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- W2949097908 abstract "In many real applications, the ground truths of class labels from voltage dip sequences used for training a voltage dip classification system are unknown, and require manual labelling by human experts. This paper proposes a novel deep active learning method for automatic labelling of voltage dip sequences used for the training process. We propose a novel deep active learning method, guided by a generative adversarial network (GAN), where the generator is formed by modelling data with a Gaussian mixture model and provides the estimated probability distribution function (pdf) where the query criterion of the deep active learning method is built upon. Furthermore, the discriminator is formed by a support vector machine (SVM). The proposed method has been tested on a voltage dip dataset (containing 916 dips) measured in a European country. The experiments have resulted in good performance (classification rate 83% and false alarm 3.2%), which have demonstrated the effectiveness of the proposed method." @default.
- W2949097908 created "2019-06-27" @default.
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- W2949097908 date "2019-06-01" @default.
- W2949097908 modified "2023-09-27" @default.
- W2949097908 title "Generative Adversarial Model-Guided Deep Active Learning for Voltage Dip Labelling" @default.
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- W2949097908 doi "https://doi.org/10.1109/ptc.2019.8810499" @default.
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