovering, managing, and improving business processes. Process mining uses event logs as a resource and works on this resource. Hence, the system is developed by analysing the event logs, including each step in the process model. Our study is made up of two significant stages: a data generator for processes and algorithms applied for discovering the created processes. In the first stage, the aim was to develop a simulator with the ability to generate data that could help process modelling and development. Within the framework of this study, a system was created that could work with various process models and extract meaningful information from these models. More productive and efficient processes can be developed as a result of his system. The simulator consists of three modules. The first module is the part where users create a process model. In this module, the user can create his own business process model in the system’s interface or select from other registered models. In the second module, team-based data are simulated through these process models. These generated data are used in the third module, called “analysis”, and meaningful information is extracted. In conclusion, the process can be improved considering the information about time, resource, and cost in the generated data. At the second stage, processes were discovered using alpha, heuristic, and genetic algorithms, which are process mining discovery algorithms and synthetic and real event logs. The discovered processes were demonstrated with Petri nets, and the algorithms’ performances were compared using the fitness function, accuracy rates, and running times. In our study, the heuristic algorithm is more successful because it improves the noise in the data and incomplete processes, which are the disadvantages of the alpha algorithm. However, the genetic algorithm yielded more successful results than the alpha and heuristic algorithms due to its genetic operators. [ABSTRACT FROM AUTHOR]

Fault diagnosis problem is discussed for discrete events systems described by partially observed Petri nets. Our goal is to detect and identify faults that may have occurred in both transitions and places assuming that we can measure some of them. The proposed approach is based on two mains steps (i) designing an algebraic observer for estimating the markings and the transitions of a partially observed Petri nets and (ii) presenting algorithms to detect and identify faults based on comparing the estimation of both the transitions and markings of the faulty system provided by an algebraic observer with those of the normal system. The effectiveness of the proposed approach is illustrated through a simple and a manufacturing example. • Fault diagnosis model-based approach for discrete events systems modeled by Petri nets is proposed. • An algebraic observer for partially observed Petri nets is designed. • Algorithms capable to detect and identify faults for partially observed Petri nets are presented. • Application to a manufacturing systems. [ABSTRACT FROM AUTHOR]

A Distributed Cyber-Physical System (DCPS) composition poses challenges in determining its emergent behaviour. These challenges occur due to (1) the appearance of causal loops of information and energy flow through cyber and physical channels and (2) inherent non-determinism in the temporally ordered flow of events within independently evolving interacting processes of Constituent Systems (CSs). Hence, there is a need to construct a model of the envisaged schematic of DCPS composition for analysis and verification of its significant properties in the conceptual design stage of the system development life cycle. This paper presents a procedure to construct DCPS composition models in Petri net formalism using distributed abstractions. The model for each CS is obtained from elementary constructs using compositional operators. The interaction among CSs occurs through channels obtained by connecting send and receive constructs of two CSs participating in an interaction. The internal processing within a CS characterizing its primary function is abstracted in a generic passthrough construct. Representing these constructs with compositional operators results in the complete DCPS model in Petri net formalism. A toolchain with Reference net workshop (Renew) as an integrated Petri net editing and analysis platform is configured to support DCPS modelling, simulation and analysis. The Renew tool functionality has been enhanced with a plugin designed and developed by authors to facilitate the drawing of the distributed composition model. A low-level Petri net analysis (Lola) v2.0 plugin is employed to verify the Petri net and temporal properties of the modelled DCPS scenarios. The properties of the resultant model are verified using well-established algorithms to analyze Petri nets. Further, system properties specified using temporal logic can be verified using model-checking algorithms for Petri nets. A moderately complex scenario involving interactions among six CSs illustrates the presented approach. [ABSTRACT FROM AUTHOR]

