The process of engineering analysis, especially its preprocessing stage, comprises some knowledge-based tasks which influence the quality of the results greatly, require considerable level of expertise from an engineer; the support for these tasks by the contemporary CAE systems is limited. Analysis of the knowledge and reasoning involved in solving these tasks shows that the appropriate support for them by an automated system can be implemented using case-based reasoning (CBR) technology and ontological knowledge representation model. In this paper the knowledge-based system for intelligent support of the preprocessing stage of engineering analysis in the contact mechanics domain is presented which employs the CBR mechanism. The knowledge representation model is formally represented by the OWL DL ontology. Case representation model, case retrieval and adaptation algorithms for this model and the automated system are described.
2008 (1)
Peter Wriggers, Marina Siplivaya, Irina Joukova, Roman Slivin (2008)
Intelligent support of the preprocessing stage of engineering analysis using case-based reasoning.
Engineering with Computers 24 (4) pp. 383–404.
doi web bibtex @Article{Wriggers2008,
author="Wriggers, Peter
and Siplivaya, Marina
and Joukova, Irina
and Slivin, Roman",
title="Intelligent support of the preprocessing stage of engineering analysis using case-based reasoning",
journal="Engineering with Computers",
year="2008",
volume="24",
number="4",
pages="383--404",
abstract="The process of engineering analysis, especially its preprocessing stage, comprises some knowledge-based tasks which influence the quality of the results greatly, require considerable level of expertise from an engineer; the support for these tasks by the contemporary CAE systems is limited. Analysis of the knowledge and reasoning involved in solving these tasks shows that the appropriate support for them by an automated system can be implemented using case-based reasoning (CBR) technology. In this paper the automated knowledge-based system for intelligent support of the preprocessing stage of engineering analysis in the contact mechanics domain is presented which employs the CBR mechanism. The case representation model is proposed which is centered on the structured qualitative model of a technical object. The model is formally represented by the Ontology Web Language Description Logics (OWL DL) ontology. Case retrieval and adaptation algorithms for this model are described which according to the initial tests perform better in the chosen domain then the known prototypes. The automated system is described and a sample problem-solving scenario from the contact mechanics domain is presented. Use of such system can potentially lower costs of engineering analysis by reducing the number of inappropriate decisions and analysis iterations and facilitate knowledge transfer from research into industry.",
issn="1435-5663",
doi="10.1007/s00366-007-0079-5",
url="http://dx.doi.org/10.1007/s00366-007-0079-5"
}
Abstract The process of engineering analysis, especially its preprocessing stage, comprises some knowledge-based tasks which influence the quality of the results greatly, require considerable level of expertise from an engineer; the support for these tasks by the contemporary CAE systems is limited. Analysis of the knowledge and reasoning involved in solving these tasks shows that the appropriate support for them by an automated system can be implemented using case-based reasoning (CBR) technology. In this paper the automated knowledge-based system for intelligent support of the preprocessing stage of engineering analysis in the contact mechanics domain is presented which employs the CBR mechanism. The case representation model is proposed which is centered on the structured qualitative model of a technical object. The model is formally represented by the Ontology Web Language Description Logics (OWL DL) ontology. Case retrieval and adaptation algorithms for this model are described which according to the initial tests perform better in the chosen domain then the known prototypes. The automated system is described and a sample problem-solving scenario from the contact mechanics domain is presented. Use of such system can potentially lower costs of engineering analysis by reducing the number of inappropriate decisions and analysis iterations and facilitate knowledge transfer from research into industry.
2007 (2)
Peter Wriggers, Marina Siplivaya, Irina Joukova, Roman Slivin (aug 2007)
Intelligent Support of Engineering Analysis Using Ontology and Case-based Reasoning.
