This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on a two time-scale forecasting models: a daily model is based on a ARIMA time series, while the 10-minute model is based on distributing the daily flow using a 10-minute demand pattern. The demand patterns has been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network providing very good results.
It is shown that, by the use of the Hilbert transform, it is possible to obtain values of the imaginary component, x"( omega ), of the AC magnetic susceptibility of a colloidal suspension of single-domain magnetic particles, from experimental measurements of the real component, x'( omega ).
We demonstrate the loading of very short optical pulses into a high-Q cavity with linewidth much narrower than the pulse frequency envelope. We show that loading into the cavity is significantly enhanced if the pulse is combined with a cw-field, thus altering the pulse frequency profile to better match the cavity profile.
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