Relevant bibliographies by topics / Stochastic Petri Net Modeling

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Academic literature on the topic 'Stochastic Petri Net Modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Stochastic Petri Net Modeling"

1

Proß, Sabrina, and Bernhard Bachmann. "An Advanced Environment for Hybrid Modeling of Biological Systems Based on Modelica." Journal of Integrative Bioinformatics 8, no. 1 (2011): 1–34. http://dx.doi.org/10.1515/jib-2011-152.

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Summary Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process.The Modelica-models can be connected to Simulink-models for parameter optimization, sensitivity analysis and stochastic simulation in Matlab.The present paper illustrates the implementation of the Petri Net component models, their usage within the modeling process and the coupling between the Modelica-tool Dymola and Matlab/Simulink. The application is demonstrated by modeling the metabolism of Chinese Hamster Ovary Cells.
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Amrita Dey, Nabendu Chaki, and Sugata Sanyal. "Modeling Smart Grid using Generalized Stochastic Petri Net." Journal of Convergence Information Technology 6, no. 11 (2011): 104–14. http://dx.doi.org/10.4156/jcit.vol6.issue11.13.

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3

Shareef, Ali, and Yifeng Zhu. "Effective Stochastic Modeling of Energy-Constrained Wireless Sensor Networks." Journal of Computer Networks and Communications 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/870281.

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Energy consumption of energy-constrained nodes in wireless sensor networks (WSNs) is a fatal weakness of these networks. Since these nodes usually operate on batteries, the maximum utility of the network is dependent upon the optimal energy usage of these nodes. However, new emerging optimal energy consumption algorithms, protocols, and system designs require an evaluation platform. This necessitates modeling techniques that can quickly and accurately evaluate their behavior and identify strengths and weakness. We propose Petri nets as this ideal platform. We demonstrate Petri net models of wireless sensor nodes that incorporate the complex interactions between the processing and communication components of an WSN. These models include the use of both an open and closed workload generators. Experimental results and analysis show that the use of Petri nets is more accurate than the use of Markov models and programmed simulations. Furthermore, Petri net models are extremely easier to construct and test than either. This paper demonstrates that Petri net models provide an effective platform for studying emerging energy-saving strategies in WSNs.
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4

Haas, Peter J., and Gerald S. Shedler. "Stochastic Petri Nets: Modeling Power and Limit Theorems." Probability in the Engineering and Informational Sciences 5, no. 4 (1991): 477–98. http://dx.doi.org/10.1017/s0269964800002242.

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Generalized semi-Markov processes and stochastic Petri nets provide building blocks for specification of discrete event system simulations on a finite or countable state space. The two formal systems differ, however, in the event scheduling (clock-setting) mechanism, the state transition mechanism, and the form of the state space. We have shown previously that stochastic Petri nets have at least the modeling power of generalized semi-Markov processes. In this paper we show that stochastic Petri nets and generalized semi-Markov processes, in fact, have the same modeling power. Combining this result with known results for generalized semi-Markov processes, we also obtain conditions for time-average convergence and convergence in distribution along with a central limit theorem for the marking process of a stochastic Petri net.
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5

LIU, FEI, MONIKA HEINER, and MING YANG. "MODELING AND ANALYZING BIOLOGICAL SYSTEMS USING COLORED HIERARCHICAL PETRI NETS ILLUSTRATED BYC. ELEGANSVULVAL DEVELOPMENT." Journal of Biological Systems 22, no. 03 (2014): 463–93. http://dx.doi.org/10.1142/s0218339014500181.

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Colored Petri nets allow compact, parameterizable and scalable representations of large-scale biological models by encoding, e.g., species as colored tokens, and offer a variety of analysis techniques, e.g., structural analysis, simulation and model checking to analyze biological models. However, so far colored Petri nets have not been widely used and well explored in systems biology. In this paper, we aim to present a systematic approach to modeling and analyzing complex biological systems using colored Petri nets in order to help biologists to easily use them. We first describe a framework comprising a family of related colored Petri nets: colored qualitative Petri net (𝒬𝒫𝒩𝒞), colored stochastic Petri net (𝒮𝒫𝒩𝒞) and colored continuous Petri net (𝒞𝒫𝒩𝒞). They share structure, but are specialized by their kinetic information. Based on this framework, we present our colored Petri net approach to modeling and analyzing complex biological systems. First a biological system is modeled as a hierarchical 𝒬𝒫𝒩𝒞model, animated and analyzed by structural analysis; then it is converted into a 𝒮𝒫𝒩𝒞or 𝒞𝒫𝒩𝒞model, to be further analyzed using stochastic or continuous simulation, and simulative or numerical model checking. We demonstrate this approach using a nontrivial example, Caenorhabditis elegans vulval development.
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6

Haas, Peter J., and Gerald S. Shedler. "Modeling Power of Stochastic Petri Nets for Simulation." Probability in the Engineering and Informational Sciences 2, no. 4 (1988): 435–59. http://dx.doi.org/10.1017/s0269964800000152.

