Knowledge Representation Using High-Level Fuzzy Petri Nets

Shen, Victor R.L.

doi:10.1109/tsmca.2006.878968

Cited by 62 publications

(33 citation statements)

References 23 publications

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“…In this sub-section, we briefly review fuzzy reasoning algorithm (FRA) from [8] to determine whether there exists a fuzzy relational matrix between the antecedent and the consequent of a fuzzy production rule or not. w } is a fuzzy set for the consequent; and each element of a fuzzy set is denoted by a fuzzy interval.…”

Section: Fuzzy Reasoning Algorithmmentioning

confidence: 99%

A Code Plagiarism Detection System Based on Abstract Syntax Tree and a High Level Fuzzy Petri Net

Shen¹,

Putri²

2017

dtmse

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syntax tree, High-level fuzzy petri net.Abstract. Students, who majored in computer science, are required to design and code in a variety of programming languages. However, many students submit the source code assignments they get from the Internet or friends with no or little modification. This paper proposes a novel method of detecting the source code plagiarism by using a high-level fuzzy Petri net (HLFPN) based on abstract syntax trees (AST). The experimental results indicate that we are directed to make better determination to detect the code plagiarism.

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Section: Fuzzy Reasoning Algorithmmentioning

confidence: 99%

A Code Plagiarism Detection System Based on Abstract Syntax Tree and a High Level Fuzzy Petri Net

Shen¹,

Putri²

2017

dtmse

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“…In this section, we briefly review Signal Vector Magnitude and some basic definitions of HLFPN [4] , [5] , [6] .…”

Section: Preliminariesmentioning

confidence: 99%

Application of High-Level Fuzzy Petri Nets to fall detection system using smartphone

Shen

Lai

2013

2013 International Conference on Machine Learning and Cybernetics

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The coming of aging society signifies that falling down has become one of the very important issues in global public health.The smart phone, which is more commonly used than human fall detection devices, was selected as a mobile device of human fall detection and a fall detection algorithm was developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for fall detection. The signals detected by the tri-axial G-sensor adopted were converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition.The Z-axis data were captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use were used as input parameters. High-Level Fuzzy Petri Net was used for analysis and development to identify walking, exercising and falling down.The result of this study may be used in relevant equipment or the field of home nursing.

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“…The following approaches are available to examine attributes such as the reachability trees for restriction, accessibility, and activity [13]; the simplified technologies for boundedness, accessibility, and activity; the state equations for accessibility [21]; and the correlational matrices for activity [25].…”

Section: Petri Netmentioning

confidence: 99%

Analysis of the problem description in the Algorithm for Inventive Problem Solving using Petri nets

Shen

Yang²,

Lee

2012

Knowledge-Based Systems

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Knowledge Representation Using High-Level Fuzzy Petri Nets

Cited by 62 publications

References 23 publications

A Code Plagiarism Detection System Based on Abstract Syntax Tree and a High Level Fuzzy Petri Net

A Code Plagiarism Detection System Based on Abstract Syntax Tree and a High Level Fuzzy Petri Net

Application of High-Level Fuzzy Petri Nets to fall detection system using smartphone

Analysis of the problem description in the Algorithm for Inventive Problem Solving using Petri nets

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