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 development of the manufacturing systems has made it increasinglynecessary to monitor the data generated by multiple interconnected subsystemswith rapid incoming of samples. Based on incremental Singular ValueDecomposition (ISVD), we develop a general online monitoring approach for therelationship of data generated from two interconnected subsystems, where eachsubsystem produces big data streams with several variation patterns in normalworking condition. When special situation happens and new associations occur, avery small amount of computation is sufficient to update the system status andcompute the control statistics by using this approach. The proposed methodreduces computational overhead and retains only a small number of pairs ofpossible dependent patterns at each step. The validation of the method throughsimulation studies and a case study on semiconductor manufacturing processesfurther supports its effectiveness.

Efficient online cross-covariance monitoring with incremental SVD: An approach for the detection of emerging dependency patterns in IoT systems