Data on Machine Learning Reported by Researchers at University of Southern Calif ornia (USC) (Damage Detection and Localization In Sealed Spent Nuclear Fuel Dry Storage Canisters Using Multi-task Machine Learning Classifiers)

南加州大学奥尼亚分校(USC)研究人员报告的机器学习数据（使用多任务机器学习分类器对密封乏核燃料干贮罐进行损伤检测和定位）

Robotics & Machine Learning Daily News2024，Issue(Dec.3) ：152-153.

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Data on Machine Learning Reported by Researchers at University of Southern Calif ornia (USC) (Damage Detection and Localization In Sealed Spent Nuclear Fuel Dry Storage Canisters Using Multi-task Machine Learning Classifiers)

南加州大学奥尼亚分校(USC)研究人员报告的机器学习数据（使用多任务机器学习分类器对密封乏核燃料干贮罐进行损伤检测和定位）

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-调查人员发布马学习的新报告。根据消息来源来自加利福尼亚州洛杉矶的NEWSRX记者，研究称，“乏核燃料(SNF)”由捆扎的放射性燃料棒组成的组件(FAs)储存在不锈钢容器中，作为临时干式存储选项，直到永久存储解决方案无法使用为止。意外损坏储罐可能在搬运或运输事件中出现。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news originatingfrom Los Angeles, California, by N ewsRx correspondents, research stated, “Spent nuclear fuel (SNF)assemblies (FAs ), composed of bundled radioactive fuel rods, are stored in stainless-steel cani sters as aninterim dry storage option until permanent storage solutions are ava ilable. Accidental damage to thesecanisters may occur during handling or transp ortation events.”

Key words

Los Angeles/California/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning/Uni versity of Southern California (USC)

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出版年

2024

Robotics & Machine Learning Daily News

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