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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Sensitive fire sensors are necessary to detect fires in their early stage, but they often produce false alarms from non-fire phenomena. It has lately became clear that fire and non-fire patterns exist as a result of analyzing fire and non-fire testing data. Changing the rate of sensor output per minute and normalized sensor output are used as patterns for analyzing sensors' data in various condition. An odor sensor and a smoke sensor are very effective in discriminating between fire / non-fire phenomena by adopting artificial neural net which has been trained to recognize their patterns.

Design Of Neural Net To Detect Early Stage Of Fire And Evaluation By Using Real Sensors' Data