No abstract

No abstract

Electroencephalogram (EEG) signal presents a great potential for highly secure biometric systems due to its characteristics of universality, uniqueness, and natural robustness to spoofing attacks. EEG signals are measured by sensors placed in various positions of a person's head (channels). In this work, we address the problem of reducing the number of required sensors while maintaining a comparable performance. We evaluated a binary version of the Flower Pollination Algorithm under different transfer functions to select the best subset of channels that maximizes the accuracy, which is measured by means of the Optimum-Path Forest classifier. The experimental results show the proposed approach can make use of less than a half of the number of sensors while maintaining recognition rates up to 87%, which is crucial towards the effective use of EEG in biometric applications.

No abstract

According to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately U.S.$ 4 billion in commercial losses during 2011, which correspond to more than 27 000 GWh. The strengthening of the smart grid has brought a considerable amount of research that can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization. Index Terms-Commercial losses, black hole algorithm, optimum-path forest. I. INTRODUCTION T HE ENERGY losses are defined as the difference between the energy generated or purchased and the energy billed, being classified in two different types: technical and commercial losses. The former are related to the physical characteristics of the energy system, i.e., the technical losses are defined as the energy lost in the transportation, transformation and in the measuring equipments, being its costs predicted by the electric utilities [1]. The commercial losses, also called non-technical losses (NTL), are associated with the energy delivered to the consumer that is not billed, being more difficult to be detected and quantified.