Net energy metering (NEM) systems have been promoting the growth of photovoltaic (PV) distributed generation worldwide. However, the economic and financial sustainability of this policy has been questioned due to the possibility of triggering the utility death spiral. In this context, this study presents a method to quantify the economic impact of NEM policies considering utilities and non-adopter consumers' points of view. The proposed method employs a cost-benefit risk analysis to obtain the present value of accumulated utility cash flows. Electricity tariff, PV system unit price, and PV systems average installed power varying based on the Monte Carlo method are considered in the computational simulations. The Bass diffusion model is utilized to forecast the number of PV adopters. A univariate sensitivity analysis is carried out to determine how the key parameters affect the economic analysis. The method was applied in a case study that contemplates 98% of the Brazilian distribution market, considering three different scenarios of PV adoption. The results showed that, if the current NEM policy continues, the payback of micro- and mini-PV systems will decrease significantly over the years, culminating in a high average present value of the accumulated cash flow. This method allows researchers and policymakers worldwide to reassess their NEM regulations to stimulate the sustainable development of PV systems and to avoid cross-subsidies between prosumers and other consumers. • Net metering costs in Brazil are identified using risk analysis. • Number of prosumers is forecasted for three scenarios employing the Bass method. • Net metering resulted in financial losses for utilities in the analyzed scenarios. • Self-consumption rate is the main parameter in determining net metering impact. [ABSTRACT FROM AUTHOR]

A method of repairing process models with non-free-choice constructs is proposed based on logical Petri nets, aiming at the problem of low precision in the existing repair methods. An extended successor matrix of transitions is determined according to the distance between any two transitions. There are two types of choice-construct transitions. One is a non-free-choice construct transition, and the other is a general choice construct transition. The type of choice-construct transitions can be determined based on the extended successor matrix and the relationship between the front and back sets of transitions. The location of the deviations is calculated by an improved replaying method. Finally, a model can be repaired according to remaining-token places and missing-token places. Based on the experiments on real event logs, the method proposed in this paper has a better performance in fitness, precision, and simplicity compared with its peers. [ABSTRACT FROM AUTHOR]

In order to model complex workflow systems with databases, and detect their data-flow errors such as data inconsistency, we defined Workflow Net with Table model (WFT-net) in our previous work. We used a Petri net to describe control flows and data flows of a workflow system, and labeled some abstract table operation statements on transitions so as to simulate database operations. Meanwhile, we proposed a data refinement method to construct the state reachability graph of WFT-nets, and used it to verify some properties. However, this data refinement method has a defect, i.e., it does not consider the constraint relation between guard functions, and its state reachability graph possibly has some pseudo states. In order to overcome these problems, we propose a new data refinement method that considers some constraint relations, which can guarantee the correctness of our state reachability graph. What is more, we develop the related algorithms and tool. We also illustrate the usefulness and effectiveness of our method through some examples. [ABSTRACT FROM AUTHOR]

In order to improve the prediction precision of film score, the improved whale algorithm is established to predict the IMDB film score. Firstly, the IMDB scoring mechanism is studied, the reason of inaccurate scoring or network "water army" is analyzed, and the score of the meta score, the number of reviews and the popularity index are discussed. Data of the film in system is updated in real time. Secondly, the improved whale optimization algorithm is constructed, the mathematical model of encircling prey, bubble net attacking and search for prey are discussed. The adaptive weight formula is established and the improved adaptive updating formula is constructed, and the corresponding algorithm procedure is designed. Finally, through the analysis of visual results, the key factors affecting film scoring are found out, and suggestions are given for the film recommendation system. One hundred films are selected to carry out prediction analysis, the top 85% films are used as training samples, and the bottom 15% films are used as testing samples. The prediction mode is trained through training samples, and the testing samples are predicted based on trained prediction model. A prediction simulation analysis is carried out using IMDB samples, and results show that the proposed prediction model has high prediction precision. [ABSTRACT FROM AUTHOR]

This work is part of a learning environment that has virtual laboratories that are designed for distant practical work (Tele-PW). In these environments, Tele-PW is performed in two modes: individual and/or collaborative. In this paper, we are concentrating on the tele-collaborative distant practical work model. The work, presented in this paper, proposes an artificial agent called Synchronizer Coordinator Agent (SCA) to synchronize and coordinate the activities of a cognitive process in order to build and maintain a shared conception of a distant practical work between a set of learners. This agent provides certain features such as managing groups of learners, coordinating tasks, shared workspace among members of the Working Group. It is also responsible for the synchronization of workspace agents when they want to manipulate shared virtual objects simultaneously. We have chosen Petri nets to illustrate the principle of granting access to shared objects in the case of simultaneous requests. Experimental results show the effectiveness, of the artificial agent within any tele-collaborative/tele-cooperative learning situation. Several situations describe the geographical/time dispersion of learners and tutors in our system are considered during the system design phase. [ABSTRACT FROM AUTHOR]

