Abstract: As one of the most important equipment in the power system, it is of great significance to conduct fault diagnosis research on transformers. Aiming at the problem of difficult selection of ...
Abstract: Monotone missing data is a common problem in data analysis. However, imputation combined with dimensionality reduction can be computationally expensive, especially with the increasing size ...
Inside living cells, mitochondria divide, lysosomes travel, and synaptic vesicles pulse—all in three dimensions (3Ds) and constant motion. Capturing these events with clarity is vital not just for ...
Nuclear imaging for industrial process analysis and non-destructive component testing has been around for longtime, but progression and innovation in this field has been limited and not as advanced ...
Principal component analysis (PCA) is one of the most common exploratory data analysis techniques with applications in outlier detection, dimensionality reduction, graphical clustering, and ...
Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust ...
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