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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: 4702

Citations:  Bibtex RefMan EndNote GB/T7714


Jiao BAO


Jiuwen CAO


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


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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author="Jiao BAO, Lifu LIU, Jiuwen CAO",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T Vibration-based hypervelocity impact identification and localization
%A Jiao BAO
%A Lifu LIU
%A Jiuwen CAO
%J Frontiers of Information Technology & Electronic Engineering
%V 23
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%P 515-529
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%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000483

T1 - Vibration-based hypervelocity impact identification and localization
A1 - Jiao BAO
A1 - Lifu LIU
A1 - Jiuwen CAO
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 4
SP - 515
EP - 529
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000483

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.




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


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