多通道分类器用于识别用phi-OTDR记录的声学冲击.
Ivan Alekseevich Barantsov1, Alexey Borisovich Pnev1, Kirill Igorevich Koshelev1
1Photonics and Infra-Red Technology Scientific Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一个用于周边安全的机器学习算法,准确地检测脚步 (98.3%) 并使用相位敏感光时反射计 (phi-OTDR) 传感器数据将其与背景噪声 (97.93%) 区分开来.
科学领域:
- 安全工程是安全工程.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 周边安全系统经常难以可靠地检测微妙的入侵信号,例如背景噪音中的脚步.
- 阶段敏感光学时间反射计 (phi-OTDR) 为周边监测提供了一个有前途的传感技术.
研究的目的:
- 开发一种改进的算法来分类声学冲击,以加强对未经授权的入侵的周边安全.
- 通过准确识别行走信号,提高周边检测工具的效率.
主要方法:
- 一个机器学习算法被开发使用数据集分类脚步与非脚步声学影响.
- 从phi-OTDR数据生成的时空图 (灰度图像) 的实时处理.
- 一个三通道神经网络分类器,结合denoised自编码器和自适应相关模型,被用于特征提取和分类.
主要成果:
- 该算法实现了98.3%的正确检测脚步的概率.
- 背景行动被正确分类,概率为97.93%.
- 该系统在区分弱步信号与高能背景噪声方面表现出有效性.
结论:
- 开发的算法显著提高了检测基于脚步的入侵者在外围安全中的准确性.
- 将phi-OTDR与先进的机器学习技术集成,为增强区域安全提供了强大的解决方案.
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