Search Results (19)

With the advancement of modern agricultural technology and the expansion of large-scale production, this article aims to solve the difficulties in plant disease and pest control through the application of artificial intelligence and automation technology, and provide accurate disease and pest warning mechanisms. This study first conducted a detailed identification and classification of plant disease and pest warning mechanisms, and established a dynamic model of disease and pests based on the environmental factors and symptoms of affected areas. On this basis, using the isomorphism relationship between generalized stochastic Petri nets and Markov chains, a plant disease and pest diagnosis model based on generalized stochastic Petri nets and an equivalent Markov chain model were constructed. The simulation results show that different combinations of infection rates have a significant impact on the probability of meeting treatment standards, with the combination of moderate and severe infection rates having the greatest impact on the probability of meeting treatment standards, while the impact of mild infection rates is relatively small. By comprehensively analyzing the interaction between mild, moderate, and severe infection rates, the critical zone surface under different disease and pest warning thresholds was obtained. Through actual data verification, the generalized stochastic Petri net model can effectively quantify the dynamic characteristics of disease and pest propagation. Combined with the equivalent analysis of Markov chains, it can provide key thresholds and decision support for disease and pest warning. This method provides a theoretical basis for automated monitoring and precise control of pests and diseases in large-scale agricultural planting, and it has high practical application value. Full article

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

The application of queueing theory is very broad. Examples include electronic communication systems and devices. New technologies, electronic communication systems, and devices are used by many modern organizations. However, this implies certain requirements and risks. The requirements are, first and foremost, reliability, which accounts for the complexity and interdependence of the system. On the other hand, the stochastic characteristics and complexity of these systems introduce risks related to the demands of reliability control, transmission quality, availability, and security. The research conducted so far is concerned with relatively simple queueing models that require certain assumptions to be made about the stochastic nature of the event stream. This is because complex queueing systems are very difficult to analyze using analytical methods. Hence, this paper attempts to use timed Petri nets in the modeling and performance evaluation of queueing systems belonging to the PQS (Priority Queueing System) group. IntServ and DiffServ architectures are discussed, as well as queueing systems used in quality-of-service assurance. A weighted PQS that eliminates the possibility of blocking lower-priority traffic is investigated. Based on a Petri model, the performance characteristics of the studied system are obtained. The impact of data generation on the system performance was analyzed, showing that temporal Petri nets can be effectively used in the modeling and performance evaluation of PQS systems. Full article

Predicting system faults is critical to improving productivity, reducing costs, and enforcing safety in industrial processes. Yet, traditional methodologies frequently falter due to the intricate nature of the task. This research presents a novel use of spiking neural networks (SNNs) in anticipating faults in syntactical time series, utilizing the generalized stochastic Petri net (GSPN) model. The inherent ability of SNNs to process both time and space aspects of data positions them as a prime instrument for this endeavor. A comparative evaluation with long short-term memory (LSTM) networks suggests that SNNs offer comparable robustness and performance. Full article

In this paper, we aim to explore the operational performance of a green closed-loop supply chain under random events. A green closed-loop supply chain model based on generalized stochastic Petri nets (GSPN) is built using the Petri nets theory. According to the isomorphic relationship between GSPN and continuous-time Markov chains, the relevant Markov model is converted from GSPN, and the steady-state probability of the model is then calculated. Finally, the model is analyzed from the aspects of time performance and operation efficiency of each link. Compared to previous studies, this paper finds that: when the whole green closed-loop supply chain system reaches a dynamic equilibrium state, the product has a steady-state probability at all stages, and thus the overall operational performance of the system can be obtained; compared with the recycling of waste products, the green product takes a longer time in the production and distribution stages; since marketing, packaging processing, market feedback, and market demand formulation account for a high level of utilization throughout the life cycle of green products, decision makers need to focus on the supervision and management of these links. Managers of green closed-loop supply chain systems need to adjust their decision-making strategies in a timely manner according to the performance level of the system in the steady state to realize the efficient operation of the system. This paper not only provides theoretical support for the improvement of the operational efficiency of green closed-loop supply chain system, but also provides new ideas for the research of green closed-loop supply chain operation mode. Full article

