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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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Journal ArticleDOI
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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A feature extraction technique based on principal component analysis for pulsed eddy current ndt
TL;DR: In this paper, the authors introduced the application of principal component analysis (PCA) in extracting information from PEC responses, which has performed better than the conventional technique in the classification of defects.
Journal ArticleDOI
A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications
TL;DR: The challenges and state-of-the-art methods of passive RFID antenna sensors and systems in terms of sensing and communication from system point of view are highlighted and future trends are discussed.
Journal ArticleDOI
Illuminant and device invariant colour using histogram equalisation
TL;DR: This paper proposes a new colour invariant image representation based on an existing grey-scale image enhancement technique: histogram equalisation and applies the method to an image indexing application and shows that the method out performs all previous invariant representations.
Proceedings ArticleDOI
Deep Learning Models for Cyber Security in IoT Networks
TL;DR: This paper proposes deep learning models for the cyber security in IoT (Internet of Things) networks and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms.