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深度学习和动态时间扭曲用于共振超声谱中的自动模式识别

Wuyi Yang¹, Deyi Lin¹, Shanshan Sun¹

¹Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361010, China.

The Journal of the Acoustical Society of America

|March 10, 2026

概括

本研究介绍了一种使用深度神经网络 (DNN) 和动态时间扭曲 (DTW) 的自动化方法,用于识别压电材料中的共振模式. 与传统的手工方法相比,这种方法显著提高了效率和准确性.

科学领域:

材料科学材料科学材料科学
声学声学在声学方面
计算物理计算物理

背景情况:

共振超声谱 (RUS) 对于在具有高机械质量因子的材料中表征弹性和压电常数至关重要.
由于模式遗漏和重叠,在RUS中准确识别共振模式具有挑战性,需要费力地进行手动频率匹配.
在压电材料中用于共振模式识别的现有方法耗时且容易出现错误.

研究的目的:

使用RUS开发一种自动化和高效的方法,用于在压电材料中识别共振模式.
将深度神经网络 (DNN) 与动态时间扭曲 (DTW) 结合起来,以实现强大的模式识别.
通过手动识别和实验数据验证拟议方法的准确性和效率.

主要方法:

一个深度神经网络 (DNN) 被训练来将材料常数映射到压电矩形平行平行管的共振频率.
动态时间扭曲 (DTW) 用于将实验测量的共振频率序列与DNN生成的参考序列对齐.
该方法通过在各种温度下对富士C-213压电样本进行广泛的模拟和实验测试来验证.

主要成果:

拟议的DNN-DTW方法实现了准确的共振模式识别,与劳动密集型手工技术相比.
自动化方法比传统的手动匹配方法显著提高了效率.
通过全面的模拟和实验验证,方法的稳定性得到了证实.

结论:

DNN和DTW的集成为俄罗斯的自动共振模式识别提供了高效和准确的解决方案.
这种自动化方法克服了手动模式匹配的局限性,提高了压电材料的特性.
开发的方法为精确和快速分析压电材料特性提供了重大进步.

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