In urban air mobility (UAM), video streaming platforms have gained significant attention from media companies due to their growing necessity for on-demand video streaming services-as-you-go in flights. Video streaming services can provide constant data transactions in a huge amount, especially in its operational digital twin (ODT). As a result, the ability to provide a satisfactory user experience through video streaming platforms is critical and complex. This requires continuously operating services while handling numerous user requests for near real-time video streaming. At the same time, high-quality video with high resolution and minimal interruptions is often expected. Therefore, the availability and performance (i.e., performability) of the Back-End video streaming infrastructure are crucial parameters for these platforms. However, evaluating novel video-on-demand architectures in real-world scenarios can be costly due to the numerous parameters involved. Analytical models, such as stochastic Petri nets (SPNs), can serve as an alternative in this complex scenario as they can be used to analyze systems during the design process. In this study, we developed a set of SPN models to assess the performance of a video-on-demand system. These models were designed to illustrate and to evaluate a video-on-demand architecture while considering performance. We had a base SPN model as well as three enhanced variants available. The extended models were generated using the Design of Experiments (DoE) technique and sensitivity analysis results. The DoE identified the factors with the greatest impacts on performance, and the most significant factor interactions. Redundancy strategies were applied to the extended models to increase the availability of the most important components. This redundancy increased the availability of “9 s” from three to five. It is worth noting that this study can help the designers of video streaming systems, to plan and to optimize their ideas based on the provided models. Full article

Simulation is a powerful process for perfectly planning and dimensioning web systems. However, a successful analysis using a simulation model usually requires variable load intensities. Furthermore, as the client’s behavior is subject to frequent changes in modern web systems, such models need to be adapted as well. Based on web systems observation, we come across the need for tools that allow flexible definitions of web systems load profiles. We propose Timed Colored Petri Nets (TCPN) event generators for web environments that could be used to drive simulations for performance evaluation. The article proposes the systematization of the generators for future development. The theoretical part focuses on a classification with a division into deterministic/stochastic and untimed/timed generators. Next, in this study, we investigate representative models of generators from different classes based on the formalism of TCPN. We perform model design and simulation processes using CPN Tools software. Finally, we present a case study involving workload analysis of a web system that processes requests from the designed generator. Full article

Maintenance plays a crucial role in the availability of an asset. In particular, when a company’s assets are decentralized, logistical aspects directly impact maintenance management and, consequently, productivity. In the energy generation sector, this scenario is common in enterprises and projects in which distributed energy resources (DERs), such as small hydroelectric power plants (SHPPs), are considered. Hence, the objective of this work is to propose an application of generalized stochastic Petri nets (GSPN) for the planning and optimization of the maintenance logistics of a DER enterprise with two SHPPs. In the presented case study, different scenarios are modeled considering logistical aspects related to the availability of spare parts and the sharing of maintenance teams between plants. From the financial return resulting from the estimated energy generation and the operating cost of each simulated scenario, the most profitable one can be estimated. The results demonstrate the ability of GSPNs to estimate the influence of the number of spare parts and maintenance teams on the availability of DERs, allowing the optimization of costs related to maintenance logistics. Full article

Currently, there is a lack of developer-friendly software tools to formally address multi-robot coordination problems and obtain robust, efficient, and predictable strategies. This paper introduces a software toolbox that encapsulates, in one single package, modeling, planning, and execution algorithms. It implements a state-of-the-art approach to representing multi-robot systems: generalized Petri nets with rewards (GSPNRs). GSPNRs enable capturing multiple robots, decision states, action execution states and respective outcomes, action duration uncertainty, and team-level objectives. We introduce a novel algorithm that simplifies the model design process as it generates a GSPNR from a topological map. We also introduce a novel execution algorithm that coordinates the multi-robot system according to a given policy. This is achieved without compromising the model compactness introduced by representing robots as indistinguishable tokens. We characterize the computational performance of the toolbox with a series of stress tests. These tests reveal a lightweight implementation that requires low CPU and memory usage. We showcase the toolbox functionalities by solving a multi-robot inspection application, where we extend GSPNRs to enable the representation of heterogeneous systems and system resources such as battery levels and counters. Full article

