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Deep Learning with the Random Neural Network and its Applications
The random neural network (RNN) is a mathematical model for an "integrat...
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Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review
In this survey paper, we systematically summarize the current literature...
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Disease Classification in Metagenomics with 2D Embeddings and Deep Learning
Deep learning (DL) techniques have shown unprecedented success when appl...
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Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning
This paper presents a spatiotemporal unsupervised feature learning metho...
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Automatic crack detection and classification by exploiting statistical event descriptors for Deep Learning
In modern building infrastructures, the chance to devise adaptive and un...
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PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning
With the increase in the complexity of chip designs, VLSI physical desig...
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Adversarial Examples in Deep Learning for Multivariate Time Series Regression
Multivariate time series (MTS) regression tasks are common in many real-...
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Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification
This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.
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