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

This paper describes a pattern recognition approach aiming to estimate fuelcell duration time from electrochemical impedance spectroscopy measurements. Itconsists in first extracting features from both real and imaginary parts of theimpedance spectrum. A parametric model is considered in the case of the realpart, whereas regression model with latent variables is used in the lattercase. Then, a linear regression model using different subsets of extractedfeatures is used fo r the estimation of fuel cell time duration. Theperformances of the proposed approach are evaluated on experimental data set toshow its feasibility. This could lead to interesting perspectives forpredictive maintenance policy of fuel cell.

Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time