Accessibility to buildings’ envelope depends on efficient inspection and other maintenance actions of their components. When access to these components is not planned, special means of access are required to carry out the maintenance work. Means of access, besides having a fundamental role on the quality of maintenance works of building envelope components, also represents a considerable part of the maintenance costs. Thus, to optimize costs and resources in maintenance plans, assessment of the impact of the means of access on maintenance costs is crucial. For works in height, there are several alternative means of access. The choice of the most adequate solution is strongly linked to the characteristics (e.g., architecture, height) and constraints (e.g., users, surrounding space) of each building, the maintenance needs of the envelope, and the time and funds available for the intervention. Therefore, in this study, a sensitivity analysis to understand how the cost of means of access can influence the maintenance costs is carried out. Moreover, the optimisation of maintenance activities in façade claddings is also analysed. This study intends to assess whether it is advantageous to consider permanent means of access during the design phase or opt for temporary means of access. In a first stage, the impact of six temporary means of access (supported and suspended scaffolds; articulated booms; telescopic booms; scissor lifts; and rope access) on the maintenance plans developed for the six types of claddings (ceramic tiling systems—CTS, natural stone claddings—NSC, rendered façades—RF, painted surfaces—PS, external thermal insulation composite systems—ETICS, and architectural concrete façades—ACF) is examined. The impact is estimated through a stochastic maintenance model based on Petri nets. After that, a sensitivity analysis and a multi-criteria decision analysis are performed. Based on the results, general recommendations are presented concerning the maintenance strategies to adopt in the cladding solutions analysed. The results reveal that planning the means of access during the design stage can be economically beneficial for all buildings’ envelope components. Full article

Some infectious diseases such as COVID-19 have the characteristics of long incubation period, high infectivity during the incubation period, and carriers with mild or no symptoms which are more likely to cause negligence. Global researchers are working to find out more about the transmission of infectious diseases. Modeling plays a crucial role in understanding the transmission of the new virus and helps show the evolution of the epidemic in stages. In this paper, we propose a new general transmission model of infectious diseases based on the generalized stochastic Petri net (GSPN). First, we qualitatively analyze the transmission mode of each stage of infectious diseases such as COVID-19 and explain the factors that affect the spread of the epidemic. Second, the GSPN model is built to simulate the evolution of the epidemic. Based on this model’s isomorphic Markov chain, the equilibrium state of the system and its changing laws under different influencing factors are analyzed. Our paper demonstrates that the proposed GSPN model is a compelling tool for representing and analyzing the transmission of infectious diseases from system-level understanding, and thus contributes to providing decision support for effective surveillance and response to epidemic development. Full article

Rapid changes in electricity power markets have increased the production costs of coal-fired power plants and pushed their production to the limits of profitability. For power plants currently in operation, a possible approach to cope with this issue is to introduce novel methods that increase the plant’s reliability and availability. Coal mills are a subsystem that should ensure a plant’s availability without unexpected breakdowns. Remediation-based maintenance is defined as a set of actions performed after fault detection that do not require instant shutdown due to safety reasons. The aim of this paper was to provide a scientific confirmation that by implementing a novel remediation-based maintenance strategy, electricity production breakdowns can be significantly reduced. First, the performance of the proposed maintenance method was proved in simulation where coal mills were modeled by generalized stochastic Petri nets. The maintenance strategy was then experimentally verified in a 220 MW coal-fired power plant located in Croatia, where the plant’s availability, reliability and efficiency were increased. Full article

