scholarly journals Language and semantics of expressions for Grafcet model synthesis in a MDE environment

2021 ◽  
Vol Volume 33 - 2020 - Special... ◽  
Author(s):  
Gérard NZEBOP NDENOKA ◽  
Maurice Tchuenté ◽  
Emmanuel Simeu
Keyword(s):  
Discrete Event ◽  
Modeling Language ◽  
Nous Proposons ◽  
Graphical Modeling ◽  
Context Free Grammar ◽  
Model Synthesis ◽  
Model Driven ◽  
Emf Model ◽  
Context Free ◽  
Arithmetic Expressions

The GRAphe Fonctionnel de Commande Étapes Transitions (GRAFCET) is a powerful graphical modeling language for the pecification of controllers in discrete event systems.It uses expressions to express the conditions of transitions and conditional actions as well as the logical and arithmetic expressions assigned to stored actions. However, several research works has focused on the transformation of Grafcet specifications (including expressions) into control code for embedded systems. To make it easier to edit valid Grafcet models and generate code, it is necessary to propose a formalization of the Grafcet expression language permitting to validate its constructs and provide an appropriate semantics. For this, we propose a context-free grammar that generates the whole set of Grafcet expressions, by extending the usual grammars of logical and arithmetic expressions. We also propose a metamodel and an associated semantics of Grafcet expressions to facilitate the implementation of the Grafcet language. A parser of the expressions Grafcet emph G7Expr is then obtained thanks to the generator of parsers ANTLR, while the metamodel is implemented in the Eclipse EMF Model Driven Engineering (MDE) environment. The combination of the two tools makes it possible to analyze and automatically build Grafcet expressions when editing and synthesizing Grafcet models. Le GRAphe Fonctionnel de Commande Étapes Transitions (GRAFCET) est un puissant lan-gage de modélisation graphique pour la spécification de contrôleurs dans des systèmes à événe-ments discrets. Il fait usage des expressions pour exprimer les conditions de franchissement des transitions et des actions conditionnelles ainsi que les expressions logiques et arithmétiques assi-gnées aux actions stockées. Cependant, de nombreux travaux se sont penchés sur la transformation de spécifications Grafcet (y compris les expressions) en code de contrôle pour systèmes embar-qués. Pour faciliter l'édition de modèles Grafcet valides et la génération du code de contrôle, il est judicieux de proposer une formalisation du langage des expressions Grafcet, permettant de valider ses constructions et d'en pourvoir une sémantique appropriée. Pour cela, nous proposons une gram-maire hors-contexte qui génère tout l'ensemble des expressions Grafcet, en étendant les grammaires usuelles des expressions arithmétiques et logiques. Nous proposons également un métamodèle et une sémantique associée des expressions Grafcet pour faciliter la mise en oeuvre du langage Grafcet sous la forme d'un parseur des expressions Grafcet G7Expr obtenu grce au générateur d'analyseurs syntaxiques ANTLR, alors que le métamodèle est mis en oeuvre dans l'environnement d'Ingénie-rie Dirigée par les Modèles (IDM) Eclipse EMF. L'association des deux outils permet d'analyser et de construire automatiquement les expressions Grafcet lors de l'édition et la synthèse des modèles Grafcet.

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

scholarly journals Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Witold Dyrka ◽  
Marlena Gąsior-Głogowska ◽  
Monika Szefczyk ◽  
Natalia Szulc
Keyword(s):  
Functional Relationship ◽  
High Sensitivity ◽  
Alternative Methods ◽  
Discriminative Power ◽  
Context Free Grammar ◽  
Protein Motifs ◽  
Functional Features ◽  
Universal Grammars ◽  
Context Free ◽  
Probabilistic Context

Abstract Background Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs. Results First, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship. Conclusions While the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

A generalization of the concept of a context-free grammar

Cybernetics ◽  
1974 ◽  
Vol 8 (3) ◽  
pp. 349-351
Author(s):  
A. A. Letichevskii
Keyword(s):  
Context Free Grammar ◽  
Context Free

scholarly journals Knowledge Sources for Constituent Parsing of German, a Morphologically Rich and Less-Configurational Language

2013 ◽  
Vol 39 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Alexander Fraser ◽  
Helmut Schmid ◽  
Richárd Farkas ◽  
Renjing Wang ◽  
Hinrich Schütze
Keyword(s):  
State Of The Art ◽  
Lessons Learned ◽  
Knowledge Sources ◽  
Lexical Knowledge ◽  
Context Free Grammar ◽  
The Impact ◽  
Context Free ◽  
Probabilistic Context

We study constituent parsing of German, a morphologically rich and less-configurational language. We use a probabilistic context-free grammar treebank grammar that has been adapted to the morphologically rich properties of German by markovization and special features added to its productions. We evaluate the impact of adding lexical knowledge. Then we examine both monolingual and bilingual approaches to parse reranking. Our reranking parser is the new state of the art in constituency parsing of the TIGER Treebank. We perform an analysis, concluding with lessons learned, which apply to parsing other morphologically rich and less-configurational languages.

scholarly journals Context-free grammar forms with strict interpretations

1980 ◽  
Vol 21 (1) ◽  
pp. 110-135 ◽  
Author(s):  
H.A. Maurer ◽  
A. Salomaa ◽  
D. Wood
Keyword(s):  
Context Free Grammar ◽  
Context Free
Sign in / Sign up

Export Citation Format

Share Document