Predicting Safety Logic Device Solutions via Decision Trees and Rules Algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU137880" target="_blank" >RIV/00216305:26220/20:PU137880 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9257284" target="_blank" >https://ieeexplore.ieee.org/document/9257284</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCC49264.2020.9257284" target="_blank" >10.1109/ICCC49264.2020.9257284</a>
Alternative languages
Result language
angličtina
Original language name
Predicting Safety Logic Device Solutions via Decision Trees and Rules Algorithms
Original language description
Considering the extensive data sets and statistical techniques, a digital factory (plant) embodies a branch of machine learning that has an impact on machine safety. We propose a study based on an application of decision trees and rules algorithms (JRIP, J48, Random Forest, Random Tree, and PART). Our experimental data were collected from various industrial machine safety solutions. Diverse validation techniques were employed to derive the classification performance of each method; the approach is expected to simplify the user choice of a suitable safety logic device type. In this study, the overall classification methods proportion of individual safety logic device solutions were correctly assigned by using the training-evaluated test mode, and the prediction accuracy reached 100%; further, when assessing the 5-fold cross-validation test mode, we obtained the success rate of 82% (JRIP and PART). PART as the best method was correctly assigned for the 10-fold cross-validation test mode (85%). New developments within the broad province of machine learning, including the concepts characterized in our study, may facilitate effective assessment of machine safety systems.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2020 21st International Carpathian Control Conference (ICCC)
ISBN
978-1-7281-1951-9
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1-7
Publisher name
IEEE
Place of publication
High Tatras, Slovakia
Event location
High Tatras
Event date
Oct 27, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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