Over the years, the concept of Quality of Service (QoS) has evolved from traditional network performance metrics to include Quality of Experience (QoE) considerations. This evolution also encompasses various business-related aspects, such as the impact of service quality on customer satisfaction, the alignment of service offerings with market demands, and the optimization of resource allocation to ensure cost-effectiveness and competitive advantage. This comprehensive approach, considering all the QoS dimensions (QoX), ensures the proper management of QoS across different services, contexts and technologies. Building on this broader QoX framework, it is essential to rely on advanced monitoring tools capable of handling the complexity introduced by these new demands. In this context, this paper describes a Generalized Stochastic Petri Net (GSPN) based model to analyze the performance of a Wi-Fi network probe in terms of computational capacity. The probe node plays a crucial role in a distributed monitoring system designed to implement a machine learning based global QoX management framework. Hence, the model explores the probe's computational resources to handle supplementary machine learning tasks alongside its typical packet capture and data processing responsibilities. Additionally, the model can evaluate the efficiency of the probe node under different scenarios, providing valuable insight into the potential need for additional resources at the node as operational demands continue to evolve. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation is an essential task for medical diagnosis and treatment planning. Multi-modal MRI provides complementary information that is essential for accurate segmentation of brain tumors, but missing modality images are a common problem in clinical practice. Existing segmentation methods often fail to generate accurate object boundaries and selectively fuse the tumor region, resulting in unreliable segmentation masks. In this work, we propose an Edge-aware Discriminative Feature Fusion Based Transformer U-Net (EA-DFFTU-Net) to segment the brain tumor effectively even in the absence of modalities. First, the MRI input data is pre-processed, and features are then extracted using a ResNet-50 encoder, which learns local spatial. We employ an Edge Feature Module (EFM) to acquire edge attention representations. These features are then transferred to the Discriminative Feature Fusion based Transformer U-Net (DFFTU-Net) that learns global contexts and captures multiscale features. We use a Discriminative Feature Fusion Module (DFFM) in the decoder of the DFFTU-Net to effectively fuse the multiscale features in order to obtain accurate segmentation. The performance of the proposed EA-DFFTU-Net segmentation method was determined by evaluating it and comparing the obtained results with those of existing brain tumor segmentation techniques. • Features are extracted using ResNet-50 encoder. • Vision transformer is used to capture global contexts. • EFM is used to acquire edge attention representations. • DFFM is used to effectively fuse the multiscale features. [ABSTRACT FROM AUTHOR]

In modern industrial production, robotic assembly systems play a crucial role. As robots take on more tasks, the need for formal methods arises to define, control, and execute these tasks. This paper introduces a comprehensive approach to designing and generating control code for robotic assembly systems, taking task sequence planning into account. This methodology utilizes Petri nets (PNs) as a formal modeling and synthesis tool for the controller. Initially, the task sequences for assembly operations are represented using PN formalism. Supervisors are then synthesized for task sequence control specifications. Finally, the control code is obtained by the proposed methodology for industrial robots. By implementing this supervisory control structure, real-time control of the robotic assembly system is achieved. Experimental studies were conducted using an assembly cell equipped with an industrial robot. This methodology bridges the gap between the design and implementation of formal controllers for industrial robots. The proposed approach integrates formal methods into robot programming to leverage several advantages, including correctness assurance, complexity handling, improved documentation and clarity, enhanced safety and reliability, property verification, and scalability. [ABSTRACT FROM AUTHOR]

A way to reduce carbon emissions in cities is through movement by bicycle or on foot. However, it sometimes means to pass through high-pollution zones and consequently breath low quality air. We then propose a green Intelligent Transportation System (ITS) for zero-emission mobility users, providing users with low-pollution routes to avoid the high-pollution zones. This proposal uses ITS to promote the use of alternative transportation to classical motor vehicles to reduce carbon emissions. This is based on Complex Event Processing (CEP) technology to gather and process real-time data, a Decision Support System designed as a Fuzzy Inference System (FIS) to make decisions about recommended transit zones, taking also into account the user experience level and specific weather data, and Colored Petri Nets (CPN) as a tool to compute the routes. This is therefore an all-in-one solution to provide green routes, with the benefits of each one of the technologies used. [ABSTRACT FROM AUTHOR]

