Application-driven sensing data reconstruction and selection based on correlation mining and dynamic feedback

2016 ◽  
Author(s):  
Zhichuan Huang ◽  
Tiantian Xie ◽  
Ting Zhu ◽  
Jianwu Wang ◽  
Qingquan Zhang
Keyword(s):  
Data Reconstruction ◽  
Dynamic Feedback ◽  
Sensing Data ◽  
Correlation Mining

scholarly journals Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach

2019 ◽  
Vol 19 (1) ◽  
pp. 293-304 ◽  
Author(s):  
Yuequan Bao ◽  
Zhiyi Tang ◽  
Hui Li
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Compressive Sensing ◽  
Health Monitoring ◽  
Applied Mathematics ◽  
Modal Identification ◽  
Data Reconstruction ◽  
Wireless Data ◽  
Sensing Data ◽  
Structural Health

Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage identification. The key issue in compressive sensing is finding the optimal solution for sparse optimization. In the past several years, many algorithms have been proposed in the field of applied mathematics. In this article, we propose a machine learning–based approach to solve the compressive-sensing data-reconstruction problem. By treating a computation process as a data flow, the solving process of compressive sensing–based data reconstruction is formalized into a standard supervised-learning task. The prior knowledge, i.e. the basis matrix and the compressive sensing–sampled signals, is used as the input and the target of the network; the basis coefficient matrix is embedded as the parameters of a certain layer; and the objective function of conventional compressive sensing is set as the loss function of the network. Regularized by l1-norm, these basis coefficients are optimized to reduce the error between the original compressive sensing–sampled signals and the masked reconstructed signals with a common optimization algorithm. In addition, the proposed network is able to handle complex bases, such as a Fourier basis. Benefiting from the nature of a multi-neuron layer, multiple signal channels can be reconstructed simultaneously. Meanwhile, the disassembled use of a large-scale basis makes the method memory-efficient. A numerical example of multiple sinusoidal waves and an example of field-test wireless data from a suspension bridge are carried out to illustrate the data-reconstruction ability of the proposed approach. The results show that high reconstruction accuracy can be obtained by the machine learning–based approach. In addition, the parameters of the network have clear meanings; the inference of the mapping between input and output is fully transparent, making the compressive-sensing data-reconstruction neural network interpretable.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

A procedure for integrated assessment of landscape fire risk using remote sensing data

2011 ◽  
Vol 17 (6) ◽  
pp. 30-44
Author(s):  
Yu.V. Kostyuchenko ◽  
◽  
M.V. Yushchenko ◽  
I.M. Kopachevskyi ◽  
S. Levynsky ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Integrated Assessment ◽  
Remote Sensing Data ◽  
Fire Risk ◽  
Sensing Data
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