Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/2515
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Christodoulou, Constantinos | en |
dc.contributor.author | Demosthenous, Demos | en |
dc.contributor.author | Cleanthous, Aris | en |
dc.contributor.author | Neocleous, Costas | - |
dc.contributor.other | Νεοκλέους, Κώστας | - |
dc.date.accessioned | 2009-07-29T07:43:05Z | en |
dc.date.accessioned | 2013-05-17T05:30:03Z | - |
dc.date.accessioned | 2015-12-02T11:28:12Z | - |
dc.date.available | 2009-07-29T07:43:05Z | en |
dc.date.available | 2013-05-17T05:30:03Z | - |
dc.date.available | 2015-12-02T11:28:12Z | - |
dc.date.issued | 2007 | en |
dc.identifier.citation | proceedings of the 25th IASTED International Multi Conference on Artificial Intelligence and Applications, February 12-14, 2007, Innsbruck, Austria | en |
dc.identifier.isbn | 9780889866264 | en |
dc.description.abstract | The safety of gas cylinders in domestic applications is of utmost importance. A crucial stage in the appraisal of the risk for failures and possible faults is occurring during the filling process. In Cyprus, this task is currently done by specialized workers who monitor the cylinders during filling. In order to explore the possibility for an automated risk appraisal and consequent screening, a system of fault identification using vibrational time series and neural network classification has been used. Two systems have been attempted. One using a multi-slab feedforward neural structure employing backpropagation-type learning, and a Kohonen self-organizing map. The results were also compared with different simple statistical methods. The feedforward net, proved to be slightly better responding than the Kohonen map for this particular problem. | en |
dc.format | en | |
dc.language.iso | en | en |
dc.subject | Feedforward neural networks | en |
dc.subject | Kohonen network | en |
dc.subject | Gas cylinder | en |
dc.title | Neural networks for the identification of gas cylinder faults | en |
dc.type | Conference Papers | en |
dc.dept.handle | 123456789/54 | en |
item.fulltext | With Fulltext | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.openairetype | conferenceObject | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Department of Mechanical Engineering and Materials Science and Engineering | - |
crisitem.author.dept | Department of Mechanical Engineering and Materials Science and Engineering | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0001-9898-261X | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
neural networks.pdf | 658.61 kB | Adobe PDF | View/Open |
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