半监督学习自动编码器用于分布式光纤声学传感器中的事件分类
Artem Kozmin1, Oleg Kalashev2, Alexey Chernenko2
1The Artificial Intelligence Research Center, Novosibirsk State University, Pirogova 1, Novosibirsk 630090, Russia.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了使用分布式声传感器的基础设施安全系统的半监督学习方法. 它减少了对标记数据的需求,降低了成本,提高了事件分类效率.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 基础设施安全系统越来越依赖于分布式声学传感 (DAS) 进行威胁检测.
- 标记数据集创建的高成本和复杂的信号处理阻碍了DAS系统的广泛部署.
- 准确的事件分类和定位对于有效的周边监控至关重要.
研究的目的:
- 开发一种增强的半监督学习方法,用于基于DAS的基础设施安全事件分类.
- 减少对广泛标记数据集的依赖,从而降低部署成本.
- 提高实时事件检测和分类的准确性和效率.
主要方法:
- 为特征提取和事件分类提出了一种混合自编码器-分类器架构.
- 自动编码器利用未标记的数据来学习有意义的数据表示.
- 一个集成的损失函数引导自动编码器提取与分类相关的特征.
- 在标记的数据上训练了分类器,以使用提取的特征识别特定事件.
主要成果:
- 拟议的半监督方法实现了与基线模型可比的识别性能.
- 与传统方法相比,这种方法显著减少了对标记数据的要求.
- 混合架构在事件分类中表现出更高的准确性和效率.
- 在现实数据集上的验证证实了该方法的实际适用性.
结论:
- 开发的半监督学习方法为基于DAS的基础设施安全提供了具有成本效益的解决方案.
- 该方法提高了实时周边监控系统的性能和效率.
- 这项研究为降低部署成本和增加新安全系统部署吞吐量提供了实际见解.
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