EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.

ABSENCE OF STABILIZATION POINT OF THE SPECIES ACCUMULATION CURVE IN TROPICAL FORESTS

The definition of the sample size is a major problem in studies of phytosociology. The species accumulation curve is used to define the sampling sufficiency, but this method presents some limitations such as the absence of a stabilization point that can be objectively determined and the arbitrariness of the order of sampling units in the curve. A solution to this problem is the use of randomization procedures, e. g. permutation, for obtaining a mean species accumulation curve and empiric confidence intervals. However, the randomization process emphasizes the asymptotical character of the curve. Moreover, the inexistence of an inflection point in the curve makes it impossible to define objectively the point of optimum sample size.