RESUMEN

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation.

RESUMEN

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.

RESUMEN

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision-making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices.

RESUMEN

As an inevitable part of construction and demolition (C&D) waste, muck has a dreadful environmental impact due its inadequate management by the traditional governance process. This paper therefore focuses on the management of muck generated from C&D waste by utilizing platform governance as an alternative process, which should more effectively contribute to China's circular economy. The study explores the feasibility of providing such a platform governance mode by using Petri net to compare the traditional governance process and platform governance process for the management of muck trucks, and by using Nanjing's muck smart supervision platform as a case study to assess the effectiveness of the platform governance mode. Results from Petri net simulation modeling reveal that the platform governance mode is more effective than the traditional mode, and from the case study it is found that the success of Nanjing's muck waste management can be attributed to the platform governance mode. The platform management approach can therefore contribute to the sustainability of muck waste governance, and is suitable as an integrated and effective management mode for current practices of muck waste management and resource recovery in China. The main finding from the study is that the platform governance mode significantly improves the efficiency of muck waste management as compared with the traditional governance mode and can therefore provide greater economic and environmental benefits as part of a circular economy.

Asunto(s)

RESUMEN

The primary aim of this research was to propose algorithms enabling the identification of significant reactions and subprocesses within models of biological systems constructed using classical Petri nets. These solutions allow to performance of two analysis methods: an importance analysis for identifying individual reactions critical to the functioning of the model and an occurrence analysis for finding essential subprocesses. To demonstrate the utility of these methods, analyses of an example model have been performed. In this case, it was a model related to the DNA damage response mechanism. It is worth noting that the proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods represent an extension of classical Petri net-based analyses. Their utility lies in their potential to enhance our comprehension of the biological phenomena under investigation. Furthermore, they can lead to the development of more effective medical therapies, as they can aid in the identification of potential molecular targets for drugs.

Asunto(s)

RESUMEN

The cold chain supply chain of e-commerce has the characteristics of long chain, dispersed production, heterogeneous information sources, etc., which is easy to cause the upstream and downstream information of the supply chain to be broken and opaque. Traditional traceability data is stored in each node enterprise, and there are problems such as low cooperation trust and poor authenticity in the upstream and downstream data transmission process of the supply chain, resulting in consumers' trust crisis on the authenticity of traceability information. Blockchain traceability system through the establishment of multi-party participation, joint maintenance of distributed database, and the use of cryptography and consensus mechanism to establish trust relationships, with data can not be tampered with, sharing of high credibility. On the basis of stochastic Petri net theory, this study constructed a blockchain e-commerce cold chain traceability model centering on the actual application of blockchain technology in logistics traceability to improve the reliability and validity of e-commerce cold chain traceability. Correlation matrix and invariants were used to analyze the validity of the model, and an isomorphic Markov chain was constructed to analyze the effectiveness evolution of the model. This study aims to deepen the understanding of the cold chain traceability system of blockchain e-commerce from the three aspects of the best optimization link, the best synergism scale, and the adaptive ability of the system to provide reference for the improvement of cold chain traceability ability by blockchain technology.

RESUMEN

Dynamic discrete event systems (DDES) are systems that evolve from the asynchronous occurrence of discrete events. Their versatility has become a critical modeling tool in different applications. Finding models that define the behavior of DES is a topic that has been addressed from different approaches, depending on the type of system to be modeled and the model's objective. This article focuses on the identification of timed models for stochastic discrete event systems. The identified model includes both observable and unobservable behavior. The objective of the method is achieved through the following steps:•Identifying the sequences of events observed at different time instances during the closed-loop operation of the system (observed language),•Inferring the stochastic behavior of time between events and modeling the observable behavior as a stochastic timed Interpreted Petri Net (st-IPN),•and finally, inferring the non-observable behavior using the language projection operation between the observed language and the language generated by the st-IPN.This method has novel aspects because it uses timed events, can be applied to systems with a low number of sensors and can infer unobservable behavior for any sequence of events.

