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使用分布式光纤传感器进行智能模式识别,用于智能环境.

Brian Pamukti1, Shofuro Afifah1, Shien-Kuei Liaw1,2

  • 1Graduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.

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|January 11, 2025
PubMed
概括
此摘要是机器生成的。

分布式光纤传感器 (DFOS) 提供先进的入侵检测. 一种新的马赫-泽恩德干扰仪 (MZI) 和时间森林神经网络 (TFNN) 方法提高了智能环境的准确性和效率.

关键词:
深度学习是一种深度学习.模式识别 模式识别 模式识别智能环境是一个智能环境.

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科学领域:

  • 光电子和传感器技术
  • 人工智能和机器学习
  • 网络安全和监控

背景情况:

  • 分布式光纤传感器 (DFOS) 对于敏感区域的入侵检测至关重要.
  • 传统方法面临复杂性,计算负载和信号路径低效的挑战.
  • 需要在信号处理和深度学习方面取得进展,以提高DFOS性能.

研究的目的:

  • 为入侵检测引入一种创新的干扰度传感方法.
  • 为了提高DFOS系统的准确性和效率.
  • 探索马赫-泽恩德干扰仪 (MZI) 与时间森林神经网络 (TFNN) 结合的应用.

主要方法:

  • 使用马赫-泽恩德干扰仪 (MZI) 来获取信号.
  • 使用时间森林神经网络 (TFNN) 进行模式识别和入侵检测.
  • 与传统的一维卷积神经网络 (1D-CNN) 的性能比较.

主要成果:

  • 拟议的MZI-TFNN方法在入侵检测方面表现出卓越的性能.
  • 与1D-CNN相比,获得了8.43%的更高准确度.
  • 在实时信号处理中表示提高效率.

结论:

  • 使用TFNN的新型干扰度传感方法对现有的DFOS技术提供了显著的改进.
  • 这种方法在智能环境中具有实时入侵检测的巨大潜力.
  • 强调将先进的光学传感与深度学习相结合的有效性,以加强安全应用.