Search

Your search keyword '"He, Qingbo"' showing total 521 results
Start Over Author "He, Qingbo"
521 results on '"He, Qingbo"'

5. Scattering-coded elastic meta-boundary

Author

Jiang, Tianxi, Liao, Xinxin, Huang, Hao, Peng, Zhi-Ke, and He, Qingbo

Subjects
Physics - Applied Physics
Abstract

Object localization through active elastic waves is a crucial technology, but generally requires a transducer array with complex hardware. Although computational sensing has been demonstrated to be able to overcome the short-comings of transducer array by merging artificially designed structures into sensing process, coding spatial elastic waves for active object identification is still a knowledge gap. Here we propose a scattering-coded elastic meta-boundary composed of randomly distributed scatterers for computational identification of objects with a single transducer. The multiple scattering effect of the meta-boundary introduces complexity into scattered fields to achieve a highly uncorrelated scattering coding of elastic waves, thereby eliminating the ambiguity of the object location information. We demonstrate that the locations of objects can be uniquely identified by using the scattering coding of our designed meta-boundary, delivering a design of meta-boundary touchscreen for human-machine interaction. The proposed scattering-coded meta-boundary opens up avenues for artificially designed boundaries with the capability of information coding and identification, and may provide important applications in wave sensing, such as structural monitoring, underwater detection, indoor localization, and biomedical imaging., Comment: 10 pages, 5 figures

Published
2021

8. A Novel Cylindrical Mechanical Metastructure for Drone Vibration Isolation

Author

Lin, Yubin, Huang, Shiqing, Gu, Lichang, Deng, Rongfeng, Okhionkpamwonyi, Solomon, He, Qingbo, Gu, Fengshou, Ball, Andrew D., Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Zhang, Hao, editor, Feng, Guojin, editor, Wang, Hongjun, editor, Gu, Fengshou, editor, and Sinha, Jyoti K., editor

Published
2023
Full Text
View/download PDF

27. Gearbox Condition Monitoring Using Sparse Filtering and Parameterized Time–Frequency Analysis

Author

Wang, Shiyang, Liu, Zhen, He, Qingbo, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Gelman, Len, editor, Martin, Nadine, editor, Malcolm, Andrew A., editor, and (Edmund) Liew, Chin Kian, editor

Published
2021
Full Text
View/download PDF

34. DMWMN: A Deep Modulation Network for Gearbox Intelligent Fault Detection Under Variable Working Conditions

Author

Guo, Junchao, He, Qingbo, Gu, Fengshou, and Ball, Andrew D.

Abstract

Convolutional neural network (CNN) has shown great potential in real-time gearbox monitoring. In practical engineering, due to the complex multitooth meshing motions and variable working conditions resulting in gearboxes with multiple excitation sources, and the response signals exhibit amplitude modulation and frequency modulation characteristics, which makes it difficult for CNN to obtain the fault features from complex modulated signals. To tackle these challenges, this article presents a new deep multiscale weighted modulation network (DMWMN) for gearbox fault detection under variable working conditions. First, a new frequency-domain modulation spectrum is proposed as signal processing layer in DMWMN to demodulate the modulation features from complex vibration signals. Thereafter, multibranched structure with different DMWMN slice scales is utilized to obtain fault features. The frequency domain signal-to-noise ratio-based weighted fusion method is employed to optimize the weighted coefficients in DMWMN to enhance the fault feature components. Finally, a CNN is further employed to learn features from the demodulated signals in the DMWMN layer to identify for fault classification. Experimental results prove that the DMWMN has advantages over state-of-the-art algorithms for gearbox fault feature identification under variable working conditions.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results