RESUMEN

This study addresses the pressing issue of energy consumption and efficiency in the Kingdom of Saudi Arabia (KSA), a region experiencing growing demand for energy resources. Temperature control plays a vital role in achieving energy efficiency; however, traditional control systems may struggle to adapt to the non-linear and time-varying characteristics of the problem. To tackle this challenge, a fuzzy petri net (FPN) controller is proposed as a more suitable solution that combines fuzzy logic (FL) and petri nets (PN) to model and simulate complex systems. The main objective of this research is to develop an intelligent energy-saving framework that integrates advanced methodologies and air conditioning (AC) systems to optimize energy utilization and create a comfortable indoor environment. The proposed system incorporates user identification to authorize individuals who can set the temperature, and FL combined with PN is utilized to monitor and transmit their preferred temperature settings to a PID controller for adjustment. The experimental findings demonstrate the effectiveness of integrating the FPN controller with a convertible frequency AC compressor in significantly reducing energy consumption by 94% compared to using the PN controller alone. The utilization of the PN controller alone resulted in a 25% reduction in energy consumption. Conversely, employing a fixed-frequency compressor led to a 40% increase in energy consumption. These results emphasize the advantages of integrating FL into the PN model, as it effectively reduces energy consumption by half, highlighting the potential of the proposed approach for enhancing energy efficiency in AC systems.

Asunto(s)

RESUMEN

The distribution of wireless network systems challenges the communication security of Internet of Things (IoT), and the IPv6 protocol is gradually becoming the main communication protocol under the IoT. The Neighbor Discovery Protocol (NDP), as the base protocol of IPv6, includes address resolution, DAD, route redirection and other functions. The NDP protocol faces many attacks, such as DDoS attacks, MITM attacks, etc. In this paper, we focus on the communication-addressing problem between nodes in the Internet of Things (IoT). We propose a Petri-Net-based NS flooding attack model for the flooding attack problem of address resolution protocols under the NDP protocol. Through a fine-grained analysis of the Petri Net model and attacking techniques, we propose another Petri-Net-based defense model under the SDN architecture, achieving security for communications. We further simulate the normal communication between nodes in the EVE-NG simulation environment. We implement a DDoS attack on the communication protocol by an attacker who obtains the attack data through the THC-IPv6 tool. In this paper, the SVM algorithm, random forest algorithm (RF) and Bayesian algorithm (NBC) are used to process the attack data. The NBC algorithm is proven to exhibit high accuracy in classifying and identifying data through experiments. Further, the abnormal data are discarded through the abnormal data processing rules issued by the controller in the SDN architecture, to ensure the security of communications between nodes.

Asunto(s)

RESUMEN

Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (GED_NAR). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs.

RESUMEN

The bacteria Mycobacterium tuberculosis is responsible for the infectious disease Tuberculosis. Targeting the tubercule bacteria is an important challenge in developing the antimycobacterials. The glyoxylate cycle is considered as a potential target for the development of anti-tuberculosis agents, due to its absence in the humans. Humans only possess tricarboxylic acid cycle, while this cycle gets connected to glyoxylate cycle in microbes. Glyoxylate cycle is essential to the Mycobacterium for its growth and survival. Due to this reason, it is considered as a potential therapeutic target for the development of anti-tuberculosis agents. Here, we explore the effect on the behavior of the tricarboxylic acid cycle, glyoxylate cycle and their integrated pathway with the bioenergetics of the Mycobacterium, under the inhibition of key glyoxylate cycle enzymes using Continuous Petri net. Continuous Petri net is a special Petri net used to perform the quantitative analysis of the networks. We first study the tricarboxylic acid cycle and glyoxylate cycle of the tubercule bacteria by simulating its Continuous Petri net model under different scenarios. Both the cycles are then integrated with the bioenergetics of the bacteria and the integrated pathway is again simulated under different conditions. The simulation graphs show the metabolic consequences of inhibiting the key glyoxylate cycle enzymes and adding the uncouplers on the individual as well as integrated pathway. The uncouplers that inhibit the synthesis of adenosine triphosphate, play an important role as anti-mycobacterials. The simulation study done here validates the proposed Continuous Petri net model as compared with the experimental outcomes and also explains the consequences of the enzyme inhibition on the biochemical reactions involved in the metabolic pathways of the mycobacterium.

Asunto(s)

RESUMEN

The simulation of immune response is a challenging task because quantitative data are scarce. Quantitative theoretical models either focus on specific cell-cell interactions or have to make assumptions about parameters. The broad variation of, e.g., the dimensions and abundance between lymph nodes as well as between individual patients hampers conclusive quantitative modeling. No theoretical model has been established representing a consensus on the set of major cellular processes involved in the immune response. In this paper, we apply the Petri net formalism to construct a semi-quantitative mathematical model of the lymph nodes. The model covers the major cellular processes of immune response and fulfills the formal requirements of Petri net models. The intention is to develop a model taking into account the viewpoints of experienced pathologists and computer scientists in the field of systems biology. In order to verify formal requirements, we discuss invariant properties and apply the asynchronous firing rule of a place/transition net. Twenty-five transition invariants cover the model, and each is assigned to a functional mode of the immune response. In simulations, the Petri net model describes the dynamic modes of the immune response, its adaption to antigens, and its loss of memory.

