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Biography and Research Information
OverviewAI-generated summary
Zijun Zhang's research activities encompass the application of deep learning and data-driven models to address challenges in energy systems, materials science, and infrastructure analytics. His work includes developing frameworks for the early prediction of battery lifetime, utilizing machine learning to forecast remaining useful life with limited data, and employing deep convolutional recurrent networks for short-term wind power predictions. Zhang has also investigated methods for imputing missing data in wind farm SCADA systems using deep autoencoder-based approaches.
Further research by Zhang involves automated analytics for rail surface crack detection using deep data-driven models and transfer learning. He has also explored energy harvesting technologies, such as triboelectric nanogenerators for wave energy conversion. In the realm of materials science, his research includes studying the synergistic mechanisms of biochar-nano TiO2 for pollutant adsorption and photocatalytic oxidation, and investigating the use of hyaluronic acid and modified cationic liposomes for enhanced skin penetration and retention.
Zhang holds a distinguished record with an h-index of 56 and over 11,900 citations from more than 430 publications. He is recognized as a highly cited researcher and maintains an active laboratory website. His recent activity indicates continued engagement in research, with his most recent publication in 2025.
Metrics
- h-index: 56
- Publications: 436
- Citations: 11,934
Selected Publications
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Distance-Aware Risk Minimization for Domain Generalization in Machine Fault Diagnosis (2024)
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Single imbalanced domain generalization network for intelligent fault diagnosis of compressors in HVAC systems under unseen working conditions (2024)
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MNHP-GAE: A Novel Manipulator Intelligent Health State Diagnosis Method in Highly Imbalanced Scenarios (2024)
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Unraveling the nexus: Microplastics, antibiotics, and ARGs interactions, threats and control in aquaculture – A review (2024)
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Development and trending of deep learning methods for wind power predictions (2024)
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Deep learning powered rapid lifetime classification of lithium-ion batteries (2023)
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Solar panel defect detection design based on YOLO v5 algorithm (2023)
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A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data (2023)
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Conditional Variational Autoencoder Informed Probabilistic Wind Power Curve Modeling (2023)
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Deep Learning Powered Online Battery Health Estimation Considering Multitimescale Aging Dynamics and Partial Charging Information (2023)
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Hyaluronic acid and HA-modified cationic liposomes for promoting skin penetration and retention (2023)
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EAF-WGAN: Enhanced Alignment Fusion-Wasserstein Generative Adversarial Network for Turbulent Image Restoration (2023)
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A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data (2023)
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Development and multi-center validation of machine learning model for early detection of fungal keratitis (2023)
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Blockchain technology adoption of the manufacturers with product recycling considering green consumers (2023)
Collaboration Network
Top Collaborators
Kwok‐Leung Tsui
Virginia Tech (US)
10 shared publications
- Early prediction of battery lifetime via a machine learning based framework
- A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data
- A Deep Generative Approach for Rail Foreign Object Detections via Semisupervised Learning
- Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features
- The Variational Kernel-Based 1-D Convolutional Neural Network for Machinery Fault Diagnosis
Showing 5 of 10 shared publications
Luoxiao Yang
City University of Hong Kong (HK)
9 shared publications
- Short-Term Multi-Step Ahead Wind Power Predictions Based On A Novel Deep Convolutional Recurrent Network Method
- A Deep Attention Convolutional Recurrent Network Assisted by K-Shape Clustering and Enhanced Memory for Short Term Wind Speed Predictions
- Generative Probabilistic Wind Speed Forecasting: A Variational Recurrent Autoencoder Based Method
- A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data
- The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions
Showing 5 of 9 shared publications
Zhong Zheng
Dongguan University of Technology (CN)