Applications in the Internet of Things (IoT) context continuously generate large amounts of data. The data must be processed and monitored to allow rapid decision making. However, the wireless connection that links such devices to remote servers can lead to data loss. Thus, new forms of a connection must be explored to ensure the system’s availability and reliability as a whole. Unmanned aerial vehicles (UAVs) are becoming increasingly empowered in terms of processing power and autonomy. UAVs can be used as a bridge between IoT devices and remote servers, such as edge or cloud computing. UAVs can collect data from mobile devices and process them, if possible. If there is no processing power in the UAV, the data are sent and processed on servers at the edge or in the cloud. Data offloading throughout UAVs is a reality today, but one with many challenges, mainly due to unavailability constraints. This work proposes stochastic Petri net (SPN) models and reliability block diagrams (RBDs) to evaluate a distributed architecture, with UAVs focusing on the system’s availability and reliability. Among the various existing methodologies, stochastic Petri nets (SPN) provide models that represent complex systems with different characteristics. UAVs are used to route data from IoT devices to the edge or the cloud through a base station. The base station receives data from UAVs and retransmits them to the cloud. The data are processed in the cloud, and the responses are returned to the IoT devices. A sensitivity analysis through Design of Experiments (DoE) showed key points of improvement for the base model, which was enhanced. A numerical analysis indicated the components with the most significant impact on availability. For example, the cloud proved to be a very relevant component for the availability of the architecture. The final results could prove the effectiveness of improving the base model. The present work can help system architects develop distributed architectures with more optimized UAVs and low evaluation costs. Full article

The high reliability of modern engineering systems is achieved by performing predictive maintenance. Mathematical models based on stochastic timed colored Petri nets are an effective tool for developing complex production processes for Industry 4.0. This article discusses the maintainability evaluation used in hierarchical Petri net models. The hierarchical simulation model was built using timed colored Petri nets, and was constructed with four levels of repair and maintenance modules. New module structures are proposed for simulating the schedule of production tasks and interaction with technological units. The emphasis is on the processes of predicting maintenance and repair, moving units to service, replacing units, and forming a reserve. The design of the simulation modules allows the setting of probabilistic parameters for the distributions of equipment failures, requests for unit maintenance, repair time, and recovery time after repair. The article proposes to use the hierarchical Petri model in conjunction with solving the problem of minimizing the cost of service. The iterative procedure consists of obtaining an approximate unit distribution by tasks, subsequent simulation of the technological process, and adjusting the optimization problem constraints. For example, the hierarchical Petri net is considered to assess the maintainability of autonomous agricultural vehicles. The results of the simulation experiments are presented. A simulation of the agrotechnical production process was performed, during which vehicles were maneuvered, taken out for repair or maintenance, and returned to the reserve fund. The interdependencies of preventive maintenance periods, service operations, failure rates, and predictive maintenance requests were obtained in order to comply with the task scheduling. The proposed model is a generalization, but it is especially effective in studying mobile equipment servicing. Full article

With the development of satellite communication networks and the increase of satellite services, security problems have gradually become some of the most concerning issues. Researchers have made great efforts, including conventional safety methods such as secure transmission, anti-jamming, secure access, and especially the new generation of active defense technology represented by MTD. However, few scholars have theoretically studied the influence of active defense technique on the performance of satellite networks. Formal modeling and performance analysis have not been given sufficient attention. In this paper, we focus on the performance evaluation of satellite network moving target defense system. Firstly, two Stochastic Petri Nets (SPN) models are constructed to analyze the performance of satellite network in traditional and active defense states, respectively. Secondly, the steady-state probability of each marking in SPN models is obtained by using the isomorphism relation between SPN and Markov Chains (MC), and further key performance indicators such as average time delay, throughput, and the utilization of bandwidth are reasoned theoretically. Finally, the proposed two SPN models are simulated based on the PIPE platform. In addition, the effect of parameters on the selected performance indexes is analyzed by varying the values of different parameters. The simulation results prove the correctness of the theoretical reasoning and draw the key factors affecting the performance of satellite network, which can provide an important theoretical basis for the design and performance optimization of the satellite network moving target defense system. Full article