RESUMEN

The aim of the study was to optimize the pre-hospital first aid management strategy for patients with infectious diseases in Huizhou city, which is expected to provide a basis for the epidemic prevention and control, to save lives, and increase the pre-hospital first aid efficiency. At the Department of Emergency, Huizhou Third People's Hospital as the research subject, the common pre-hospital first aid procedure for infectious diseases was identified. The Petri net was used to model and determine the execution time of each link of the pre-hospital first aid process. The isomorphic Markov chain was used to optimize the pre-hospital first aid procedure for infectious diseases. In terms of the emergency path, deep learning was combined with the reinforcement learning model to construct the reinforcement learning model for ambulance path planning. Isomorphic Markov chain analysis revealed that the patient status when returning to the hospital, the time needed for the ambulance to come to designated location, and the on-site treatment were the main problems in the first aid process, and the time needed for the pre-hospital first aid process was reduced by 25.17% after optimization. In conclusion, Petri net and isomorphic Markov chain can optimize the pre-hospital first aid management strategies for patients with infectious diseases, and the use of deep learning algorithm can effectively plan the emergency path, achieving intelligent and informationalized pre-hospital transfer, which provides a basis for reducing the suffering, mortality, and disability rate of patients with infectious diseases.

Asunto(s)

RESUMEN

Due to the complexity of the virus and its rapid rate of spread, many countries face the same challenges of providing adequate medical resources. This paper provides an analytical approach for evaluating the possibility of the regional construction industry constructing a large number of cabin hospitals within a short time. The key idea is to compare the demand and supply of patient beds using a Petri net-based approach that incorporates a neural network for the prediction of demand, fuzzy logic for decision-making, and a linear model for predicting supply. The data reported in the Shanghai Omicron battle is used to validate the developed model. Our results show that the fastest conversion speed and the least manpower requirement are obtained from high-rise buildings. Then, preparing some high-rises for easy conversion into cabin hospitals seems a possible solution for future citywide preparedness toward pandemic resilience.

RESUMEN

Autonomous components within electric power systems can be successfully specified by interpreted Petri nets. Such a formal specification makes it possible to check some basic properties of the models, such as determinism or deadlock freedom. In this paper, it is shown how these models can also be formally verified against some behavioral user-defined properties that relate to the safety or liveness of a designed system. The requirements are written as temporal logic formulas. The rule-based logical model is used to support the verification process. An interpreted Petri net is first written as an abstract logical model, and then automatically transformed into a verifiable model that is supplemented by appropriate properties for checking. Formal verification is then performed with the nuXmv model checker. Thanks to this the initial specification of autonomous components can be formally verified and any design errors can be identified at an early stage of system development. An electric energy storage (EES) is presented as an application system for the provision of a system service for stabilizing the power of renewable energy sources (RES) or highly variable loads. The control algorithm of EES in the form of an interpreted Petri net is then written as a rule-based logical model and transformed into a verifiable model, allowing automatic checking of user-defined requirements.

Asunto(s)

RESUMEN

EnOcean, a commonly used control protocol in smart lighting systems, provides authentication, as well as message integrity verification services, and can resist replay attack and tamper attack. However, since the device identity information transmitted between sensors in smart lighting control systems is easily accessible by malicious attackers, attackers can analyze users' habits based on the intercepted information. This paper analyzed the security of the EnOcean protocol using a formal analysis method based on the colored Petri net (CPN) theory and the Dolev-Yao attacker model and found that the protocol did not anonymize the device identity information and did not have a communication key update mechanism, so an attacker could easily initiate a key compromise impersonation attack (KCIA) after breaking the pre-shared communication key. To address the above security issues, this paper proposed an EnOcean-A protocol with higher security based on the EnOcean protocol. The EnOcean-A protocol introduced a trusted third-party server to send communication keys to communication devices because devices must obtain different communication keys from the trusted third-party server each time they communicated. Thus, this protocol could resist a KCIA and achieve forward security. Meanwhile, the device identity information was anonymized using a homomorphic hash function in the EnOcean-A protocol, and the dynamic update mechanism of the device identity information was added so that an attacker could not obtain the real identity information of the device. Finally, the formal analysis of the EnOcean-A protocol showed that the new protocol could resist a KCIA and ensure the anonymity and untraceability of the communication device, which had higher security compared with the EnOcean protocol.

