Studies from National Research Council (CNR) Yield New Data on Machine Learning (Machine Learning Unveils the Physical Properties of Materials Driving Thermoele ctric Generator Efficiency: the Case of Half-heuslers)

美国国家研究委员会(CNR)的研究产生了机器学习的新数据(机器学习揭示了驱动热电发电机效率的材料的物理性质:半休斯勒案例)

Robotics & Machine Learning Daily News2024，Issue(Dec.6) ：6-7.

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Studies from National Research Council (CNR) Yield New Data on Machine Learning (Machine Learning Unveils the Physical Properties of Materials Driving Thermoele ctric Generator Efficiency: the Case of Half-heuslers)

美国国家研究委员会(CNR)的研究产生了机器学习的新数据(机器学习揭示了驱动热电发电机效率的材料的物理性质:半休斯勒案例)

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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新报告。根据新闻报道来自意大利博洛尼亚的NewsRx Edito RS的研究表明，“我们报告了一种基于机器学习(ML)的研究。”方法允许直接从五个参数米评估热电发电机(TEG)的效率：两个物理特性载体密度和能隙，三个发动机运转参数外部负载电阻、三甘醇热侧温度和腿部高度。然后，我们使用遗传算法来优化这些使T EG效率最大化的参数。

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 reportingfrom Bologna, Italy, by NewsRx edito rs, the research stated, “We report a machine learning (ML)-basedapproach allow ing thermoelectric generator (TEG) efficiency evaluation directly from five para meters:two physical properties-carrier density and energy gap, and three engine ering parameters-external loadresistance, TEG hot side temperature, and leg hei ght. Then, we use a genetic algorithm to optimize theseparameters to maximize T EG efficiency.”

Key words

Bologna/Italy/Europe/Cyborgs/Emergin g Technologies/Engineering/Machine Learning/National Research Council (CNR)

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出版年

2024

Robotics & Machine Learning Daily News

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