7 shared publications
- Automated rail surface crack analytics using deep data-driven models and transfer learning
- Denoising temporal convolutional recurrent autoencoders for time series classification
- Generative Probabilistic Wind Speed Forecasting: A Variational Recurrent Autoencoder Based Method
- A Stochastic Recurrent Encoder Decoder Network for Multistep Probabilistic Wind Power Predictions
- Conditional Variational Autoencoder Informed Probabilistic Wind Power Curve Modeling
Showing 5 of 7 shared publications
Zicheng Fei
City University of Hong Kong (HK)
5 shared publications
- Early prediction of battery lifetime via a machine learning based framework
- A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data
- Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features
- Deep Learning Powered Online Battery Health Estimation Considering Multitimescale Aging Dynamics and Partial Charging Information
- Deep learning powered rapid lifetime classification of lithium-ion batteries
Fangfang Yang
Sun Yat-sen University (CN)
5 shared publications
- Early prediction of battery lifetime via a machine learning based framework
- A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data
- Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features
- Deep learning powered rapid lifetime classification of lithium-ion batteries
- Automatic Rail Component Detection Based on AttnConv-Net
Long Wang
University of Science and Technology Beijing (CN)
5 shared publications
- Denoising temporal convolutional recurrent autoencoders for time series classification
- Generative Probabilistic Wind Speed Forecasting: A Variational Recurrent Autoencoder Based Method
- Generative Wind Power Curve Modeling via Machine Vision: A Deep Convolutional Network Method With Data-Synthesis-Informed-Training
- Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification With a Hybrid Symbolic Aggregate Approximation Algorithm
- A Continual Learning-Based Framework for Developing a Single Wind Turbine Cybertwin Adaptively Serving Multiple Modeling Tasks
Zhenling Mo
City University of Hong Kong (HK)
4 shared publications
- The Variational Kernel-Based 1-D Convolutional Neural Network for Machinery Fault Diagnosis
- Sparsity-Constrained Invariant Risk Minimization for Domain Generalization With Application to Machinery Fault Diagnosis Modeling
- MNHP-GAE: A Novel Manipulator Intelligent Health State Diagnosis Method in Highly Imbalanced Scenarios
- Distance-Aware Risk Minimization for Domain Generalization in Machine Fault Diagnosis
Xin Liu
City University of Hong Kong (HK)
3 shared publications
- Short-Term Multi-Step Ahead Wind Power Predictions Based On A Novel Deep Convolutional Recurrent Network Method
- A Two-Stage Deep Autoencoder-Based Missing Data Imputation Method for Wind Farm SCADA Data
- The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions
Haoyang Qi
City University of Hong Kong (HK)
3 shared publications
- Automated rail surface crack analytics using deep data-driven models and transfer learning
- The Automatic Rail Surface Multi-Flaw Identification Based on a Deep Learning Powered Framework
- A Deep-Learning-Powered Near-Real-Time Detection of Railway Track Major Components: A Two-Stage Computer-Vision-Based Method
Tiange Wang
City University of Hong Kong (HK)
3 shared publications
- A Deep Generative Approach for Rail Foreign Object Detections via Semisupervised Learning
- A Deep-Learning-Powered Near-Real-Time Detection of Railway Track Major Components: A Two-Stage Computer-Vision-Based Method
- Automatic Rail Component Detection Based on AttnConv-Net
Hong Liu
City University of Hong Kong, Shenzhen Research Institute (CN)
3 shared publications
- A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data
- Development and trending of deep learning methods for wind power predictions
- A Bi-Party Engaged Modeling Framework for Renewable Power Predictions With Privacy-Preserving
Lishuai Li
City University of Hong Kong (HK)
2 shared publications
- Early prediction of battery lifetime via a machine learning based framework
- Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features
Xiong Luo
University of Science and Technology Beijing (CN)
2 shared publications
- Denoising temporal convolutional recurrent autoencoders for time series classification
- Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification With a Hybrid Symbolic Aggregate Approximation Algorithm
Bingying Zhang
City University of Hong Kong (HK)
2 shared publications
- A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data
- A two-stage model for asynchronously scheduling offshore wind farm maintenance tasks and power productions
Pengfei Guo
Xi'an Jiaotong University (CN)
2 shared publications
- Stretchable and Compressible Si<sub>3</sub>N<sub>4</sub> Nanofiber Sponge with Aligned Microstructure for Highly Efficient Particulate Matter Filtration under High‐Velocity Airflow
- Adsorption properties of millimeter porous spheres constructed by montmorillonite nanosheets
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