南京大学学报（自然科学版）2024，Vol.60Issue(3)：523-530,8.DOI:10.13232/j.cnki.jnju.2024.03.015
基于数据驱动的声源表面振速稀疏恢复方法
A data-driven sparse recovery method for surface velocity of the sound source
摘要
Abstract
The accurate description of surface velocity of the sound source is of great significance.The accuracy of the description of velocity mainly depends on the number of sampling points,and the increasing number of sampling points leads to the high cost of measurement.To overcome the aforementioned issue,a data-driven sparse recovery method for surface velocity of the sound source is proposed in this study.In the method,the velocity data sample is generated by numerical simulations by taking advantage of equivalent source method.Then the sparse basis of surface velocity of the sound source is constructed based on K-SVD dictionary learning method and sparse regularization is applied to realize an accurate reconstruction of the surface velocity with limited number of sampling points.To validate the effectiveness of the proposed method,the simulation of simply supported plate is conducted and the experiment is carried out in the anechoic chamber.The results of the simulation and the experiment indicate that compared with the conventional equivalent source method,the proposed method provides a more accurate reconstruction of the surface velocity.Meanwhile,the performance of the proposed method is more stable,which can provide a new scheme for the measurement of the surface velocity of the sound source.关键词
声源表面振速/振速恢复/数据驱动/字典训练/等效源法Key words
surface velocity of the sound source/velocity recovery/data-driven/dictionary learning/equivalent source method分类
信息技术与安全科学引用本文复制引用
刘袁,刘文强,赵瑾瑜,胡定玉,李永畅..基于数据驱动的声源表面振速稀疏恢复方法[J].南京大学学报（自然科学版）,2024,60(3):523-530,8.基金项目
国家自然科学基金(12004007,12274282),安徽省自然科学基金(1908085QA39),安徽省教育厅高校自然科学研究项目(2023AH050196) （12004007,12274282）
