深度神经网络的应用,用于在聚合物声学发送器/传感器中推断压力
Seyedhamidreza Alaie1, Subhi J Al'Aref2
1Department of Mechanical & Aerospace Engineering, New Mexico State University, Las Cruces, NM, USA.
概括
深度学习模型可以准确地推断声响应器信号的压力. 这种方法为低成本的被动远程监控应用提供了传统方法的有希望的替代方案.
科学领域:
- 声学和材料科学 声学和材料科学
- 人工智能和信号处理
背景情况:
- 被动传感器-发送器提供低成本的远程监控,没有储能.
- 完全声学转发器在电磁设计上提供了优势,包括简单性和降低成本.
- 使用共振频率转移推断声学共振器信号的压力是具有挑战性的.
研究的目的:
- 探索深度学习的应用,从聚合物共振器的超声波反射推断压力.
- 评估神经网络回归器在从完全声学转发器推断压力的效率.
- 为此任务比较不同深度学习架构的性能.
主要方法:
- 利用深度神经网络,包括卷积神经网络 (CNN),启发ResNet的网络和完全连接的神经网络.
- 从聚合物声学共振器反射散射的超声波信号上训练和评估回归器.
- 对压力回归的各种深度学习模型的预测准确度进行了比较.
主要成果:
- 深度神经网络在推断压力信息方面表现出显著的优势.
- 这些模型需要最小的手动信号分析才能有效估计压力.
- 所有测试的深度学习架构都显示出准确压力回归的潜力.
结论:
- 深度学习方法在推断声响应器信号的压力方面非常有效.
- 这些方法有可能取代或增强压力传感的传统技术.
- 这些发现支持将深度学习集成到被动声学传感器系统中,以提高性能.
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