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Currently, the majority of metal–oxide surge arrester (MOSA) monitoring techniques are based on total leakage current decomposition of their capacitive and resistive components. However, these techniques are subject to some financial, technical, and practical limitations, which can hamper their usage on the field. In this paper, a new monitoring technique for the zinc–oxide surge arrester is proposed...
This paper presents field results for a pollution estimation system based on ultrasound noise and Statistical AutoAssociative Artificial Neural Networks (SA³N²). The system extracts spectral information from the ultrasonic noise emitted by the corona discharges that occur nearby electric insulation, then correlates this information to a previously known pollution intensity situation. The entire acquisition...
In this paper we present an alternative approach for music genre classification which converts the audio signal into spectrograms and then extracts features from this visual representation. The idea is that treating the time-frequency representation as a texture image we can extract features to build reliable music genre classification systems. The proposed approach also takes into account a zoning...
In this work a monitoring and diagnostic technique for ZnO surge arresters is proposed. This technique is based on a special kind of Artificial Neural Network (ANN) known as Self-Organizing Maps (SOM), which is a network, trained using unsupervised learning. The proposed technique performs the thermal profile analysis of ZnO surge arresters when submitted to their operating voltage. From this analysis,...
This work introduces a hidden Markov model (HMM) based technique to classify agricultural crops. The method recognizes different crops by analyzing their spectral profiles over a sequence of satellite images. Different HMMs, one for each of the considered crop classes, are used to relate the varying spectral response along the crop cycles with plant phenology. The method assigns for a given image...
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