English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Monte Carlo dropout method has proved to be a scalable and easy-to-useapproach for estimating the uncertainty of deep neural network predictions.This approach was recently applied to Fault Detection and Di-agnosis (FDD)applications to improve the classification performance on incipient faults. Inthis paper, we propose a novel approach of augmenting the classification modelwith an additional unsupervised learning task. We justify our choice ofalgorithm design via an information-theoretical analysis. Our experimentalresults on three datasets from diverse application domains show that theproposed method leads to improved fault detection and diagnosis performance,especially on out-of-distribution examples including both incipient and unknownfaults.

Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults