A Novel Approach for Constraint Transformation in Petri Nets With Uncontrollable Transitions

2018 ◽  
Vol 48 (8) ◽  
pp. 1403-1410 ◽  
Author(s):  
Shouguang Wang ◽  
Dan You ◽  
Carla Seatzu
2016 ◽  
Vol 78 (5-2) ◽  
Author(s):  
Rizati Hamidun ◽  
Nurul Elma Kordi ◽  
Intan Rohani Endut ◽  
Siti Zaharah Ishak ◽  
Mohd Faudzi Mohd Yusof

Risk of pedestrian while crossing a road section may influence by several factors, including their crossing behaviors which might be difficult to be measured. In this paper, a model using Petri nets is introduced to consider the behavioral factors in measuring pedestrian risk. The crossing scenario of the pedestrian was observed to identify the pedestrian accident event. Sequence of event in pedestrian accident was modeled into Petri Nets elements. The model is designed in the hierarchical structure to consider risk factors related to human behavior, engineering and environment. The analysis of the model provides the numerical value of pedestrian potential risk as they crossed at a signalized intersection. The effect of each factor on the potential risk can be observed through sensitivity analysis.  The use of Petri Nets is a novel approach in predicting pedestrian potential risk through the modeling of pedestrian accident process.


Author(s):  
Michael Blondin ◽  
Christoph Haase ◽  
Philip Offtermatt

AbstractNumerous tasks in program analysis and synthesis reduce to deciding reachability in possibly infinite graphs such as those induced by Petri nets. However, the Petri net reachability problem has recently been shown to require non-elementary time, which raises questions about the practical applicability of Petri nets as target models. In this paper, we introduce a novel approach for efficiently semi-deciding the reachability problem for Petri nets in practice. Our key insight is that computationally lightweight over-approximations of Petri nets can be used as distance oracles in classical graph exploration algorithms such as $$\mathsf {A}^{*}$$ A ∗ and greedy best-first search. We provide and evaluate a prototype implementation of our approach that outperforms existing state-of-the-art tools, sometimes by orders of magnitude, and which is also competitive with domain-specific tools on benchmarks coming from program synthesis and concurrent program analysis.


2016 ◽  
Vol 14 (05) ◽  
pp. 1650026 ◽  
Author(s):  
Mani Mehraei ◽  
Rza Bashirov ◽  
Şükrü Tüzmen

Recent molecular studies provide important clues into treatment of [Formula: see text]-thalassemia, sickle-cell anaemia and other [Formula: see text]-globin disorders revealing that increased production of fetal hemoglobin, that is normally suppressed in adulthood, can ameliorate the severity of these diseases. In this paper, we present a novel approach for drug prediction for [Formula: see text]-globin disorders. Our approach is centered upon quantitative modeling of interactions in human fetal-to-adult hemoglobin switch network using hybrid functional Petri nets. In accordance with the reverse pharmacology approach, we pose a hypothesis regarding modulation of specific protein targets that induce [Formula: see text]-globin and consequently fetal hemoglobin. Comparison of simulation results for the proposed strategy with the ones obtained for already existing drugs shows that our strategy is the optimal as it leads to highest level of [Formula: see text]-globin induction and thereby has potential beneficial therapeutic effects on [Formula: see text]-globin disorders. Simulation results enable verification of model coherence demonstrating that it is consistent with qPCR data available for known strategies and/or drugs.


Author(s):  
JAYA SIL ◽  
AMIT KONAR

Knowledge acquisition from multiple experts and its refinement are important issues in knowledge management of an expert system. The paper presents a novel approach to handling the above problems by combining the synergistic behavior of neural Petri nets and the Dempster–Shafer theory. The Dempster–Shafer theory has been employed here to reduce the scope of uncertainty in the supplied noisy input instances and the inferences generated therefrom by the multiple experts. The noise-free training instances thus obtained are subsequently used to train the neural Petri net model for refining the parameters of its knowledge base. A comparison of the performance of the proposed training algorithm with the classical back-propagation algorithm has also been presented in the paper.


2020 ◽  
Vol 1 ◽  
pp. 617-626
Author(s):  
J. Juranić ◽  
N. Pavković ◽  
D. Jurinić

AbstractA new way of structuring and interpretation of multiple domain matrix is proposed as the basis for categorisation of design parameter relations complexity. Depending on the kind and the degree of coupling of the parameters, the developed methodology activates the appropriate coloured Petri net (CPN) models for semi-automatic support of communication between the members of the design team. The proposed extension of MDM combined with CPN is a novel approach to predicting and managing communication patterns necessary during teamwork coordination on critical interfaces between product components.


Author(s):  
Hussein ‎ A. Lafta ◽  
Rand Abdul-Wahid M. Ali

In this wok, a novel approach based on ordinary Petri net is used to generate private key . The reachability marking  of petri net is used as encryption/decryption key to provide more complex key . The same ordinary Petri Nets models  are used for the sender(encryption) and  the receiver(decryption).The plaintext has been permutated  using  look-up table ,and XOR-ed with key to generate cipher text


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