A Bayesian Approach for Fault Location in Medium Voltage Grids With Underground Cables

Xiang, Yu; Cobben, J.F.G.

doi:10.1109/jpets.2015.2477598

Cited by 12 publications

(2 citation statements)

References 21 publications

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“…In order to solve this problem, we introduced a new model-variational autoencoder [25,26]. Although the model is also trained to train encoders and decoders, the essence of the encoder in this model is used to calculate the mean and variance in the normal distribution, so two encoders are generated for the mean and variance, which is essentially based on the conventional autoencoder.…”

Section: Variational Auto-encodermentioning

confidence: 99%

Deep Anomaly Detection Based on Variational Deviation Network

Wang

Wei

et al. 2022

Future Internet

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There is relatively little research on deep learning for anomaly detection within the field of deep learning. Existing deep anomaly detection methods focus on the learning of feature reconstruction, but such methods mainly learn new feature representations, and the new features do not fully reflect the original features, leading to inaccurate anomaly scores; in addition, there is an end-to-end deep anomaly detection algorithm, but the method cannot accurately obtain a reference score that matches the data themselves. In addition, in most practical scenarios, the data are unlabeled, and there exist some datasets with labels, but the confidence and accuracy of the labels are very low, resulting in inaccurate results when put into the model, which makes them often designed for unsupervised learning, and thus in such algorithms, the prior knowledge of known anomalous data is often not used to optimize the anomaly scores. To address the two problems raised above, this paper proposes a new anomaly detection model that learns anomaly scores mainly through a variational deviation network (i.e., not by learning the reconstruction error of new features, distance metrics, or random generation, but by learning the normal distribution of normal data). In this model, we force the anomaly scores to deviate significantly from the normal data by a small amount of anomalous data and a reference score generated by variational self-encoding. The experimental results in multiple classes of data show that the new variational deviation network proposed in this paper has higher accuracy among the mainstream anomaly detection algorithms.

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Section: Variational Auto-encodermentioning

confidence: 99%

Deep Anomaly Detection Based on Variational Deviation Network

Wang

Wei

et al. 2022

Future Internet

View full text Add to dashboard Cite

show abstract

“…Applying deterministic approach to calculate this high-dimensional integral is almost impossible. Thus, Monte Carlo integration should be applied with independent importance sampling [36], [37]. Since the parameters in G c are not dependent on each other, it is viable to sample each component independently and merge them together.…”

Section: Monte Carlo Integration With Independent Importance Samplingmentioning

confidence: 99%