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Generalized semi-Markov processes and stochastic Petri nets have been proposed as general frameworks for a discrete event simulation on a countable state space. The two formal systems differ, however, with respect to the clock setting (event scheduling) mechanism, the state transition mechanism, and the form of the state space. We obtain conditions under which the marking process of a stochastic Petri net “mimics” a generalized semi-Markov process in the sense that the two processes (and their underlying general state-space Markov chains) have the same finite dimensional distributions. The results imply that stochastic Petri nets have at least the modeling power of generalized semiMarkov processes for discrete event simulation.
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7

Coman, Daniela, Nicu Bizdoaca, Anca Petrisor, Adela Ionescu, and Mihaela Florescu. "Modeling and Analysis of Soccer Robot Tasks Using Generalized Stochastic Petri Nets." Solid State Phenomena 166-167 (September 2010): 133–38. http://dx.doi.org/10.4028/www.scientific.net/ssp.166-167.133.

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Robot soccer competition provides an excellent opportunity for robotics research. In particular, the soccer robots must perform real-time visual recognition, navigate in a dynamic field, avoid the obstacle, collaborate with teammates, and kick the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other robot to accomplish tasks that are beyond individual’s capabilities), and intelligent (reasoning and planing actions and perhaps learning from experience). This paper proposes a method of defining and analysing the attacking robot tasks using Petri nets. Attacking soccer robot behaviors are modeled using the generalized stochastic Petri nets. Using Petri nets allows qualitative and quantitative analysis of the task execution. Petri net model is implemented in Petri Net Toolbox under MATLAB environment. Therefore, it is validated the net topology, the evolution of (their dynamics), the structural and behavioral properties (corresponding to checking if resources usage is stable and the model have no deadlocks), as well as the stochastic performance.
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8

Pan, Cheng Xian, Rui Peng, Jun Li Wang, and Qing Lu. "OOSPN-Based Modeling and Simulation of Tridimensional Rail Net Transmission System in Container Terminal." Applied Mechanics and Materials 58-60 (June 2011): 1240–45. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1240.

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Object-Oriented Stochastic Petri Net (OOSPN) is utilized concerning the complexity of container terminal tridimensional rail transmission system network. It simplifies the structure and modeling with object-oriented and hierarchy idea, while the efficiency of each state is detected based on Stochastic Petri Net performance analysis. Thereby, it finds the system bottleneck which also provides reference for the improvement of the program. The efficiency indicators are obtained by analysis and construction of container terminal transmission system as well.
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9

Chin, Toshio M., and Alan S. Willsky. "Stochastic Petri net modeling of wave sequences in cardiac arrhythmias." Computers and Biomedical Research 22, no. 2 (1989): 136–59. http://dx.doi.org/10.1016/0010-4809(89)90022-0.

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10

Zhuang, Lu, Zhong Lu, and Ziwen Zhang. "Dynamic Reliability Model for Airborne Systems Based on Stochastic Petri Net." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 4 (2020): 846–54. http://dx.doi.org/10.1051/jnwpu/20203840846.

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The reliability of the airborne systems have a significant influence on the safety of aircraft. The modern airborne systems have a high degree of automation and integration, which lead to obvious dynamic failure characteristics. Namely, system failure is not only dependent on the combination of units' failures but also related to their sequence. A dynamic reliability method for modeling airborne systems is proposed based on the stochastic Petri nets. Stochastic Petri nets are applied in reliability modeling for typical dynamic structures including warm standby, cold standby and load sharing, which are widely used in airborne systems. In this way, the dynamic (time-dependent) failure behaviors of the airborne system can be represented. In terms of the stochastic Petri net based reliability model, a reliability analysis method based on Monte Carlo simulation is proposed by generating system life samples for system reliability parameter calculation. Finally, an electrical power system is used as a case to illustrate the application and effectiveness of the present approaches. The results show that the difference by using the present method and the analytical method is below 2×10-7, which can be neglected in practice.
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