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人工智能驱动的传感技术:审查

Long Chen1, Chenbin Xia1, Zhehui Zhao1

  • 1Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
概括

机器学习和深度学习增强了传感器技术,提高了准确性,并使工程和生物医学领域的新应用成为可能. 这种整合推动了传感器设计,性能和预测能力方面的创新.

科学领域:

  • 传感器技术 传感器技术
  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 机器学习 (ML) 和深度学习 (DL) 正在彻底改变传感技术.
  • 这些进步显著提高了传感器的精度,灵敏度和适应性.
  • 人工智能 (AI) 与传感器的整合是进步的关键驱动力.

研究的目的:

  • 审查ML/DL算法与传感器技术的融合.
  • 突出AI对传感器设计,校准和性能的影响.
  • 展示AI驱动传感的新型应用和未来趋势.

主要方法:

  • 对传感器技术中的ML/DL当前文献的综述.
  • 对用于传感器增强的AI算法集成进行分析.
  • 检查各种领域的示范应用程序.

主要成果:

  • 人工智能算法显著升级传感器功能和性能.
  • ML/DL使传感器校准,补偿和对象识别方面的进步成为可能.
  • 在工业自动化,机器人技术和生物医学工程领域,新应用正在出现.
关键词:
由人工智能驱动的传感应用程序在ML/DL算法中,我们使用了ML/DL算法性能提升 性能提升 性能提升传感技术的感应技术.

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结论:

  • 人工智能和传感器之间的协同作用提供了变革性的潜力.
  • 应对当前的挑战将释放传感能力的进一步进步.
  • 未来的趋势指向更智能,更适应性的传感器系统.