This paper presents an estimation method for the capacitor and input voltages in Flying Capacitor Multilevel Converters (FCMC). The scheme uses only the output current and voltage measurements. Furthermore, the estimation scheme is represented as a Petri Net, allowing a compact and formal approach to represent the estimation process. This scheme simplifies the needed hardware by using only one voltage sensor, simplifying its associated electronics and communication channels, allowing its real-time implementation in systems with a high number of levels. Simulation and experimental results obtained with a nine-level FCMC current-controlled chopper are presented. The converter control loops use the estimates. Different operating conditions are used to evaluate the system, verifying that the estimation scheme can successfully replace the (n-1) needed voltage sensors with only one. • The estimation scheme can be extended to converters of any number of levels. • The scheme is represented as a Petri Net integrating the hybrid nature of the system. • Scheme simplicity enables its real time implementation with a high number of levels. • The scheme replaces (n-1) voltage sensors with only one. • Reduced computational burden if compared with the other techniques. [ABSTRACT FROM AUTHOR]

This paper proposes an iterative control policy to design a less restrictive and admissible Petri net (PN) controller for a Discrete Event System (DES) with uncontrollable transitions, when a maximally permissive controller is not obtains by. This paper exploits a structural supervisory control method, avoiding reachability graph. This exciting method addresses the controllability condition of the desired functioning PN to define it as General Mutual Exclusion constraints (GMEC), which leads to design place invariant-based controller. The controller is non-admissible if one control place is connected to uncontrollable transition. To obtain an admissible PN controller, if such a controller exists, authors propose constraints transformation, which is computationally complex, while the controller arcs displacement approach is unsystematic. Based on it, we develop the idea to iterate the structural supervisory control method, to ensure that control place is connected to controllable transition. Through this, it was found that the displacement of arcs is systematic and the controller is less restrictive and admissible, namely the supreme controller. The results indicate that the linear constraints are never violated through the firing of uncontrollable transitions. The finding of this work may serve to evaluate the structural optimality of the controller, in order to perform it practically. [ABSTRACT FROM AUTHOR]

Focusing on the stochastic flow manufacturing system (SFMS), an Absorptive Markov Chain-based algorithm is proposed to calculate the network reliability as the performance index. The SFMS is a hybrid flow shop with multiple production lines, and each workstation in the production line contains multiple identical parallel machines. Because of machine failure, human factors, maintenance, and other factors, machines may have multiple capacities and be regarded as random variables. In addition, in practice, to avoid the waste of raw materials and reduce manufacturing costs, rework and scrapping mechanisms are often used in manufacturing systems. Therefore, whether the SFMS is meeting the demand is an important issue, and network reliability is an ideal quantitative indicator. However, most of the algorithms proposed for SFMS in the past are established for specific scenarios and are not easy to be used in complex manufacturing systems. The Absorption Markov Chain model is used to calculate the capacity required by all workstations, and then the minimum capacity vectors are obtained for calculating network reliability. [ABSTRACT FROM AUTHOR]

Traffic flow at urban intersections varies randomly during the day. It depends on several dynamic factors and requires efficient regulation and flexible control strategies in particular for traffic light regulation. The proposed strategy allows managing the green light time autonomously. The dynamic behavior of traffic signals at intersections can be seen as a discrete event system. Through this paper, a modular Timed Synchronized Petri Net (TSPN) model is developed and a real-time adaptive control strategy of urban signalized intersections is proposed. The control is shared between two communicant actors. The master-slaves approach is adopted in this control strategy. The master (controller) decides the next phase to be served with green light and its duration. While, the slaves (TSPN modules) control the traffic signals displays, phases transitions, and model traffic flow fluctuations. Thanks to the used modularity approach, the developed models reduce the system complexity in terms of combinatorial explosion, and they could be adapted easily for any real intersection. Using the developed models, some interesting properties of the system are checked, and some simulations are performed and analyzed in order to validate the proposed control approach. [ABSTRACT FROM AUTHOR]