Studies from University of Science and Technology Beijing in the Area of Machine Learning Described (Crack Pattern Identification In Cementitious Materials Base d On Acoustic Emission and Machine Learning)

描述了北京科技大学在机器学习领域的研究(基于声发射和机器学习的水泥基材料裂纹模式识别)

Robotics & Machine Learning Daily News2024，Issue(Jun.19) ：109-110.

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Studies from University of Science and Technology Beijing in the Area of Machine Learning Described (Crack Pattern Identification In Cementitious Materials Base d On Acoustic Emission and Machine Learning)

描述了北京科技大学在机器学习领域的研究(基于声发射和机器学习的水泥基材料裂纹模式识别)

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摘要

一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。据《中国人民日报北京消息》报道，NewsRx记者的研究表明：“胶凝材料破坏前的开裂形态与(AE)监测信号的声发射密切相关，传统的上升角平均频率分析方法依靠经验判断来确定边界线，缺乏有效的自动识别方法来区分拉伸、剪切和混合裂纹类型。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Beijing, People's Repu blic of China, by NewsRx correspondents, research stated, "The cracking patterns in cementitious materials before failure are closely related to acoustic emissi on (AE) monitoring signals. Traditional rise angle -average frequency analysis m ethods rely on empirical judgment for boundary line determination, lacking effec tive automated recognition methods to distinguish between tensile, shear, and mi xed crack types."

Key words

Beijing/People's Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning/Support Vector Machines/Un iversity of Science and Technology Beijing

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出版年

2024

Robotics & Machine Learning Daily News

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