Robotics & Machine Learning Daily News2024,Issue(Jun.28) :40-41.

Reports from Tongji University Provide New Insights into Machine Learning (Autoe ncoded Chemical Feature Interaction Machine Learning Method Boosting Performance of Piezoelectric Catalytic Process)

来自同济大学的报告为机器学习提供了新的见解(自动编码化学特征交互机器学习方法提高压电催化过程性能)

Robotics & Machine Learning Daily News2024,Issue(Jun.28) :40-41.

Reports from Tongji University Provide New Insights into Machine Learning (Autoe ncoded Chemical Feature Interaction Machine Learning Method Boosting Performance of Piezoelectric Catalytic Process)

来自同济大学的报告为机器学习提供了新的见解(自动编码化学特征交互机器学习方法提高压电催化过程性能)

扫码查看

摘要

机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据中国人民代表大会上海的新闻报道,NewsRx记者的研究表明:“压电催化工艺可以降低水处理过程中的能耗,但高性能压电材料的设计和操作参数的寻找仍然是一项具有挑战性的任务。”本研究的资金来源包括国家重点研究开发项目、上海市科技重大项目。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Shanghai, People’s Rep ublic of China, by NewsRx journalists, research stated, “Piezoelectric catalytic process can reduce energy consumption in water treatment processes. However, th e design of high-performance piezoelectric materials and the search for operatin g parameters are still challenging tasks.” Financial supporters for this research include National Key Research and Develop ment Program of China, Shanghai Municipal Science and Technology Major Project.

Key words

Shanghai/People’s Republic of China/As ia/Cyborgs/Emerging Technologies/Machine Learning/Tongji University

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
段落导航相关论文