B
Bin Gao
Researcher at University of Electronic Science and Technology of China
Publications - 175
Citations - 4079
Bin Gao is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Nondestructive testing & Thermography. The author has an hindex of 30, co-authored 171 publications receiving 2949 citations. Previous affiliations of Bin Gao include Newcastle University & University of Newcastle.
Papers
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Structural Health Monitoring Framework Based on Internet of Things: A Survey
TL;DR: A framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring is introduced and technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented.
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Automatic Defect Identification of Eddy Current Pulsed Thermography Using Single Channel Blind Source Separation
TL;DR: A single-channel blind source separation is proposed to process the ECPT image sequences to automatically extract valuable spatial and time patterns according to the whole transient response behavior without any training knowledge.
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Single-Channel Source Separation Using EMD-Subband Variable Regularized Sparse Features
Bin Gao,Wai Lok Woo,Satnam Dlay +2 more
TL;DR: It is shown, in this paper, that the IMFs have several desirable properties unique to SCSS problem and how these properties can be advantaged to relax the constraints posed by the problem.
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Impact Damage Detection and Identification Using Eddy Current Pulsed Thermography Through Integration of PCA and ICA
TL;DR: An integration of principal components analysis (PCA) and independent component analysis (ICA) on transient thermal videos has been proposed, which enables spatial and temporal patterns to be extracted according to the transient response behavior without any training knowledge.
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Temporal and spatial deep learning network for infrared thermal defect detection
TL;DR: Results show that visual geometry group-Unet (VGG- unet) cross learning structure can significantly improve the contrast between the defective and non-defective regions.