Raven Announces Strategic Integration of Augmenta, Enhances Automated Technology Portfolio

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CNH Industrial acquires machine vision company, adding key components to Raven’s autonomous farming technology development

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Merging bodily area knowledge with AI enhances prediction precision of battery ability — ScienceDaily

A short while ago, electrical autos (EVs) are found all over the place, from passenger cars and trucks, buses, to taxis. EVs have the benefit of being eco-welcoming and owning low upkeep costs but their proprietors should stay wary of fatal incidents in scenario the battery runs out or reaches the stop of its life. Consequently, exact capability and lifespan predictions for the lithium-ion batteries — usually applied in EVs — are essential.

A POSTECH analysis workforce led by Professor Seungchul Lee, and Ph.D. prospect Sung Wook Kim (Department of Mechanical Engineering) collaborated with Professor Ki-Yong Oh of Hanyang College to create a novel artificial intelligence (AI) know-how that can correctly forecast the ability and lifespan of lithium-ion batteries. This analysis breakthrough, which noticeably improved the prediction precision by merging physical area understanding with AI, has lately been posted in Utilized Power, an intercontinental educational journal in the electricity industry.

There are two strategies of predicting the battery capacity: a physics-dependent design, which simplifies the intricate inside composition of batteries, and an AI design, which makes use of the electrical and mechanical responses of batteries. On the other hand, the regular AI product expected significant amounts of info for

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