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dc.contributor.authorPaulraj, Murugesa Pandiyan, Prof. Dr.
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.contributor.authorMohd Shukry, Abdul Majid, Dr.
dc.contributor.authorKrishnan, Pranesh
dc.date.accessioned2014-06-06T02:31:11Z
dc.date.available2014-06-06T02:31:11Z
dc.date.issued2013-01
dc.identifier.citationp. 545-549en_US
dc.identifier.isbn978-1-4673-4359-6
dc.identifier.issnhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6481214
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/35127
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6481214
dc.descriptionProceeding of the 7th International Conference on Intelligent Systems and Control (ISCO) 2013 at Coimbatore, Tamilnadu, India on 4 January 2013 through 5 January 2013. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp?tag=1en_US
dc.description.abstractThis paper discusses the application of frame energy based DFT spectral band features for the detection of damages in steel plates. A simple experimental model is devised to suspend the steel plates in a free-free condition. Experimental modal analysis methods are analyzed and protocols are formed to capture vibration signals from the steel plate using accelerometers when subjected to external impulse. Algorithms based on frame energy based DFT spectral band feature extraction are developed and prominent features are extracted. A Probabilistic Neural Network is modeled to classify the condition of the steel plate. The output of the network model is validated using Falhman testing criterion and the results are compared.en_US
dc.language.isoenen_US
dc.publisherIEEE Conference Publicationsen_US
dc.relation.ispartofseriesProceeding of The 7th International Conference on Intelligent Systems and Control (ISCO 2013);
dc.subjectDFT spectral banden_US
dc.subjectDiscrete cosine transformationen_US
dc.subjectExperimental modal analysisen_US
dc.subjectFalhman criterionen_US
dc.subjectFrame energyen_US
dc.subjectProbabilistic neural networken_US
dc.subjectStructural health monitoringen_US
dc.titleSteel plate damage diagnosis using probabilistic neural networken_US
dc.typeWorking Paperen_US
dc.identifier.urlhttp://dx.doi.org/10.1109/ISCO.2013.6481214
dc.contributor.urlpaul@unimap.edu.myen_US
dc.contributor.urls.yaacob@unimap.edu.myen_US
dc.contributor.urlshukry@unimap.edu.myen_US


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