Application of a sparse representation method using K-SVD to data compression of experimental ambient vibration data for SHM

2011 ◽  
Author(s):  
Hae Young Noh ◽  
Anne S. Kiremidjian
Keyword(s):  
Data Compression ◽  
Sparse Representation ◽  
Ambient Vibration ◽  
Vibration Data ◽  
Representation Method

Enhanced covariances matrix sparse representation method for DOA estimation

2015 ◽  
Vol 51 (16) ◽  
pp. 1288-1290 ◽  
Author(s):  
Wei Cui ◽  
Tong Qian ◽  
Jing Tian
Keyword(s):  
Sparse Representation ◽  
Doa Estimation ◽  
Representation Method

A novel sparse representation method based on virtual samples for face recognition

2012 ◽  
Vol 24 (3-4) ◽  
pp. 513-519 ◽  
Author(s):  
Deyan Tang ◽  
Ningbo Zhu ◽  
Fu Yu ◽  
Wei Chen ◽  
Ting Tang
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Virtual Samples ◽  
Representation Method

Integrating Sparse and Collaborative Representation Classifications for Image Classification

2017 ◽  
Vol 17 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Chunwei Tian ◽  
Guanglu Sun ◽  
Qi Zhang ◽  
Weibing Wang ◽  
Teng Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Linear Combination ◽  
Image Recognition ◽  
Test Sample ◽  
Collaborative Representation ◽  
Training Samples ◽  
Sparse Representation Classification ◽  
The Difference ◽  
Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

scholarly journals Dynamic Study of a Rooftop Vertical Axis Wind Turbine Tower Based on an Automated Vibration Data Processing Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3135 ◽  
Author(s):  
Ying Wang ◽  
Wensheng Lu ◽  
Kaoshan Dai ◽  
Miaomiao Yuan ◽  
Shen-En Chen
Keyword(s):  
Wind Turbine ◽  
Vertical Axis ◽  
Dynamic Responses ◽  
Ambient Vibration ◽  
Structural Vibration ◽  
Vertical Axis Wind Turbine ◽  
Mode Identification ◽  
Stochastic Subspace Identification ◽  
Vibration Data ◽  
Vibration Responses

When constructed on tall building rooftops, the vertical axis wind turbine (VAWT) has the potential of power generation in highly urbanized areas. In this paper, the ambient dynamic responses of a rooftop VAWT were investigated. The dynamic analysis was based on ambient measurements of the structural vibration of the VAWT (including the supporting structure), which resides on the top of a 24-story building. To help process the ambient vibration data, an automated algorithm based on stochastic subspace identification (SSI) with a fast clustering procedure was developed. The algorithm was applied to the vibration data for mode identification, and the results indicate interesting modal responses that may be affected by the building vibration, which have significant implications for the condition monitoring strategy for the VAWT. The environmental effects on the ambient vibration data were also investigated. It was found that the blade rotation speed contributes the most to the vibration responses.

scholarly journals Damage detection based on damping analysis of ambient vibration data

2008 ◽  
pp. n/a-n/a ◽  
Author(s):  
Michele Frizzarin ◽  
Maria Q. Feng ◽  
Paolo Franchetti ◽  
Serdar Soyoz ◽  
Claudio Modena
Keyword(s):  
Damage Detection ◽  
Ambient Vibration ◽  
Vibration Data
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