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传感器AI:用于传感器数据的机器学习框架
Stephen Coshatt1, He Yang1, Shushan Wu1
1The Center for Cyber-Physcial Systems, University of Georgia, Athens, GA 30602, USA.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
工程师需要机器学习 (ML) 和人工智能 (AI) 技能用于网络物理系统. 本研究提出了一个ML框架,用于训练和测试时间序列传感器数据的模型,以帮助学生研究人员.
科学领域:
- 网络物理系统工程 网络物理系统工程
- 机器学习应用 机器学习应用
- 信号处理 信号处理
背景情况:
- 将机器学习 (ML) 和人工智能 (AI) 集成到网络物理系统 (CPS) 中需要专门的工程知识.
- 工程师越来越需要了解适当的ML模型,以了解时间序列传感器数据和有效的信号处理技术.
- 乔治亚大学 (UGA) 的网络物理系统中心 (CCPS) 发现了学生研究人员的这些技能差距.
研究的目的:
- 开发一个实用的机器学习框架,适用于CPS内部的时间序列传感器数据分析.
- 为快速构建,培训和评估多个ML模型提供一个工具.
- 为学生研究人员提供教育资源,以掌握CPS的基本ML和信号处理概念.
主要方法:
- 开发一个通用的机器学习框架.
- 该框架应用于来自CCPS测试台的时间序列传感器数据.
- 促进快速模型建设,培训和测试.
主要成果:
- 对时间序列传感器数据的功能性ML框架的演示.
- 成功应用CCPS测试数据框架.
- 为学生研究人员创建一个有价值的教学工具.
结论:
- 开发的ML框架有效支持在CPS中分析时间序列传感器数据.
- 该框架作为一个可访问的教育工具,增强学生研究人员的理解和技能.
- 这个倡议解决了在网络物理系统领域对ML专业知识的关键需求.
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