Zheng Liu

Professor

University of British Columbia Okanagan

Canada

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Professor Zheng Liu is a faculty member at the University of British Columbia Okanagan, Canada. His research focuses on machine learning applications in industrial big data, computer vision, and sensor technology, as evidenced by recent publications including works on grain mycotoxin detection, image matching systems, and pipeline monitoring. Additionally, his contributions in areas such as airway segmentation and causal modeling highlight his diverse expertise in engineering and data analysis.

Zheng Liu

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Articles (30)

GIMS: Image matching system based on adaptive graph construction and graph neural network

Visualization of Industrial Big Data: State-of-the-Art and Future Perspectives

Dual-phase airway segmentation: Enhancing distal bronchial identification with anatomical prior guidance

Harnessing machine learning for grain mycotoxin detection

TrajReducer: a cross-dimension indexer-based reducer for vessel destination prediction

Causality-Augmented generalization network with cross-domain meta-learning for interlayer slipping recognition in viscoelastic sandwich structures

Translation of MFL and UT data by using generative adversarial networks: A comparative study

Adaptive Ordinal Sample-Weighted Meta-ResNet for Fault Severity Classification Under Class Imbalance

Fiber-Optic Sensing Technologies for Underground Pipeline Monitoring

LSDC-RC-RAPID: An Improved Probabilistic Reconstruction Approach for Pipeline Corrosion Detection With UGWT

Progressive generative adversarial network for generating high-dimensional and wide-frequency signals in intelligent fault diagnosis

Personalized federated learning for buildings energy consumption forecasting

FTnet: An integrated network for fusing multi-modal NDE data of lightning damage in aircraft composite materials

Vision Fourier transformer empowered multi-modal imaging system for ethane leakage detection

Capturing and modeling reality to enhance the built environment

A Visual and Textual Information Fusion-Based Zero-Shot Framework for Hazardous Material Placard Detection and Recognition

Multivariate multiscale dispersion Lempel–Ziv complexity for fault diagnosis of machinery with multiple channels

Pipeline condition monitoring towards digital twin system: A case study

TrajBERT - DSSM: Deep bidirectional transformers for vessel trajectory understanding and destination prediction

A deep learning‐based framework (Co‐ReTr) for auto‐segmentation of non‐small cell‐lung cancer in computed tomography images

Domain Discrepancy-Guided Contrastive Feature Learning for Few-Shot Industrial Fault Diagnosis Under Variable Working Conditions

Prior knowledge-embedded meta-transfer learning for few-shot fault diagnosis under variable operating conditions

An adaptive federated learning system for community building energy load forecasting and anomaly prediction

Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network

Foreground Fusion-Based Liquefied Natural Gas Leak Detection Framework From Surveillance Thermal Imaging

Ontology-Based Driving Simulation for Traffic Lights Optimization

Universal source-free domain adaptation method for cross-domain fault diagnosis of machines

Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network

Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions

Learning multi-view and centerline topology connectivity information for pulmonary artery–vein separation

Collaborators (3)

LIXIN TU

Shan Du

Adam Glowacz

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