Elon Musk’s big promises apparently no longer seem to be enough for many Tesla investors.

Households will pay an average of $2,400 more for goods this year, thanks to Trump’s policies.

In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the latent ...

Complex industrial processes exhibit typical nonstationarity due to frequently fluctuating material flows and complex control loops. This poses three challenges for trustworthy process monitoring, ...

Deep learning models, with their excellent modeling capabilities for large-scale and nonlinear data, have played a significant role in the intelligent perception of quality-related variables.

Accurately and promptly extracting subsynchronous oscillation (SSO) components from measurements and locating SSO sources are crucial for SSO suppression. Existing transient energy flow (TEF) based ...

Conventional methods often struggle with spectral variability, complicating detection accuracy. This paper introduces spectral variability-aware hybrid autoencoder (SVHAE) for HUTD, a novel ...

Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice, it is ...

In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on training combined with ...

Accurate prediction of protein subcellular localization is critical for understanding cellular functions and guiding drug design. However, current computational methods have limited and insufficient ...

Research in emotion recognition using electroencephalography (EEG) has advanced rapidly, and affective EEG-based Brain-computer Interface (aBCI) technology is increasingly moving from lab research to ...