By David Chinarro
This thesis tackles basic questions in regards to the discharge of a pre-Pyrenean karst aquifer process and an Antarctic glacier process, using a process engineering technique and data-driven strategy. It offers for the 1st time a simplified and powerful linear move functionality for karst aquifers. the writer offers distinctive wavelet spectrum effects, which show convinced non-linearities in drought classes. moreover, buildings in line with Hammerstein-Wiener blocks have yielded a nonlinear version that's considerably extra effective than its linear counterparts.
Another pioneering discovering is using wavelet coherence among glacier discharge and air temperature to estimate SEC (Seasonal potent middle) obstacles. the once a year SEC is key to acquiring a version in accordance with Hammerstein-Wiener constructions, which bargains significantly better potency. furthermore, kinds of glacier dynamics were stumbled on (over damped and overshoot), looking on the yearly cycle and the SEC normal temperature.
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Extra resources for System Engineering Applied to Fuenmayor Karst Aquifer (San Julián de Banzo, Huesca) and Collins Glacier (King George Island, Antarctica)
Example text
Some of these sensors require computing capability to integrate a distributed artificial intelligence which offers a wide range of possibilities for the operation, automation and control of different systems [5]. The Nyquist-Shannon criterion provides some clearly stated bounds, but it does not assure that a system can operate right at the Nyquist rate. So, some practical caveats should be taken in mind in designing a sampling process. The Nyquist criterion assumes: a sampling is regular (low noise); the value obtained for each sample should have an infinite precision (issue never happens in sampling natural system); there can be no component in the original signal higher than those correspond to half the sampling frequency; and the criterion does not take in account if (so prevalent in system identification) the signal is going to be processed (modified) after sampling [6].
2 Choosing Wavelet Function One singular characteristic of wavelet analysis is the arbitrary choice of the wavelet function. The below list is based on factors given by Torrence and Compo [43], in order to select a suitable wavelet to get best performance, and has been completed with other considerations by the author of the thesis: (a) Discrete or continuous. DWT provides a more compact representation of data. So, DWT is rather suited for image processing, signal coding, noise reduction and computer vision.
1. 2). 2). 8) relies on a model previously defined by a set of parameters that must be calculated to accomplish a given quality criteria. 2). 3). In the time domain Sect. 2). 2). 9). Finally, in the conclusion Sect. 10, important assertions related to this chapter are offered. 2 Time Series To capture critical information about the processes to be investigated, field data are achieved through the sensor network. Process variables should be sampled for a duration and sampling frequency enough to obtain those quality time series that the 14 2 System Identification Techniques analysis requires.
