Robotics & Machine Learning Daily News2024,Issue(Jul.2) :22-22.

Researchers from Wuhan University Report on Findings in Machine Learning (Pipeli ne Damage Identification In Nuclear Industry Using a Particle Swarm Optimization -enhanced Machine Learning Approach)

武汉大学的研究人员报告了机器学习的发现(使用粒子群优化增强机器学习方法的核工业Pipeli核损伤识别)

Robotics & Machine Learning Daily News2024,Issue(Jul.2) :22-22.

Researchers from Wuhan University Report on Findings in Machine Learning (Pipeli ne Damage Identification In Nuclear Industry Using a Particle Swarm Optimization -enhanced Machine Learning Approach)

武汉大学的研究人员报告了机器学习的发现(使用粒子群优化增强机器学习方法的核工业Pipeli核损伤识别)

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摘要

由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在已经可用。根据NewsRx记者在中国武汉的新闻报道,研究表明:“核工业管道的安全是至关重要的,因为它们容易受到各种环境因素的破坏。超声波导波检测技术具有效率高、成本低、方便等优点,是管道损伤检测中广泛采用的方法。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting from Wuhan, People’s Republic of China, by NewsRx journalists, research stated, “The safety of nuclear industry pipeline s is of paramount importance due to their vulnerability to damage from a variety of environmental factors. Ultrasonic guided wave inspection technology presents advantages such as high efficiency, low cost, and convenience, making it a wide ly adopted method for detecting damage in pipelines.”

Key words

Wuhan/People's Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning/Particle Swarm Optimization/Wuhan University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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