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Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function Journal article
Mei, Ling Feng, Yan, Wang Ji, Yuen, Ka Veng, Beer, Michael. Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function[J]. Mechanical Systems and Signal Processing, 2024, 222.
Authors:  Mei, Ling Feng;  Yan, Wang Ji;  Yuen, Ka Veng;  Beer, Michael
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:7.9/8.0 | Submit date：2024/08/30
Bayesian Nonparametric Model  Damage Detection  Streaming Variational Inference  Truncation-free Full Dirichlet Process Gaussian Mixture Model  
Element-wise parallel deep learning for structural distributed damage diagnosis by leveraging physical properties of long-gauge static strain transmissibility under moving loads Journal article
Liu, Yu Song, Yan, Wang Ji, Yuen, Ka Veng, Zhou, Wan Huan. Element-wise parallel deep learning for structural distributed damage diagnosis by leveraging physical properties of long-gauge static strain transmissibility under moving loads[J]. Mechanical Systems and Signal Processing, 2024, 220, 111680.
Authors:  Liu, Yu Song;  Yan, Wang Ji;  Yuen, Ka Veng;  Zhou, Wan Huan
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.9/8.0 | Submit date：2024/08/05
Damage Diagnosis  Long-gauge Strain Sensing  Moving Load  Physical Property-guided Method  Transmissibility Function  
Multi-output multi-physics-informed neural network for learning dimension-reduced probability density evolution equation with unknown spatio-temporal-dependent coefficients Journal article
Hao, Teng Teng, Yan, Wang Ji, Chen, Jian Bing, Sun, Ting Ting, Yuen, Ka Veng. Multi-output multi-physics-informed neural network for learning dimension-reduced probability density evolution equation with unknown spatio-temporal-dependent coefficients[J]. Mechanical Systems and Signal Processing, 2024, 220, 111683.
Authors:  Hao, Teng Teng;  Yan, Wang Ji;  Chen, Jian Bing;  Sun, Ting Ting;  Yuen, Ka Veng
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.9/8.0 | Submit date：2024/08/05
Dimension-reduced Probability Density Evolution Equation  Multi-output Network  Physics Informed Neural Network  Spatio-temporal-dependent Equations  Stochastic Dynamics  
StocIPNet: A novel probabilistic interpretable network with affine-embedded reparameterization layer for high-dimensional stochastic inverse problems Journal article
Mo, Jiang, Yan, Wang Ji. StocIPNet: A novel probabilistic interpretable network with affine-embedded reparameterization layer for high-dimensional stochastic inverse problems[J]. Mechanical Systems and Signal Processing, 2024, 220, 111623.
Authors:  Mo, Jiang;  Yan, Wang Ji
Favorite | TC[WOS]:3 TC[Scopus]:3  IF:7.9/8.0 | Submit date：2024/08/05
Deep Learning  Model Updating  Reparameterization Trick  Stochastic Inverse Problems  Structural Health Monitoring  
Online operational modal decomposition Journal article
Huang, Ke, Yuen, Ka Veng, Ma, Yafei, Wang, Lei. Online operational modal decomposition[J]. Mechanical Systems and Signal Processing, 2024, 214, 111376.
Authors:  Huang, Ke;  Yuen, Ka Veng;  Ma, Yafei;  Wang, Lei
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.9/8.0 | Submit date：2024/05/16
Extended Kalman Filter  Modal Participation Factors  Mode Shape Components  Nonstationary Measurements  Online Updating  Operational Modal Decomposition  
Adaptive event-triggered dynamic output feedback control for nonlinear active suspension systems based on interval type-2 fuzzy method Journal article
Wong, Pak Kin, Li, Wenfeng, Ma, Xinbo, Yang, Zhixin, Wang, Xianbo, Zhao, Jing. Adaptive event-triggered dynamic output feedback control for nonlinear active suspension systems based on interval type-2 fuzzy method[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2024, 212, 111280.
Authors:  Wong, Pak Kin;  Li, Wenfeng;  Ma, Xinbo;  Yang, Zhixin;  Wang, Xianbo; et al.
Favorite | TC[WOS]:7 TC[Scopus]:7  IF:7.9/8.0 | Submit date：2024/05/02
Feedback Control  Nonlinear Dynamics  Suspension Systems  Vibration Control  
A novel closed-loop current control unit for decoupling vibration control of semi-active electrically interconnected suspension Journal article
Liao, Yulin, Liu, Pengfei, Zhao, Jing, Wong, Pak Kin, Ning, Donghong, Du, Haiping. A novel closed-loop current control unit for decoupling vibration control of semi-active electrically interconnected suspension[J]. Mechanical Systems and Signal Processing, 2024, 212, 111308.
Authors:  Liao, Yulin;  Liu, Pengfei;  Zhao, Jing;  Wong, Pak Kin;  Ning, Donghong; et al.
Favorite | TC[WOS]:0 TC[Scopus]:1  IF:7.9/8.0 | Submit date：2024/05/02
Decoupling Control  Electrical Network  Electrically Interconnected Suspension  Semi-active Control  
A non-iterative partitioned computational method with the energy conservation property for time-variant dynamic systems Journal article
Yuan, Peng, Yuen, Ka Veng, Beer, Michael, Cai, C. S., Yan, Wangji. A non-iterative partitioned computational method with the energy conservation property for time-variant dynamic systems[J]. Mechanical Systems and Signal Processing, 2024, 209, 111105.
Authors:  Yuan, Peng;  Yuen, Ka Veng;  Beer, Michael;  Cai, C. S.;  Yan, Wangji
Favorite | TC[WOS]:2 TC[Scopus]:2  IF:7.9/8.0 | Submit date：2024/04/02
Energy Conservation  Partitioned Computation  Stability And Accuracy  Time-variant Systems  Vehicle-bridge Interaction  
A hierarchical Bayesian modeling framework for identification of Non-Gaussian processes Journal article
Ping, Menghao, Jia, Xinyu, Papadimitriou, Costas, Han, Xu, Jiang, Chao, Yan, Wang Ji. A hierarchical Bayesian modeling framework for identification of Non-Gaussian processes[J]. Mechanical Systems and Signal Processing, 2024, 208, 110968.
Authors:  Ping, Menghao;  Jia, Xinyu;  Papadimitriou, Costas;  Han, Xu;  Jiang, Chao; et al.
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:7.9/8.0 | Submit date：2024/02/22
Hierarchical Bayesian Modeling Framework  Improved Orthogonal Series Expansion  Model Class Selection  Non-gaussian Pdf Estimation  Non-gaussian Process  Polynomial Chaos Expansion  
From model-driven to data-driven: A review of hysteresis modeling in structural and mechanical systems Journal article
Wang, Tianyu, Noori, Mohammad, Altabey, Wael A., Wu, Zhishen, Ghiasi, Ramin, Kuok, Sin Chi, Silik, Ahmed, Farhan, Nabeel S.D., Sarhosis, Vasilis, Farsangi, Ehsan Noroozinejad. From model-driven to data-driven: A review of hysteresis modeling in structural and mechanical systems[J]. Mechanical Systems and Signal Processing, 2023, 204, 110785.
Authors:  Wang, Tianyu;  Noori, Mohammad;  Altabey, Wael A.;  Wu, Zhishen;  Ghiasi, Ramin; et al.
Favorite | TC[WOS]:22 TC[Scopus]:23  IF:7.9/8.0 | Submit date：2024/01/02
Data-driven Method  Hysteresis Modeling  Model-data Hybrid Driven Method  Model-driven Method  Structural And Mechanical System