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

Consistency-based diagnosis is an established approach to diagnose technicalapplications, but suffers from significant modeling efforts, especially fordynamic multi-modal time series. Machine learning seems to be an obvioussolution, which becomes less obvious when looking at details: Which notion ofconsistency can be used? If logical calculi are still to be used, how candynamic time series be transferred into the discrete world?  This paper presents the methodology Discret2Di for automated learning oflogical expressions for consistency-based diagnosis. While these logicalcalculi have advantages by providing a clear notion of consistency, they havethe key problem of relying on a discretization of the dynamic system. Thesolution presented combines machine learning from both the time series and thesymbolic domain to automate the learning of logical rules for consistency-baseddiagnosis.

Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis