3 citations · 6 across the 5 of their papers we have counts for
5 papers
ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing
Hwa Hui Tew, Fan Ding, Gaoxuan Li +4
Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections…
KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes
Hwa Hui Tew, Gaoxuan Li, Fan Ding +5
Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improvin…
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
Jia Quan Loh, Xuewen Luo, Fan Ding +5
With the advancements of sensor hardware, traffic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligen…
Sigma-point Kalman Filter with Nonlinear Unknown Input Estimation via Optimization and Data-driven Approach for Dynamic Systems
Junn Yong Loo, Ze Yang Ding, Vishnu Monn Baskaran +2
Most works on joint state and unknown input (UI) estimation require the assumption that the UIs are linear; this is potentially restrictive as it does not hold in many intelligent…
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework
Shageenderan Sapai, Junn Yong Loo, Ze Yang Ding +4
Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are…