Full Text:   <2085>

CLC number: TP391.4

On-line Access: 2022-04-20

Received: 2020-09-16

Revision Accepted: 2022-05-04

Crosschecked: 2021-04-09

Cited: 0

Clicked: 4782

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jiao BAO

https://orcid.org/0000-0002-0692-332X

Jiuwen CAO

https://orcid.org/0000-0002-6088-3912

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.4 P.515-529

http://doi.org/10.1631/FITEE.2000483


Vibration-based hypervelocity impact identification and localization


Author(s):  Jiao BAO, Lifu LIU, Jiuwen CAO

Affiliation(s):  Department of Computer Engineering, Chengdu Technological University, Chengdu 611730, China; more

Corresponding email(s):   jwcao@hdu.edu.cn

Key Words:  Ensemble learning, Synchrosqueezied transform, Gray-level co-occurrence matrix, Image entropy, Distance estimation


Jiao BAO, Lifu LIU, Jiuwen CAO. Vibration-based hypervelocity impact identification and localization[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 515-529.

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Abstract: 
Hypervelocity impact (HVI) vibration source identification and localization have found wide applications in many fields, such as manned spacecraft protection and machine tool collision damage detection and localization. In this paper, we study the synchrosqueezed transform (SST) algorithm and the texture color distribution (TCD) based HVI source identification and localization using impact images. The extracted SST and TCD image features are fused for HVI image representation. To achieve more accurate detection and localization, the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features. Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization. To demonstrate the effectiveness of the proposed algorithm, the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation. The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms. Finally, based on sensor distribution, an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.

基于振动的超高速碰撞识别与定位

包姣1,刘力夫2,曹九稳2,3
1成都工业学院计算机工程学院,中国成都市,611730
2杭州电子科技大学浙江省机器学习与健康国际合作基地,中国杭州市,310018
3杭州电子科技大学人工智能研究院,中国杭州市,310018
摘要:超高速碰撞(HVI)振动源识别与定位在载人航天器防护、机床碰撞损伤检测与定位等领域有着广泛应用。本文研究了基于同步压缩变换(SST)和纹理颜色分布(TCD)的冲击图像HVI源识别和定位算法。提出基于SST和TCD图像特征融合的HVI图像表示方法。为实现更精确的检测和定位,通过关联和评估样本标签与特征维度之间的相似性,获得最优选择性特征OSSST+TCD。将常用的分类和回归模型通过投票和堆叠融合,实现最终的检测和定位。基于所采集的3种高速子弹撞击铝合金板产生的HVI数据,验证了所提算法的有效性。实验结果表明本文提出的HVI识别与定位算法具有更高精准度。最后基于传感器分布,提出一种精确的四圆质心定位算法用于HVI源坐标定位。

关键词:集成学习;同步压缩变换;灰度共生矩阵;图像熵;距离估计

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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