Eng. Appl. Artif. Intell. 20 (5) pp. 709–720. Pergamon Press, Inc.. Tarrytown, NY, USA.
doi web bibtex @article{Wriggers:2007:ISE:1265605.1265719,
author = {Wriggers, Peter and Siplivaya, Marina and Joukova, Irina and Slivin, Roman},
title = {Intelligent Support of Engineering Analysis Using Ontology and Case-based Reasoning},
journal = {Eng. Appl. Artif. Intell.},
issue_date = {August, 2007},
volume = {20},
number = {5},
month = aug,
year = {2007},
issn = {0952-1976},
pages = {709--720},
numpages = {12},
url = {http://dx.doi.org/10.1016/j.engappai.2006.12.002},
doi = {10.1016/j.engappai.2006.12.002},
acmid = {1265719},
publisher = {Pergamon Press, Inc.},
address = {Tarrytown, NY, USA},
keywords = {Case-based reasoning, Engineering analysis, Intelligent support, Ontology},
}
P. Wriggers, M. Siplivaya, I. Zhukova, A. Kapysh, A. Kultsov (2007)
Integration of a case-based reasoning and an ontological knowledge base in the system of intelligent support of finite element analysis.
Computer Assisted Mechanics and Engineering Sciences Vol. 14, No. 4 pp. 753–765.
bibtex @article{WSZKK2007,
author="P. Wriggers and M. Siplivaya and I. Zhukova and A. Kapysh and A. Kultsov",
title = "Integration of a case-based reasoning and an ontological knowledge base in the system of intelligent support of finite element analysis",
journal = "Computer Assisted Mechanics and Engineering Sciences",
volume = "Vol. 14, No. 4",
year = "2007",
pages = "753--765",
}
Global aim: Conservation and development of the Elbe river floodplains in a
state, that the claims of water management, flood protection, agriculture,
fishery, nature conservation, tourism and navigation are answered at the
best possible rate.
Concept of CBR-based decision making developed;
general schema of integration of CBR, QR and ontology;
modified CBR-cycle supported with QR and ontology;
ontological knowledge representation model proposed as well as case representation and case qualitative models;
jRamwass system developed.
idss_ramwass.ppt
2014 (1)
Peter Wriggers, Marina Kultsova, Alexander Kapysh, Anton Kultsov, Irina Zhukova (2014)
Intelligent Decision Support System for River Floodplain Management. In
Knowledge-Based Software Engineering: 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014. Proceedings. pp. 195–213. Springer International Publishing. Cham.
doi web bibtex @Inbook{Wriggers2014,
author="Wriggers, Peter
and Kultsova, Marina
and Kapysh, Alexander
and Kultsov, Anton
and Zhukova, Irina",
editor="Kravets, Alla
and Shcherbakov, Maxim
and Kultsova, Marina
and Iijima, Tadashi",
title="Intelligent Decision Support System for River Floodplain Management",
bookTitle="Knowledge-Based Software Engineering: 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014. Proceedings",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="195--213",
isbn="978-3-319-11854-3",
doi="10.1007/978-3-319-11854-3_18",
url="http://dx.doi.org/10.1007/978-3-319-11854-3_18"
}
A concept of intelligent support of decision making in waste management using knowledge-based approach was developed, which is a promising way to increase efficiency of waste management system in the cities. Analysis of the domain of waste management shows that the appropriate support of decision making can be implemented using contemporary technologies of artificial intelligent such as rule-based reasoning and ontology. In the paper a general scheme of the integration of this reasoning mechanism and ontology is suggested, as well the problems of domain knowledge representation are considered. Implementation of a prototype of intelligent decision support system in waste management using rule-based reasoning and ontology is described.
2016 (1)
M. Kultsova, R. Rudnev, A. Anikin, I. Zhukova (July 2016)
An ontology-based approach to intelligent support of decision making in waste management. In
2016 7th International Conference on Information, Intelligence, Systems Applications (IISA). pp. 1-6.
doi bibtex @INPROCEEDINGS{7785401,
author={M. Kultsova and R. Rudnev and A. Anikin and I. Zhukova},
booktitle={2016 7th International Conference on Information, Intelligence, Systems Applications (IISA)},
title={An ontology-based approach to intelligent support of decision making in waste management},
year={2016},
pages={1-6},
keywords={decision making;decision support systems;environmental science computing;inference mechanisms;ontologies (artificial intelligence);waste management;contemporary technologies;domain knowledge representation;intelligent decision making support;knowledge-based approach;ontology-based approach;rule-based reasoning;waste management system;Cognition;Decision making;Electronic mail;Knowledge based systems;Ontologies;Recycling;Waste management},
doi={10.1109/IISA.2016.7785401},
month={July},}