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Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which...
For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able...
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This transient, called coup de fouet (whipcrack), is common to all lead-acid batteries discharged at high rates. In most applications, the transient has no effect on the battery system's performance. The areas of concern are systems that have low-voltage cutouts or that have digital ...
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gradually throughout the reconstruction layers to recover the original signals. The effectiveness of the approach is evaluated by using the data from Dowling Hall footbridge. The network exhibits the good capability of lost data recovery, even when the signals have severe data loss proportion up to...
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To address the limitations of traditional coal pillar design methods in engineering practice for deep retreat mining roadways, this research focuses on the retreat mining face at the Pinggang Coal Mine in Heilongjiang Province. Combining mechanical exper