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 foreign convention on Adaptive and normal Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers awarded have been conscientiously reviewed and chosen from a complete of one hundred forty four submissions. the second one quantity comprises forty-one papers prepared in topical sections on development reputation and studying, smooth computing, platforms concept, help vector machines, and bioinformatics.
Read Online or Download Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II PDF
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Extra resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II
A 3-fold cross validation was employed to the data set in order to evaluate the tests performance. The results are reported in Table 1. To assess the tests performance accuracies, macro-average and micro-average were used. On the latter, the F-measure performance is calculated in two diﬀerent ways: a) the mean value of every F-scores computed for each binary problem (macro-average); b) the global F-measure, calculated from a global confusion matrix computed from the sum of all the confusion matrices related to the binary problems (micro-average).
Yet, for transient identiﬁcation a high sampling rate is needed. Since both steady-state and transient signatures have their own limitations, considering both for a study of a joint ID is interesting. Such was considered by Chang et al. in , very recently. The following section describes our approach. 3 Steady-States (StS) Recognition: Proposed Approach Electrical signatures are the main component of a NILM system. Usually individual load identiﬁcation uses transients and steady-states (StS) signatures.
B. Figueiredo, A. de Almeida, and B. Ribeiro Fig. 3. 1 Data Collection and Feature Extraction The data, namely, the active power, voltage, current and power factor signals were acquired using a sensing meter prototype provided by ISA-Intelligent Sensing Anywhere . However, for monitoring several parameters this prototype has a severe limitation for monitoring several parameters: only one parameter value can be supplied at each point in time. This implies the existence of a delay between the values of diﬀerent parameter types.