Download Adaptive and Natural Computing Algorithms: 10th by Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, PDF

By Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)

The two-volume set LNCS 6593 and 6594 constitutes the refereed court cases of the tenth overseas convention on Adaptive and ordinary Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers provided have been rigorously reviewed and chosen from a complete of a hundred and forty four submissions. the second one quantity contains forty-one papers geared up in topical sections on trend attractiveness and studying, tender computing, structures idea, aid vector machines, and bioinformatics.

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Additional resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II

Sample text

However, one of the drawbacks of StS is the fact that distinct appliances can present very similar signatures. B. Figueiredo, A. de Almeida, and B. Ribeiro an incorrect identification. This paper studies the incorporation of further signal information in order to enrich the electric profile of each appliance, namely from the reactive power signal and power factor measurements. The first step in the definition of a StS is the recognition of a stable value sequence in the sampled signal. In [9] the authors presented a method for the identification of a steady-state signature based in ratios between rectangular areas defined by the successive states values.

1. A NILM high-level system and approach for the device signature study a NILM system, usually these parameters result, either from signal steady-states or are obtained by sensing transients. A steady-state signature is deduced from the difference between two steadystates in a signal consisting of a stable set of consecutive samples, whose values are within a given threshold. The basic steady-state identifies the turning on and off of an electrical device connected to the network. To achieve this steady-state detection is much less demanding that what is required for capture and analysis of the transients.

The minimum number of consecutive samples needed to identify a stable state depends on the sampling frequency: when this is low, a small number of samples is enough, otherwise a bigger number is needed. For instance, with a sampling frequency of 1Hz, the minimum number of samples can be defined as three, which is the one used in [1], where other methods for steady-states recognition are proposed (namely, filtering, differentiating and peak detection). Consider a sequence of n consecutive sampling values, Y = {yi , i = 1, .

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