Asunto(s)

RESUMEN

This paper proposes the transition times of Petri net models of human gait as training features for multiclass random forests (RFs) and classification trees (CTs). These models are designed to support screening for neurodegenerative diseases. The proposed Petri net describes gait in terms of nine cyclic phases and the timing of the nine events that mark the transition between phases. Since the transition times between strides vary, each is represented as a random variable characterized by its mean and standard deviation. These transition times are calculated using the PhysioNet database of vertical ground reaction forces (VGRFs) generated by feet-ground contact. This database comprises the VGRFs of four groups: amyotrophic lateral sclerosis, the control group, Huntington's disease, and Parkinson disease. The RF produced an overall classification accuracy of 91%, and the specificities and sensitivities for each class were between 80% and 100%. However, despite this high performance, the RF-generated models demonstrated lack of interpretability prompted the training of a CT using identical features. The obtained tree comprised only four features and required a maximum of three comparisons. However, this simplification dramatically reduced the overall accuracy from 90.6% to 62.3%. The proposed set features were compared with those included in PhysioNet database of VGRFs. In terms of both the RF and CT, more accurate models were established using our features than those of the PhysioNet.

Asunto(s)

RESUMEN

The control of the pandemic caused by SARS-CoV-2 is a challenge for governments all around the globe. To manage this situation, countries have adopted a bundle of measures, including restrictions to population mobility. As a consequence, drivers face with the problem of obtaining fast routes to reach their destinations. In this context, some recent works combine Intelligent Transportation Systems (ITS) with big data processing technologies taking the traffic information into account. However, there are no proposals able to gather the COVID-19 health information, assist in the decision-making process, and compute fast routes in an all-in-one solution. In this paper, we propose a Pandemic Intelligent Transportation System (PITS) based on Complex Event Processing (CEP), Fuzzy Logic (FL) and Colored Petri Nets (CPN). CEP is used to process the COVID-19 health indicators and FL to provide recommendations about city areas that should not be crossed. CPNs are then used to create map models of health areas with the mobility restriction information and obtain fast routes for drivers to reach their destinations. The application of PITS to Madrid region (Spain) demonstrates that this system provides support for authorities in the decision-making process about mobility restrictions and obtain fast routes for drivers. PITS is a versatile proposal which can easily be adapted to other scenarios in order to tackle different emergency situations.

RESUMEN

Malicious botnets such as Mirai are a major threat to IoT networks regarding cyber security. The Botnet Defense System (BDS) is a network security system based on the concept of "fight fire with fire", and it uses white-hat botnets to fight against malicious botnets. However, the existing white-hat Worm Launcher of the BDS decides the number of white-hat worms, but it does not consider the white-hat worms' placement. This paper proposes a novel machine learning (ML)-based white-hat Worm Launcher for tactical response by zoning in the BDS. The concept of zoning is introduced to grasp the malicious botnet spread with bias over the IoT network. This enables the Launcher to divide the network into zones and make tactical responses for each zone. Three tactics for tactical responses for each zone are also proposed. Then, the BDS with the Launcher is modeled by using agent-oriented Petri nets, and the effect of the proposed Launcher is evaluated. The result shows that the proposed Launcher can reduce the number of infected IoT devices by about 30%.

RESUMEN

The UAV industry is developing rapidly and drones are increasingly used for monitoring industrial facilities. When designing such systems, operating companies have to find a system configuration of multiple drones that is near-optimal in terms of cost while achieving the required monitoring quality. Stochastic influences such as failures and maintenance have to be taken into account. Model-based systems engineering supplies tools and methods to solve such problems. This paper presents a method to model and evaluate such UAV systems with coloured Petri nets. It supports a modular view on typical setup elements and different types of UAVs and is based on UAV application standards. The model can be easily adapted to the most popular flight tasks and allows for estimating the monitoring frequency and determining the most appropriate grouping and configuration of UAVs, monitoring schemes, air time and maintenance periods. An important advantage is the ability to consider drone maintenance processes. Thus, the methodology will be useful in the conceptual design phase of UAVs, in monitoring planning, and in the selection of UAVs for specific monitoring tasks.

Asunto(s)