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Gui Yun Tian
Researcher at Newcastle University
Publications - 508
Citations - 14615
Gui Yun Tian is an academic researcher from Newcastle University. The author has contributed to research in topics: Nondestructive testing & Eddy current. The author has an hindex of 56, co-authored 489 publications receiving 11308 citations. Previous affiliations of Gui Yun Tian include University of East Anglia & University of Derby.
Papers
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Sparse ensemble matrix factorization for debond detection in CFRP composites using optical thermography
TL;DR: The sparse ensemble matrix factorization approach to remove the noise and enhance the resolution for the defects detection is proposed, based on the sparse representation and noise is modeled as a mixture of Gaussian (MoG) distribution.
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Ensemble tensor decomposition for infrared thermography cracks detection system
TL;DR: In this article, an ensemble tensor decomposition was proposed to extract weak target signal of infrared thermography videos for cracks detection, which jointly models the background and foreground tensor patterns as well as removing the ghosting.
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Diffusion and separation mechanism of transient electromagnetic and thermal fields
TL;DR: In this paper, a physical-mathematical time-dependent partition model is proposed to analyze the thermal transient process and consider characteristic times for separating Joule heating and thermal diffusion into four different stages.
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3D transient magnetic field mapping for angular slots in aluminium
I Mukriz,Gui Yun Tian,Yong Li +2 more
TL;DR: In this paper, a 3D visualisation of transient magnetic field mapping via finite element simulation for the characterisation of complex geometry defects is presented, which can help in sensor (array) design and inverse models for complex geometry characterisation.
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A Model with Leaf Area Index and Trunk Diameter for LoRaWAN Radio Propagation in Eastern China Mixed Forest
TL;DR: Results show that 433 MHz LoRa path loss in the mingled forest could be precisely predicted by the proposed model, which enables the perpetual development of reliable forestry evolution monitoring system.