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dc.contributor.authorMandal, S.
dc.date.accessioned2009-01-21T11:16:54Z
dc.date.available2009-01-21T11:16:54Z
dc.date.issued1996
dc.identifier.citationInternational Conference in Ocean Engineering COE '96, 17-20 Dec 1996. Proceedings, 237-242p.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/2254
dc.description.abstractThe spectral analysis of measured short waves can efficiently be carried out by the fast Fourier transform technique. Even though many present techniques can be used for the simulation of time series waves, these may not provide accurate and computationally efficient results for specific applications. Therefore, an alternate method is described here to provide spectral relationships with manimum loss of information. The measured short waves are expressed by the parametric models namely, authregressive or auto-regressive moving average algorithms which are the polynomical functions. Further reduced order autoregressive moving average spectral estimations are successfully applied to time series waves with different sampling rates. This paper emphasises the importance of selecting a suitable sampling interval for better estimates of parametric modelling and also for better statistical representation. Implementation of the above algorithms in a structural monitoring system has the potential advantage of storing more representations of wave environment than is currently possible because of the reduced size of the data sets.
dc.language.isoen
dc.publisherAllied, Chennai (India)
dc.rightsCopyright [1996]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository.
dc.subjectoffshore engineering
dc.subjectwaveform analysis
dc.subjectspectral analysis
dc.subjectfourier transforms
dc.subjectwave measurement
dc.subjecttime series
dc.subjectshort-term records
dc.subjectautomated recording
dc.subjectalgorithms
dc.subjectmonitoring systems
dc.subjectmodelling
dc.titleOptimal parametric modelling of measured short waves
dc.typeConference Article


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