Applying self-normalizing neural networks to tackle data-driven soft sensing problems in manufacturing lines. Abstract: In this paper we present the results ...

The challenge is about classifying soft sensing data and detecting different tasks. The data for this challenge has been collected over 92 weeks by Seagate ...

Applying self-normalizing neural networks to tackle data-driven soft sensing problems in manufacturing lines · Ali Abdin · Published in IEEE International ...

In this paper, a new deep learning approach is proposed for soft sensors, which is based on the stacked enhanced auto-encoder (SEAE). The original stacked auto- ...

Read Applying self-normalizing neural networks to tackle data-driven soft sensing problems in manufacturing lines.

Oct 17, 2023 · Shankar Gangisetty · View · Applying self-normalizing neural networks to tackle data-driven soft sensing problems in manufacturing lines.

Jun 8, 2017 · We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations. While batch normalization requires explicit ...

One solution is adapting Extreme Learning Machines (Huang et al., 2006) — a feed-forward neural network that does not rely on gradient-based backpropagation — ...

Missing: tackle manufacturing

Nov 12, 2021 · To tackle this problem, we leverage the self-attention mechanism to exploit the correlations between sensors, i.e. learn latent correlations.