In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...
Abstract: The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these ...
The control of infinite-dimensional rigid-flexible robotic arms presents significant challenges, with direct truncation of first-order modal models resulting in poor control quality and second-order ...
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery
Abstract: As an unsupervised dimensionality reduction method, the principal component analysis (PCA) has been widely considered as an efficient and effective preprocessing step for hyperspectral image ...
1 College of Information Science and Technology, Jinan University, Guangzhou, China. 2 University of Birmingham Joint Institute, Jinan University, Guangzhou, China. Text classification is an essential ...
We present a systematic investigation of the dynamic evolution of Li interfaces via operando visualization tools. In situ AFM and SECM enable mapping the morphological evolution of the Li metal and ...
On Friday August 12, after months of political debate, the US House of Representatives approved the Inflation Reduction Act of 2022—or IRA—a week after the US Senate had done the same. The IRA is a ...
Several open resource toolboxes provide feature selection algorithms to decrease redundant features, data dimensionality, and computing costs. These approaches demand programming expertise, limiting ...
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